DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Applicant submitted amendments and remarks on March 6, 2026. Therein, Applicant submitted substantive arguments. Claims 1-2, 6, and 8-9 have been amended. Claims 10 was added. Claims 4-5 were cancelled.
Applicant has made adequate amendments to the Abstract of the Specification. Therefore, this
objection is withdrawn.
Applicant has made adequate amendments to claims 1 and 8-9 in order to eliminate claim language that could be interpreted under 35 U.S.C. 101. Therefore, these rejections are withdrawn.
The submitted claims are considered below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2 and 6-9 are rejected under 35 U.S.C. 103 as being unpatentable over Naiwala, et al. (U.S. Patent No. 10629088) in view of Sekiyama, et al. (U.S. Patent Application Publication No. 2012023274).
Regarding claim 1, Naiwala, et al. teaches: A driving skill evaluation method comprising: performing a setting process of setting one or more evaluation target curves from among two or more curves; (Fig. 5, Step (S503), Col. 5, lines 48-52: "The driving assistance device (1) selects, from the reference data storage unit (6), driving data that includes a driving skill higher than the driving skill of a subject, and has a driving type which is the most similar to the driving type of the subject (S503) [setting process of evaluation curves].")
and performing an evaluation process of evaluating a driving skill of a driver of a vehicle, based on traveling data of the vehicle at the one or more evaluation target curves, wherein the setting process includes: (Fig. 4, Steps (S401) - (S402), Col. 4, lines 43-48: "FIG. 4 is a flowchart showing an identification process by the driving skill classifier (5). The driving skill classifier (5) performs feature value extraction similar to the aforementioned extraction when the driving data is input (S401), and determines the driving skills of the input driving data by performing determination using learning result data (S402) [evaluation of specific driving skill level of vehicle as a function of extracted traveling data].")
curves (Col. 6, lines 18-21: "For example, difference in speed (brake amount) before a curve is seen, and as a result, variation in handle steering angles, or difference of cross-directional jerks appears [data linked to curve position].")
having a second skill level higher than the first skill level (Col. 4, lines 58-62: "The similarity data acquisition unit (7) acquires, from the reference data storage unit (6), driving data that satisfies the following two references. The first reference is that a driving skill is higher than the driving skill in the input driving data that is determined by the driving skill classifier (5) [second skill level higher than first skill level].")
calculating multiple first similarity levels for each of the two or more curves, each first similarity level being a similarity level between one piece of the first data and one piece of the second data; (Col. 4, lines 49-54: "The similarity data acquisition unit (7) calculates a similarity between each of the pieces of driving data that are stored in the reference data storage unit (6) and input of driving data. The driving skills of the pieces of driving data that are stored in the reference data storage unit (6) are known, and are stored in association with driving skills [multiple similarity levels for curves based on 1st data with drivers with 1st skill level]." ; Col. 4, lines 58-62: "The similarity data acquisition unit (7) acquires, from the reference data storage unit (6), driving data that satisfies the following two references. The first reference is that a driving skill is higher than the driving skill in the input driving data that is determined by the driving skill classifier (5) [second skill level higher than first skill level]." ; Step (S301), Col. 4, lines 29-31: "First, a plurality of pieces of driving data (learning data) of drivers whose driving skills are known are acquired (S301) [multiple drivers].")
calculating multiple second similarity levels for each of the two or more curves, each second similarity level being a similarity level between one piece of the second data; (Col. 4, line 62 to Col. 5, lines 1-4: "The second reference is that a similarity is the largest among pieces of driving data that satisfy the first reference [comparing among multiple pieces of data]. That is, the similarity data acquisition unit (7) acquires, from the reference data storage unit (6), driving data that includes a driving skill higher than the driving skill in the input driving data, and is the most similar to the input driving data [second similarity levels]. The calculation of the similarity can be implemented, by using an algorithm such as principal component analysis (PCA), k-nearest neighbor algorithm, k-means clustering [calculation process].")
and setting the one or more evaluation target curves from among the two or more curves, based on the multiple first similarity levels and the multiple second similarity levels, (Fig. 6B, Fig. 7, Col. 6, lines 12-24: "Driving data that is the most similar to the driving data on the subject is driving data (65), and therefore this data is selected as data for determining advice [based on 1st and 2nd similarity levels]. The driving assistance device (1) detects a difference between the driving data on the subject and the selected data, by using the difference detection unit (8). For example, difference in speed (brake amount) before a curve is seen, and as a result, variation in handle steering angles, or difference of cross-directional jerks appears. The driving assistance unit (9) prepares driving advice as shown in FIG. 7 on the basis of such a difference detection result [setting evaluation curves].").
Naiwala, et al. does not teach for each of the two or more generating multiple kernel density estimation images based on traveling data of multiple drivers having a first skill level obtained at a respective one of the two or more to obtain multiple pieces of first data, each piece of the first data being image data of a respective one of the multiple kernel density estimation images; for each of the two or more generating multiple kernel density estimation images based on traveling data of multiple drivers obtained at a respective one of the two or more curves to obtain multiple pieces of second data, each piece of the second data being image data of a respective one of the multiple kernel density estimation images; and the evaluation process includes generating a kernel density estimation image based on the traveling data, and evaluating the driving skill of the driver of the vehicle by comparing the kernel density estimation image with a reference image.
In a similar field of endeavor (driver evaluation), Sekiyama, et al. teaches: for each of the two or more generating multiple kernel density estimation images based on traveling data of multiple drivers having a first skill level obtained at a respective one of the two or more to obtain multiple pieces of first data, each piece of the first data being image data of a respective one of the multiple kernel density estimation images; (Paragraph [0077]: "The proficiency is an index which represents how skilled the driver is at eco-driving in a certain driving condition compared to a learning sample obtained from an individual driver or an unspecified number of drivers [multiple drivers having various skill levels]." ; Paragraph [0106]: "The eco-driving probability density estimation unit (231) estimates the probability density function p by Kernel density estimation [kernel density estimation image data].")
for each of the two or more, generating multiple kernel density estimation images based on traveling data of multiple drivers obtained at a respective one of the two or more to obtain multiple pieces of second data, each piece of the second data being image data of a respective one of the multiple kernel density estimation images; (Paragraph [0077]: "The proficiency is an index which represents how skilled the driver is at eco-driving in a certain driving condition compared to a learning sample obtained from an individual driver or an unspecified number of drivers [multiple drivers having various skill levels]." ; Paragraph [0106]: "The eco-driving probability density estimation unit (231) estimates the probability density function p by Kernel density estimation [kernel density estimation image data].")
and the evaluation process includes generating a kernel density estimation image based on the traveling data, (Paragraph [0013]: "The evaluation standard resetting unit may estimate, as the evaluation standard, a probability density function relating to the probability distribution of evaluation values of driving for each condition, in which the one vehicle is driven, by Kernel density estimation [creating kernel density estimation image].")
and evaluating the driving skill of the driver of the vehicle by comparing the kernel density estimation image with a reference image (Fig. 13, Step (S81) - Step (S83), Paragraph [0125]: "The eco-driving capability/proficiency estimation unit (161) compares the eco-driving probability density acquired in (S81) with information of the host vehicle calculated in (S82), and calculates the eco-driving capability (S83) [comparing estimation image with reference image].").
Therefore, it would have been obvious to one of the ordinary skill of the art before the effective filing date of the claimed invention to modify Naiwala, et al. to include the teaching of Sekiyama, et al. based on a reasonable expectation of success and motivation to improve the performance evaluation of a driver in a vehicle with respect a designated driving condition (Sekiyama, et al. Paragraphs [0001], [0007]).
Regarding claim 2, Naiwala, et al., and Sekiyama, et al. remain as applied to claim 1, and in a further embodiment, teach: The driving skill evaluation method according to claim 1, wherein the setting process further includes calculating, for each of the two or more curves, a variation value of the similarity levels between the multiple pieces of second data, (Naiwala, et al. Col. 6, lines 16-21: "The driving assistance device (1) detects a difference between the driving data on the subject and the selected data, by using the difference detection unit (8). For example, difference in speed (brake amount) before a curve is seen, and as a result, variation in handle steering angles, or difference of cross-directional jerks appears [variation calculation procedure].")
and setting the one or more evaluation target curves, from among the two or more curves based on one or more curves having the variation value less than a second value (Col. 10, lines 28-31: "…having a driving skill that is greater than the determined driving skill associated with the input driving data and has a distance measurement that is less than a predetermined distance [variation is less than a second value].").
Regarding claim 6, Naiwala, et al., and Sekiyama, et al. remain as applied to claim 1, and in a further embodiment, teach: The driving skill evaluation method according to claim 1, wherein the reference image comprises a kernel density estimation image generated based on a traveling characteristic of a vehicle driven by a driver having the second skill level (Sekiyama, et al. Paragraph [0077]: "The proficiency is an index which represents how skilled the driver is at eco-driving in a certain driving condition compared to a learning sample obtained from an individual driver or an unspecified number of drivers [drivers having various skill levels]." ; Sekiyama, et al. Paragraph [0142]: "According to this embodiment, the eco-driving probability density estimation unit (231) estimates, as the evaluation standard, the probability density function relating to the probability distribution of the evaluation values of the driving for each condition, in which the host vehicle is driven, by Kernel density estimation [comparison between host driver and other drivers].").
Regarding claim 7, Naiwala, et al., and Sekiyama, et al. remain as applied to claim 1, and in a further embodiment, teach: The driving skill evaluation method according to claim 1, further comprising presenting an evaluation result of the driving skill of the driver to the driver (Col. 5, lines 27-29: "The driving assistance unit (9) presents [presenting] differences that are detected by the difference detection unit (8) as driving advice to an output device (10) [evaluation result of driving skill to driver].").
Regarding claim 8, Naiwala, et al. teaches: A driving skill evaluation system comprising: (Col. 3, lines 49-53: "thereby causing the driving assistance device to function as a map data storage unit (3), a driving data acquisition unit (4), a driving skill classifier (5), a reference data storage unit (6), a similarity data acquisition unit (1), a difference detection unit (8), and a driving assistance unit (9) [overall driving skill evaluation system].")
a setting circuit configured to set one or more evaluation target curves from among two or more curves; (Col. 5, lines 48-52: "The driving assistance device (1) [setting circuit] selects, from the reference data storage unit (6), driving data that includes a driving skill higher than the driving skill of a subject, and has a driving type which is the most similar to the driving type of the subject (S503) [setting process of evaluation curves].")
and an evaluation circuit configured to evaluate a driving skill of a driver of a vehicle, based on traveling data of the vehicle at the one or more evaluation target curves, wherein the setting circuit is configured to: (Col. 4, lines 21-26: "The driving skill classifier (5) [evaluation circuit] is a function unit that receives driving data as input and determines a driving skill of the driving. Herein, the driving skill includes four levels of a beginner, an intermediate level driver, an advanced level driver, and a professional. However, classification may be different from this [evaluating driving skills based on data].")
curves (Col. 6, lines 18-21: "For example, difference in speed (brake amount) before a curve is seen, and as a result, variation in handle steering angles, or difference of cross-directional jerks appears [data linked to curve position].")
having a second skill level higher than the first skill level (Col. 4, lines 58-62: "The similarity data acquisition unit (7) acquires, from the reference data storage unit (6), driving data that satisfies the following two references. The first reference is that a driving skill is higher than the driving skill in the input driving data that is determined by the driving skill classifier (5) [second skill level higher than first skill level].")
calculate multiple first similarity levels for each of the two or more curves, each first similarity level being a similarity level between one piece of the first data, and one piece of the second data; (Col. 4, lines 49-54: "The similarity data acquisition unit (7) calculates a similarity between each of the pieces of driving data that are stored in the reference data storage unit (6) and input of driving data. The driving skills of the pieces of driving data that are stored in the reference data storage unit (6) are known, and are stored in association with driving skills [multiple similarity levels for curves based on 1st data with drivers with 1st skill level]." ; Col. 4, lines 58-62: "The similarity data acquisition unit (7) acquires, from the reference data storage unit (6), driving data that satisfies the following two references. The first reference is that a driving skill is higher than the driving skill in the input driving data that is determined by the driving skill classifier (5) [second skill level higher than first skill level]." ; Col. 4, lines 29-31: "First, a plurality of pieces of driving data (learning data) of drivers whose driving skills are known are acquired (S301) [multiple drivers].")
calculate multiple second similarity levels for each of the two or more curves, each second similarity level being a similarity level between one piece of the second data and another piece of the second data; (Col. 4, line 62 to Col. 5, lines 1-4: "The second reference is that a similarity is the largest among pieces of driving data that satisfy the first reference [comparing among multiple pieces of data]. That is, the similarity data acquisition unit (7) acquires, from the reference data storage unit (6), driving data that includes a driving skill higher than the driving skill in the input driving data, and is the most similar to the input driving data [second similarity levels]. The calculation of the similarity can be implemented, by using an algorithm such as principal component analysis (PCA), k-nearest neighbor algorithm, k-means clustering [calculation process].")
and set the one or more evaluation target curves from among the two or more curves, based on the multiple first similarity levels and the multiple second similarity levels, (Fig. 6B, Fig. 7, Col. 6, lines 12-24: "Driving data that is the most similar to the driving data on the subject is driving data (65), and therefore this data is selected as data for determining advice [based on 1st and 2nd similarity levels]. The driving assistance device (1) detects a difference between the driving data on the subject and the selected data, by using the difference detection unit (8). For example, difference in speed (brake amount) before a curve is seen, and as a result, variation in handle steering angles, or difference of cross-directional jerks appears. The driving assistance unit (9) prepares driving advice as shown in FIG. 7 on the basis of such a difference detection result [setting evaluation curves].").
Naiwala, et al. does not teach for each of the two or more generate multiple kernel density estimation images based on traveling data of multiple drivers having a first skill level obtained at a respective one of the two or more to obtain multiple pieces of first data, each piece of the first data being image data of a respective one of the multiple kernel density estimation images; for each of the two or more generate multiple kernel density estimation images based on traveling data of multiple drivers obtained at a respective one of the two or more to obtain multiple pieces of second data, each piece of the second data being image data of a respective one of the multiple kernel density estimation images; the evaluation circuit is configured to generate a kernel density estimation image based on the traveling data, and evaluating the driving skill of the driver of the vehicle by comparing the kernel density estimation image with a reference image.
In a similar field of endeavor (driver evaluation), Sekiyama, et al. teaches: for each of the two or more, generate multiple kernel density estimation images based on traveling data of multiple drivers having a first skill level obtained at a respective one of the two or more to obtain multiple pieces of first data, each piece of the first data being image data of a respective one of the multiple kernel density estimation images; (Paragraph [0077]: "The proficiency is an index which represents how skilled the driver is at eco-driving in a certain driving condition compared to a learning sample obtained from an individual driver or an unspecified number of drivers [multiple drivers having various skill levels]." ; Paragraph [0106]: "The eco-driving probability density estimation unit (231) estimates the probability density function p by Kernel density estimation [kernel density estimation image data].")
for each of the two or more, generate multiple kernel density estimation images based on traveling data of multiple drivers obtained at a respective one of the two or more to obtain multiple pieces of second data, each piece of the second data being image data of a respective one of the multiple kernel density estimation images; (Paragraph [0077]: "The proficiency is an index which represents how skilled the driver is at eco-driving in a certain driving condition compared to a learning sample obtained from an individual driver or an unspecified number of drivers [multiple drivers having various skill levels]." ; Paragraph [0106]: "The eco-driving probability density estimation unit (231) estimates the probability density function p by Kernel density estimation [kernel density estimation image data].")
the evaluation circuit is configured to generate a kernel density estimation image based on the traveling data, (Paragraph [0013]: "The evaluation standard resetting unit may estimate, as the evaluation standard, a probability density function relating to the probability distribution of evaluation values of driving for each condition, in which the one vehicle is driven, by Kernel density estimation [creating kernel density estimation image].")
and evaluating the driving skill of the driver of the vehicle by comparing the kernel density estimation image with a reference image (Paragraph [0125]: "The eco-driving capability/proficiency estimation unit (161) compares the eco-driving probability density acquired in (S81) with information of the host vehicle calculated in (S82), and calculates the eco-driving capability (S83) [comparing estimation image with reference image].").
Therefore, it would have been obvious to one of the ordinary skill of the art before the effective filing date of the claimed invention to modify Naiwala, et al. to include the teaching of Sekiyama, et al. based on a reasonable expectation of success and motivation to improve the performance evaluation of a driver in a vehicle with respect a designated driving condition (Sekiyama, et al. Paragraphs [0001], [0007]).
Regarding claim 9, A non-transitory recording medium containing software, the software causing a processor to: (Col. 3, lines 46-49: "In the driving assistance device according to this embodiment, a central processing unit (CPU) loads and executes a computer program that is stored in an auxiliary storage device [non-transitory recording medium containing software/processor]")
perform a setting process of setting one or more evaluation target curves from among two or more curves; (Col. 5, lines 48-52: "The driving assistance device (1) [setting circuit] selects, from the reference data storage unit (6), driving data that includes a driving skill higher than the driving skill of a subject, and has a driving type which is the most similar to the driving type of the subject (S503) [setting process of evaluation curves].")
perform an evaluation process of evaluating a driving skill of a driver of a vehicle, based on traveling data of the vehicle at the one or more evaluation target curves, wherein the setting process includes: (Col. 4, lines 43-48: "FIG. 4 is a flowchart showing an identification process by the driving skill classifier (5). The driving skill classifier (5) performs feature value extraction similar to the aforementioned extraction when the driving data is input (S401), and determines the driving skills of the input driving data by performing determination using learning result data (S402) [evaluation of specific driving skill level of vehicle as a function of extracted traveling data].")
curves (Col. 6, lines 18-21: "For example, difference in speed (brake amount) before a curve is seen, and as a result, variation in handle steering angles, or difference of cross-directional jerks appears [data linked to curve position].")
having a second skill level higher than the first skill level (Col. 4, lines 58-62: "The similarity data acquisition unit (7) acquires, from the reference data storage unit (6), driving data that satisfies the following two references. The first reference is that a driving skill is higher than the driving skill in the input driving data that is determined by the driving skill classifier (5) [second skill level higher than first skill level].")
calculating multiple first similarity levels for each of the two or more curves, each similarity level being a similarity level between one piece of the first data, and one piece of the second data; (Col. 4, lines 49-54: "The similarity data acquisition unit (7) calculates a similarity between each of the pieces of driving data that are stored in the reference data storage unit (6) and input of driving data. The driving skills of the pieces of driving data that are stored in the reference data storage unit (6) are known, and are stored in association with driving skills [multiple similarity levels for curves based on 1st data with drivers with 1st skill level]." ; Col. 4, lines 58-62: "The similarity data acquisition unit (7) acquires, from the reference data storage unit (6), driving data that satisfies the following two references. The first reference is that a driving skill is higher than the driving skill in the input driving data that is determined by the driving skill classifier (5) [second skill level higher than first skill level]." ; Col. 4, lines 29-31: "First, a plurality of pieces of driving data (learning data) of drivers whose driving skills are known are acquired (S301) [multiple drivers].")
calculating multiple second similarity levels for each of the two or more curves, each second similarity level being a similarity level between one piece of the second data and another piece of the second data; (Col. 4, line 62 to Col. 5, lines 1-4: "The second reference is that a similarity is the largest among pieces of driving data that satisfy the first reference [comparing among multiple pieces of data]. That is, the similarity data acquisition unit (7) acquires, from the reference data storage unit (6), driving data that includes a driving skill higher than the driving skill in the input driving data, and is the most similar to the input driving data [second similarity levels]. The calculation of the similarity can be implemented, by using an algorithm such as principal component analysis (PCA), k-nearest neighbor algorithm, k-means clustering [calculation process].")
and setting the one or more evaluation target curves from among the two or more curves, based on the multiple first similarity levels and the multiple second similarity levels (Fig. 6B, Fig. 7, Col. 6, lines 12-24: "Driving data that is the most similar to the driving data on the subject is driving data (65), and therefore this data is selected as data for determining advice [based on 1st and 2nd similarity levels]. The driving assistance device (1) detects a difference between the driving data on the subject and the selected data, by using the difference detection unit (8). For example, difference in speed (brake amount) before a curve is seen, and as a result, variation in handle steering angles, or difference of cross-directional jerks appears. The driving assistance unit (9) prepares driving advice as shown in FIG. 7 on the basis of such a difference detection result [setting evaluation curves].").
Naiwala, et al. does not teach for each of the two or more generating multiple kernel density estimation images based on traveling data of multiple drivers having a first skill level obtained at a respective one of the two or more to obtain multiple pieces of first data, each piece of the first data being image data of a respective one of the multiple kernel density estimation images; for each of the two or more generating multiple kernel density estimation images based on traveling data of multiple drivers obtained at a respective one of the two or more curves to obtain multiple pieces of second data, each piece of the second data being image data of a respective one of the multiple kernel density estimation images; the evaluation process includes generating a kernel density estimation image based on the traveling data, and evaluating the driving skill of the driver of the vehicle by comparing the kernel density estimation image with a reference image.
In a similar field of endeavor (driver evaluation), Sekiyama, et al. teaches: for each of the two or more, generating multiple kernel density estimation images based on traveling data of multiple drivers having a first skill level obtained at a respective one of the two or more to obtain multiple pieces of first data, each piece of the first data being image data of a respective one of the multiple kernel density estimation images; (Paragraph [0077]: "The proficiency is an index which represents how skilled the driver is at eco-driving in a certain driving condition compared to a learning sample obtained from an individual driver or an unspecified number of drivers [multiple drivers having various skill levels]." ; Sekiyama, et al. Paragraph [0106]: "The eco-driving probability density estimation unit (231) estimates the probability density function p by Kernel density estimation [kernel density estimation image data].")
for each of the two or more, generating multiple kernel density estimation images based on traveling data of multiple drivers obtained at a respective one of the two or more to obtain multiple pieces of second data, each piece of the second data being image data of a respective one of the multiple kernel density estimation images; (Paragraph [0077]: "The proficiency is an index which represents how skilled the driver is at eco-driving in a certain driving condition compared to a learning sample obtained from an individual driver or an unspecified number of drivers [multiple drivers having various skill levels]." ; Paragraph [0106]: "The eco-driving probability density estimation unit (231) estimates the probability density function p by Kernel density estimation [kernel density estimation image data].")
the evaluation process includes generating a kernel density estimation image based on the traveling data, (Paragraph [0013]: "The evaluation standard resetting unit may estimate, as the evaluation standard, a probability density function relating to the probability distribution of evaluation values of driving for each condition, in which the one vehicle is driven, by Kernel density estimation [creating kernel density estimation image].")
and evaluating the driving skill of the driver of the vehicle by comparing the kernel density estimation image with a reference image (Paragraph [0125]: "The eco-driving capability/proficiency estimation unit (161) compares the eco-driving probability density acquired in (S81) with information of the host vehicle calculated in (S82), and calculates the eco-driving capability (S83) [comparing estimation image with reference image].").
Therefore, it would have been obvious to one of the ordinary skill of the art before the effective filing date of the claimed invention to modify Naiwala, et al. to include the teaching of Sekiyama, et al. based on a reasonable expectation of success and motivation to improve the performance evaluation of a driver in a vehicle with respect a designated driving condition (Sekiyama, et al. Paragraphs [0001], [0007]).
Claims 3 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Naiwala, et al. (U.S. Patent No. 10629088) and Sekiyama, et al. (U.S. Patent Application Publication No. 20120232741) in view of Huang, et al. (U.S. Patent Application Publication No. 20100209888).
Regarding claim 3, Naiwala, et al. and Sekiyama, et al. does not teach the driving skill evaluation method according to claim 1, further comprising performing a preliminary setting process of setting the two or more curves of multiple curves, wherein the preliminary setting process includes checking whether each of the multiple curves satisfies a condition that an average value of values of a parameter included in traveling data of a vehicle and corresponding to a direction change in a traveling direction of the vehicle is less than a third value, and that a maximum value of the parameter is less than a fourth value larger than the third value, and setting curves not satisfying the condition, of the multiple curves, as the two or more curves.
In a similar field of endeavor (driving skill evaluation based on curve handing maneuvers), Huang, et al. teaches: The driving skill evaluation method according to claim 1, further comprising performing a preliminary setting process of setting the two or more curves of multiple curves, (Paragraph [0174]: "FIG. 20 is a block diagram of a system (360) showing one embodiment as to how the driving skill diagnosis processor (348) identifies the differences between the driver's behavior and an average driver [setting curve procedure].")
wherein the preliminary setting process includes checking whether each of the multiple curves satisfies a condition that an average value of values of a parameter included in traveling data of a vehicle and corresponding to a direction change in a traveling direction of the vehicle is less than a third value, (Paragraph [0177]: "…decision fusion techniques, such as a Bayesian fusion and Dempster-Shafer fusion, can be used and applied in the decision fusion processor (56). To demonstrate how this works, a simple example of weighted-average based decision is given below [curve - average value of values corresponding to direction change in vehicle]." ; Paragraph [0200]: "The series of weights K_LP(i) is determined to maximize the differentiation among the desired classes of driving skill based on test data of a test subject with well recognized driving skills. For example, if it is desired to classify drivers into three levels of driving skill, high-skill driver, average skill driver and low-skill driver [parameter measurement]" ; Paragraph [0208]: "Low driving skill when SI_LP-2<SI_LP [less than third value]")
and that a maximum value of the parameter is less than a fourth value larger than the third value, (Paragraph [0207]: "Good driving skill when SILP<SILP_l [maximum value of parameter is less than fourth value larger than third value]")
and setting curves not satisfying the condition, of the multiple curves, as the two or more curves (Paragraph [0194]: "After separation of expert and non-expert drivers, the same process can be applied to classify whether a nonexpert driver falls into the category of an average driver or a low-skill driver. Consequently, driving skill can be characterized with three types with a two-tier process as described above [creating curves not satisfying condition of good driver].").
Therefore, it would have been obvious to one of the ordinary skill of the art before the effective filing date of the claimed invention to modify the combination of Naiwala, et al. and Sekiyama, et al. to include the teaching of Huang, et al. based on a reasonable expectation of success and motivation to improve the process of providing adaptive vehicle control as a function of a driving skill classification based on curve-handing maneuvers (Huang, et al. Paragraph [0002]).
Regarding claim 10, Naiwala, et al. and Sekiyama, et al. do not teach the driving skill evaluation method according to claim 1, wherein setting the one or more evaluation target curves includes: calculating a first average value of similarity levels for all combinations between the multiple pieces of the first data and the multiple pieces of the second data for each of the two or more curves; calculating a second average value of similarity levels for all combinations between the multiple pieces of the second data for each of the two or more curves; and setting the one or more evaluation target curves based on a curve where a difference between the first average value and the second average value is greater than or equal to a predetermined amount.
In a similar field of endeavor (driving skill evaluation based on curve handing maneuvers), Huang, et al. teaches: The driving skill evaluation method according to claim 1, wherein setting the one or more evaluation target curves includes: calculating a first average value of similarity levels for all combinations between the multiple pieces of the first data and the multiple pieces of the second data for each of the two or more curves; (Paragraph [0174]: "FIG. 20 is a block diagram of a system (360) showing one embodiment as to how the driving skill diagnosis processor (348) identifies the differences between the driver's behavior and an average driver [procedure - determining evaluation curve]." ; Paragraph [0175]: "FIG. 21 is a graph with frequency on the horizontal axis and magnitude on the vertical axis illustrating a situation where behavioral differences are identified through the variation of the frequency spectrum. Given a headway control maneuver, the driver may apply the brake in different ways according to a specific driving skill. While an average driver results in the spectrum in one distribution, another driver, such as driver-A, shows a higher magnitude in the low-frequency area and lower magnitude in the high-frequency area [first average of similarity values between multiple data points].")
calculating a second average value of similarity levels for all combinations between the multiple pieces of the second data for each of the two or more curves; ("Paragraph [0174]: ""FIG. 20 is a block diagram of a system (360) showing one embodiment as to how the driving skill diagnosis processor (348) identifies the differences between the driver's behavior and an average driver [procedure - determining evaluation curve]." ; Paragraph [0175]: "FIG. 21 is a graph with frequency on the horizontal axis and magnitude on the vertical axis illustrating a situation where behavioral differences are identified through the variation of the frequency spectrum. Given a headway control maneuver, the driver may apply the brake in different ways according to a specific driving skill. […] Driver-B shows the opposite trend [second average of similarity values between multiple data points]. The differences in these signal distributions can be used to determine the driving skill of the specific driver.")
and setting the one or more evaluation target curves based on a curve where a difference between the first average value and the second average value is greater than or equal to a predetermined amount (Paragraph [0176]: "…a properly trained neural network classifier can successfully characterize driver-A as low-skill and driver-B as high-skill if the difference is on the spectrum distribution is determined to have completed a predetermined threshold [difference between first and second average value is greater than predetermined amount].").
Therefore, it would have been obvious to one of the ordinary skill of the art before the effective filing date of the claimed invention to modify the combination of Naiwala, et al. and Sekiyama, et al. to include the teaching of Huang, et al. based on a reasonable expectation of success and motivation to improve the process of providing adaptive vehicle control as a function of a driving skill classification based on curve-handing maneuvers (Huang, et al. Paragraph [0002]).
Response to Arguments
Applicant's arguments filed on March 6, 2026 have been fully considered but they are not persuasive.
Applicant asserted that amended claims 1 and 8-9 were patentable over Naiwala, et al. because the reference did not meet the claim limitation of setting evaluation target curves from among two or more curves using (i) similarity data between data and second data and (ii) similarity among second data for each curve. The examiner disagrees. In Naiwala, et al., the data obtained from the driving data acquisition unit (4) is obtained from vehicle sensors (2), in which multiple pieces of data are considered (Col. 3, lines 51-55). Additionally, the evaluation target curves are determined through the process of compared the data to each other in addition to being compared to a reference value using the process of driving skill comparison in Fig. 6A (Col. 5, lines 48-67) and further conducting similarity analysis using distance measurement techniques, as described in Fig. 6B (Col. 6, lines 1-15). Subsequently, it would have been obvious to combine Naiwala, et al. with Sekiyama, et al. because Sekiyama, et al. teaches the process of generating multiple kernel density estimation images based on data of multiple drivers (Paragraphs [0077], [0106]).
Applicant also asserted that amended claims 1 and 8-9 were patentable over Naiwala, et al. because the reference did not meet the claim limitation of disclosing generating multiple kernel density estimation images for each curve to obtain first data and second data as image data. Please note that Sekiyama, et al. was cited in order to teach these features. In Sekiyama, et al., the data collection parameters explicitly state that the proficiency index value has the ability to be determined through multiple data sets involving “…an unspecified number of drivers” (Paragraph [0077]). This data is then inputted into a probability density estimation unit, which creates “…estimates the probability density function p by Kernal density estimation” multiple pieces of data specified by N (Paragraph [0106]). Subsequently, it would have been obvious to combine Sekiyama, et al. with Naiwala, et al. because Naiwala, et al. teaches curve positions (Col 6, lines 18-21 and Col. 4, lines 58-62) and the process of completing similarity analysis based on the data (Col. 6, lines 12-24).
Applicant also asserted that amended claims 1 and 8-9 were patentable over Naiwala, et al. because the reference did not meet the claim limitations of multiple first or second similarity levels. The examiner disagrees. In Naiwala, et al., the data obtained from the driving data acquisition unit (4) is obtained from vehicle sensors (2), in which multiple pieces of data are considered (Col. 3, lines 51-55). Additionally, the resulting curves are compared to each other in addition to being compared to a reference value using the process of driving skill comparison in Fig. 6A (Col. 5, lines 48-67). Subsequently, it would have been obvious to combine Naiwala, et al. with Sekiyama, et al. because Sekiyama, et al. teaches the process of generating multiple kernel density estimation images based on data of multiple drivers (Paragraphs [0077], [0106]).
Therefore, it can be concluded that since the combination of Naiwala, et al. and Sekiyama, et al. reads on the claim limitations of setting evaluation target curves from among two or more curves using (i) similarity data between data and second data and (ii) similarity among second data for each curve, disclosing generating multiple kernel density estimation images for each curve to obtain first data and second data as image data, and multiple first and second similarity levels, as stated in amended claims 1, 8, and 11, the arguments presented by the Applicant are not persuasive, and the rejection is maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kondo, et al. (U.S. Patent No. 11117593) teaches an information system which provides driving assistance information with respect to a designated driving skill-level for inexperienced drivers.
Applicant is considered to have implicit knowledge of the entire disclosure once a reference has been cited. Therefore, any previously cited figures, columns and lines should not be considered to limit the references in any way. The entire reference must be taken as a whole; accordingly, the Examiner contends that the art supports the rejection of the claims and the rejection is maintained.
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.
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/TORRENCE S MARUNDA II/Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663
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