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 08/15/2025 have been fully considered.
In regards to the independent claims, Applicant argues features from dependent claims that required an obviousness rejection have been amended into the independent claims, therefore the anticipation rejections are overcome. Applicant argues the combination of Hyun (US 20180061253) and Blackham (US 20210192636) fails to disclose the newly amended features of calculating a cluster value as recited and then associating a risk factor with a cluster value. Applicant argues Blackham has been relied on as teaching determining an overall risk score as a weighted average of individual risk scores, but the cluster value is calculated as a weighted average of several different factors, but not the claimed risk score. Therefore, Applicant concludes, the rejection of the independent claims should be withdrawn.
However, while the amended independent claim are certainly found to overcome the anticipation rejection record, a subsequent obviousness rejection is still found to be necessary. Hyun teaches an accident rate for each identified type of vehicle may be determined from a server of an insurance company, which is used to determine an associated risk. The server searches the database for risks of vehicles near the host vehicle and selects a target vehicle from vehicles within a range of the location of the host vehicle, then determines the risk for the target vehicle, which has cluster values of the host vehicle’s location over time, which is used to select the relevant nearby vehicles, thereby clustering about the location value of the host vehicle, The server searches the database for risks of vehicles near the host vehicle and selects a target vehicle from vehicles within a range of the location of the host vehicle, then determines the risk for the target vehicle. Blackham then teaches determining an overall risk score as a weighted average of individual risk scores with a table for each individual risk score, where the overall risk score is a value at which risk is clustered and thereby also serves as a cluster value corresponding to one of a plurality of vehicle descriptor sets of a particular vehicle. From the combination of these references, one of ordinary skill would have arrived at determining an overall risk score from an insurance risk and an additional risk, associated with individual locations of the host vehicle and reflecting accessed insurance data corresponding to one particular set of descriptors for one particular vehicle of the sets of vehicle descriptors. This is precisely what is required by the claim. The claim requires calculating a cluster value, which here may be either the location coordinate value of Hyun or the overall risk score of Blackham, using a weighted average of an insurance risk score and an additional risk score, which here is explained as an insurance risk score from Hyun and performing a weighted average of additional risk scores, which may be insurance risk scores as in Blackham, where the individual risk scores are each associated with descriptors. As such, Hyun, as modified by Blackham reads comfortably on the amended independent claims.
As such, this argument is unpersuasive.
In regards to claims 11 and 19, Applicant argues Hyun has been admitted to not teach the recited table with statistics table and Blackham does not remedy this deficiency. Therefore, Applicant concludes the rejection of these claims should be withdrawn.
However, Blackham teaches a table of risk score values to be used in a weighted calculation for determining an overall risk score. As the claims only broadly recite that this is a “statistics table” but provide little explanation of the content and makeup of this table, an equally broad application of prior art is appropriate. In order to be a statistics table, the data must be in a table like format and be in some way related to or usable for statistics, which is exactly what is presented within Blackham as the data for determining each individual risk is determined from past, present, and future driving behavior including using statistics involved techniques, such as mathematical weighting and historical analysis.
As such, this argument is unpersuasive.
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 1-3, 5-16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Hyun (US 20180061253), in view of Blackham et al. (US 20210192636).
In regards to claim 1, Hyun teaches a method of planning a path for a vehicle, the method comprising: (Figs 2, 4, 7, 8.)
receiving a plurality of perception images of an area surrounding the vehicle with at least one sensor; ([0055] at least one sensor or camera of host vehicles senses vehicles within the environment around the host vehicle, including ahead, behind, and on both sides of the host vehicle. This requires a plurality of perception images for each direction over time.)
detecting at least one perception task from the plurality of perception images, wherein the at least one perception task includes identifying a neighboring vehicle; ([0055] area surrounding the host vehicle is analyzed to determine other vehicles and their appearances, including particularly stability of appearance of a particular part of each nearby vehicle, deterioration of nearby vehicles, type of nearby vehicles, and freight load stability of nearby vehicles, where each of these are perception tasks that identify particular nearby vehicles.)
recognizing a plurality of vehicle descriptors of the neighboring vehicle in the plurality of perception images; ([0055], [0064], [0065] area surrounding the host vehicle is analyzed to determine other vehicles and their appearances, including particularly driving behavior, stability of appearance of a particular part of each nearby vehicle, deterioration of nearby vehicles, type of nearby vehicles, and freight load stability of nearby vehicles, where the results of each of these are descriptors of neighboring vehicles in the plurality of images. [0074] type of the target vehicle may particularly be recognized as the model and production year of the target vehicle.)
associating one of a plurality of predetermined vehicle descriptor sets with a corresponding one of a plurality of cluster values, wherein each of the plurality of cluster values is associated with a risk factor and ([0074] accident rate for each identified type of vehicle may be determined from a server of an insurance company, which is used to determine an associated risk, [0068]-[0075] appearance characteristic contains risks based on stability, appearance of freight, deterioration of the target vehicle, type of the target vehicle, and age of the target vehicle, as well as accident rate of the type of vehicle, where each appearance characteristic is determined using deep learning networks trained on saved images. These form vehicle descriptor sets of accident rate and each type of appearance characteristic for each type of vehicle, and thereby a plurality of predetermined vehicle descriptor sets, including an association of one of the plurality. [0098] server searches database for risks of vehicles near the host vehicle and selects a target vehicle from vehicles within a range of the location of the host vehicle, then determines the risk for the target vehicle. This has cluster values of the host vehicle’s locations over time, which is used to select a range within which target vehicles are determined, thereby clustering about the location value of the host vehicle, which has associated risk based on the identified presences, types, and features of nearby vehicles set as target vehicles.)
associating the risk factor associated with the neighboring vehicle by mapping the plurality of vehicle descriptors recognized for the neighboring vehicle onto one of the plurality of predetermined vehicle descriptor sets and assigning the risk factor to the neighboring vehicle associated with the one of the plurality of cluster values; ([0098] server searches database for risks of vehicles near the host vehicle and selects a target vehicle from vehicles within a range of the location of the host vehicle, transmitting, risk, identifier, driving characteristic, and appearance characteristic, and then determines the risk of the target vehicle. [0057], [0068]-[0075] appearance characteristic contains risks based on stability, appearance of freight, deterioration of the target vehicle, type of the target vehicle, and age of the target vehicle, as well as accident rate of the type of vehicle. Risk is similarly based on driving characteristic. Each of the driving characteristic and the appearance characteristic provide a risk factor, as well as each component of the driving characteristic and the appearance characteristic, such as stability, appearance of freight, deterioration, type, and age, which is done by mapping the recognized descriptors of these characteristics to particular vehicles and determining the risk of each factor, using the clustering location of the host vehicle to select the target vehicles.)
planning the path for the vehicle based on the at least one perception task and the risk factor; ([0058] vehicle control is adjusted based on determined risk from perceived target vehicles in environment around the host vehicle, which includes reducing speed and setting a path for the vehicle to then follow.) and
executing the path for the vehicle. ([0058] vehicle control is adjusted based on determined risk from perceived target vehicles in environment around the host vehicle, which includes reducing speed and setting a path for the vehicle to then follow. [0077], [0099] in steps 202 and 803, control of vehicle is executed to operate according to planned control.)
Hyun also teaches an accident rate for each identified type of vehicle may be determined from a server of an insurance company, which is used to determine an associated risk ([0074]). The server searches the database for risks of vehicles near the host vehicle and selects a target vehicle from vehicles within a range of the location of the host vehicle, then determines the risk for the target vehicle ([0098]). This has cluster values of the host vehicle’s locations over time, which is used to select a target vehicle within a range, thereby clustering about the location value of the host vehicle, which has associated risk based on the identified presences, types, and features of nearby vehicles set as target vehicles. As this is a risk score determined from an insurance company server, it includes an insurance risk score.
Hyun does not teach:
the cluster value is determined based on a weighted average of the at least one insurance risk score and an additional risk score for the corresponding one of the plurality of predetermined vehicle descriptor sets;
However, Blackham teaches determining an overall risk score as a weighted average of individual risk scores with a table for each individual risk score ([0037]-[0043]). The overall risk score is a value at which risk is clustered and thereby serves as a cluster value corresponding to one of a plurality of vehicle descriptor sets of a particular vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the risk determining method of Hyun, by incorporating the teachings of Blackham, such that an overall risk score is determined as a cluster value by weighted average of risk scores, including at least an insurance risk score and an additional risk score, associated with both the individual locations of the host vehicle and reflecting accessed insurance data corresponding to one particular set of descriptors for one particular vehicle of the sets of vehicle descriptors.
The motivation to do so is that, as acknowledged by Blackham, this allows for increased accuracy and reliability of insurance risk scores ([0007]).
In regards to claim 2, Hyun, as modified by Blackham, teaches the method of claim 1, wherein the neighboring vehicle includes at least one of a heavy-duty vehicle, a light-duty vehicle, a sports car, a luxury car, a sport utility vehicle, a family car, or a motorcycle. ([0074] type of target vehicle is determined, which includes any and all type of target vehicles including at least one heavy-duty vehicle, light-duty vehicle, sports car, luxury car, sport utility vehicle, family car, or motorcycle.)
In regards to claim 3, Hyun, as modified by Blackham, teaches the method of claim 1, wherein each of the plurality of predetermined vehicle descriptor sets includes of a vehicle brand, a vehicle model, and a range of vehicle ages. ([0074] appearance characteristic contains model and production year, which is used to determine how long ago the production year from the current data. This provides an age range of year of production and model provides the maker of the vehicle, and thereby brand.)
In regards to claim 5, Hyun, as modified by Blackham, teaches the method of claim 1.
Hyun teaches an accident rate for each identified type of vehicle may be determined from a server of an insurance company, which is used to determine an associated risk ([0074]). The server searches the database for risks of vehicles near the host vehicle and selects a target vehicle from vehicles within a range of the location of the host vehicle, then determines the risk for the target vehicle ([0098]).
Blackham teaches determining an overall risk score as a weighted average of individual risk scores with a table for each individual risk score ([0037]-[0043]).
It would have been obvious to one of ordinary skill in the before the effective filing date of the application to modify the risk determining method of Hyun, as already modified by Blackham, by further incorporating the teachings of Blackham, such that an overall risk score is determined as a weighted average of individual risk scores with a table for each individual risk score, where the table includes risks from the insurance database of Hyun and the risks are associated with different sets of descriptors.
The motivation to do so is the same as acknowledged by Blackham in regards to claim 1.
In regards to claim 6, Hyun, as modified by Blackham, teaches the method of claim 1.
Hyun teaches an accident rate for each identified type of vehicle may be determined from a server of an insurance company, which is used to determine an associated risk ([0074]). The server searches the database for risks of vehicles near the host vehicle and selects a target vehicle from vehicles within a range of the location of the host vehicle, then determines the risk for the target vehicle ([0098]).
Blackham teaches determining an overall risk score as a weighted average of individual risk scores with a table for each individual risk score ([0037]-[0043]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the risk determining method of Hyun, as already modified by Blackham, by further incorporating the teachings of Blackham, such that risk scores include at least one risk score for each type of vehicle applied for each vehicle in the environment around the host vehicle as in Hyun, and each risk score is composed of multiple risk scores which are combined using a weighted average to determine an overall weighted risk score, which is a cluster value.
The motivation to do so is the same as acknowledged by Blackham in regards to claim 1.
In regards to claim 7, Hyun, as modified by Blackham, teaches the method of claim 1, wherein the additional risk score is determined from at least one report risk score corresponding to one of the plurality of predetermined vehicle descriptor sets. ([0064], [0065] driving behavior is analyzed to determine risk associated with behavior from time analyzed distance and speed, where this time analysis forms a report of the risk associated with driving behavior characteristics, which applies to each and every risk score.)
In regards to claim 8, Hyun, as modified by Blackham, teaches the method of claim 7, wherein the at least one report risk score includes at least one of a vehicle report or a police report relating to a vehicle type of the neighboring vehicle. ([0064], [0065] driving behavior is analyzed to determine risk associated with behavior from time analyzed distance and speed, where this time analysis forms a report of the risk associated with driving behavior characteristics. This comes from a vehicle report and is associated with the particular neighboring vehicle being analyzed and therefore the type of the particular neighboring vehicle.)
In regards to claim 9, Hyun, as modified by Blackham teaches the method of claim 1.
Hyun also teaches an accident rate for each identified type of vehicle may be determined from a server of an insurance company, which is used to determine an associated risk ([0074]). The server searches the database for risks of vehicles near the host vehicle and selects a target vehicle from vehicles within a range of the location of the host vehicle, then determines the risk for the target vehicle ([0098]). This has cluster values of the host vehicle’s locations over time, which is used to select a target vehicle within a range, thereby clustering about the location value of the host vehicle, which has associated risk based on the identified presences, types, and features of nearby vehicles set as target vehicles. Driving behavior is analyzed to determine risk associated with the behavior analyzed over time, determining at least distance and speed based driving behavior, which forms a report of the risk associated with this behavior for each particular vehicle ([0064], [0065]). Each of these appearance and behavior based risk is associated with a particular vehicle descriptor and set of vehicle descriptors corresponding to particular vehicles.
Blackham teaches determining an overall risk score as a weighted average of individual risk scores with a table for each individual risk score ([0037]-[0043]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the risk determining method of Hyun, as already modified by Blackham, by further incorporating the teachings of Blackham, such that each of the individual risk scores of Hyun, as well as those of Blackham, which include both insurance based risk scores and report based risk scores, are combined using a weighted average to determine overall risk scores, which are cluster values.
The motivation to do so is the same as acknowledged by Blackham in regards to claim 1.
In regards to claim 10, Hyun, as modified by Blackham, teaches the method of claim 9.
Hyun teaches an accident rate for each identified type of vehicle may be determined from a server of an insurance company, which is used to determine an associated risk ([0074]). The server searches the database for risks of vehicles near the host vehicle and selects a target vehicle from vehicles within a range of the location of the host vehicle, then determines the risk for the target vehicle ([0098]). This has cluster values of the host vehicle’s locations over time, which is used to select a target vehicle within a range, thereby clustering about the location value of the host vehicle, which has associated risk based on the identified presences, types, and features of nearby vehicles set as target vehicles. Driving behavior is analyzed to determine risk associated with the behavior analyzed over time, determining at least distance and speed based driving behavior, which forms a report of the risk associated with this behavior for each particular vehicle ([0064], [0065]). Each of these appearance and behavior based risk is associated with a particular vehicle descriptor and set of vehicle descriptors corresponding to particular vehicles.
Blackham teaches determining an overall risk score as a weighted average of individual risk scores with a table for each individual risk score ([0037]-[0043]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the risk determining method of Hyun, as already modified by Blackham, by further incorporating the teachings of Blackham, such that each of the individual risk scores of Hyun, as well as those of Blackham, which include both insurance based risk scores and report based risk scores, are combined using a weighted average to determine overall risk scores, which are cluster values.
The motivation to do so is the same as acknowledged by Blackham in regards to claim 1.
In regards to claim 11, Blackham teaches determining an overall risk score as a weighted average of individual risk scores with a table for each individual risk score ([0037]-[0043]). This is a statistical table which associates descriptors with their risk scores, which is a risk factor, which then performs weighted summation to determine an overall risk score, which is a cluster value associated with each risk factor.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the risk determining method of Hyun, as already modified by Blackham, by further incorporating the teachings of Blackham, such that a table is constructed which associates each individual descriptor of Hyun with corresponding risk scores, and used to calculate an overall risk score, which is a cluster value for each particular set of descriptors for a particular vehicle.
The motivation to do so is the same as acknowledged by Blackham in regards to claim 1.
In regards to claim 12, Hyun, as modified by Blackham, teaches the method of claim 1, wherein the vehicle is an autonomous motor vehicle. ([0052] autonomous driving performed by autonomous vehicle.)
In regards to claim 13, Hyun, as modified by Blackham, teaches the method of claim 1, wherein the at least one sensor includes at least one camera. ([0055] at least one sensor or camera of host vehicles senses vehicles within the environment around the host vehicle, including ahead, behind, and on both sides of the host vehicle.)
In regards to claim 14, Hyun teaches a non-transitory computer-readable storage medium embodying programmed instructions which, when executed by a processor, are operable for performing a method comprising: ([0117] non-transitory computer-readable medium stores instructions for execution by processor.)
receiving a plurality of perception images of an area surrounding a vehicle with at least one sensor; ([0055] at least one sensor or camera of host vehicles senses vehicles within the environment around the host vehicle, including ahead, behind, and on both sides of the host vehicle. This requires a plurality of perception images for each direction over time.)
detecting at least one perception task from the plurality of perception images, wherein the at least one perception task includes identifying a neighboring vehicle; ([0055] area surrounding the host vehicle is analyzed to determine other vehicles and their appearances, including particularly stability of appearance of a particular part of each nearby vehicle, deterioration of nearby vehicles, type of nearby vehicles, and freight load stability of nearby vehicles, where each of these are perception tasks that identify particular nearby vehicles.)
recognizing a plurality of vehicle descriptors of the neighboring vehicle in the plurality of perception images; ([0055], [0064], [0065], area surrounding the host vehicle is analyzed to determine other vehicles and their appearances, including particularly driving behavior, stability of appearance of a particular part of each nearby vehicle, deterioration of nearby vehicles, type of nearby vehicles, and freight load stability of nearby vehicles, where the results of each of these are descriptors of neighboring vehicles in the plurality of images. [0074] type of the target vehicle may particularly be recognized as the model and production year of the target vehicle.)
associating one of a plurality of predetermined vehicle descriptor sets with a corresponding one of a plurality of cluster values, wherein each of the plurality of cluster values is associated with a risk factor and ([0074] accident rate for each identified type of vehicle may be determined from a server of an insurance company, which is used to determine an associated risk, [0068]-[0075] appearance characteristic contains risks based on stability, appearance of freight, deterioration of the target vehicle, type of the target vehicle, and age of the target vehicle, as well as accident rate of the type of vehicle, where each appearance characteristic is determined using deep learning networks trained on saved images. These form vehicle descriptor sets of accident rate and each type of appearance characteristic for each type of vehicle, and thereby a plurality of predetermined vehicle descriptor sets, including an association of one of the plurality. [0098] server searches database for risks of vehicles near the host vehicle and selects a target vehicle from vehicles within a range of the location of the host vehicle, then determines the risk for the target vehicle. This has cluster values of the host vehicle’s locations over time, which is used to select a range within which target vehicles are determined, thereby clustering about the location value of the host vehicle, which has associated risk based on the identified presences, types, and features of nearby vehicles set as target vehicles.)
associated the risk factor associated with the neighboring vehicle by mapping the plurality of vehicle descriptors recognized for the neighboring vehicle onto one of the plurality of predetermined vehicle descriptor sets and assigning the risk factor to the neighboring vehicle associated with the one of the plurality of cluster values; ([0098] server searches database for risks of vehicles near the host vehicle and selects a target vehicle from vehicles within a range of the location of the host vehicle, transmitting, risk, identifier, driving characteristic, and appearance characteristic, and then determines the risk of the target vehicle. [0057], [0068]-[0075] appearance characteristic contains risks based on stability, appearance of freight, deterioration of the target vehicle, type of the target vehicle, and age of the target vehicle, as well as accident rate of the type of vehicle. Risk is similarly based on driving characteristic. Each of the driving characteristic and the appearance characteristic provide a risk factor, as well as each component of the driving characteristic and the appearance characteristic, such as stability, appearance of freight, deterioration, type, and age, which is done by mapping the recognized descriptors of these characteristics to particular vehicles and determining the risk of each factor, using the clustering location of the host vehicle to select the target vehicles.)
planning a path for the vehicle based on the at least one perception task and the risk factor; ([0058] vehicle control is adjusted based on determined risk from perceived target vehicles in environment around the host vehicle, which includes reducing speed and setting a path for the vehicle to then follow.) and
executing the path for the vehicle. ([0058] vehicle control is adjusted based on determined risk from perceived target vehicles in environment around the host vehicle, which includes reducing speed and setting a path for the vehicle to then follow. [0077], [0099] in steps 202 and 803, control of vehicle is executed to operate according to planned control.)
Hyun also teaches an accident rate for each identified type of vehicle may be determined from a server of an insurance company, which is used to determine an associated risk ([0074]). The server searches the database for risks of vehicles near the host vehicle and selects a target vehicle from vehicles within a range of the location of the host vehicle, then determines the risk for the target vehicle ([0098]). This has cluster values of the host vehicle’s locations over time, which is used to select a target vehicle within a range, thereby clustering about the location value of the host vehicle, which has associated risk based on the identified presences, types, and features of nearby vehicles set as target vehicles. As this is a risk score determined from an insurance company server, it includes an insurance risk score.
Hyun does not teach:
the cluster value is determined based on a weighted average of at least one insurance risk score and an additional risk score for the corresponding one of the plurality of predetermined vehicle descriptor sets;
However, Blackham teaches determining an overall risk score as a weighted average of individual risk scores with a table for each individual risk score ([0037]-[0043]). The overall risk score is a value at which risk is clustered and thereby serves as a cluster value corresponding to one of a plurality of vehicle descriptor sets of a particular vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the risk determining instructions of Hyun, by incorporating the teachings of Blackham, such that an overall risk score is determined as a cluster value by weighted average of risk scores, including at least an insurance risk score and an additional risk score, associated with both the individual locations of the host vehicle and reflecting accessed insurance data corresponding to one particular set of descriptors for one particular vehicle of the sets of vehicle descriptors.
The motivation to do so is that, as acknowledged by Blackham, this allows for increased accuracy and reliability of insurance risk scores ([0007]).
In regards to claim 15, Hyun, as modified by Blackham, teaches the storage medium of claim 14.
Claim 15 recites a storage medium having substantially the same features of claim 13 above, therefore claim 15 is rejected for the same reasons as claim 13.
In regards to claim 16, Hyun, as modified by Blackham, teaches the storage medium of claim 14.
Claim 16 recites a storage medium having substantially the same features of claims 2 and 3 above, therefore claim 16 is rejected for the same reasons as claim 2 and 3.
In regards to claim 20, Hyun teaches a vehicle system comprising: (Figs 9, 10.)
a drivetrain; ([0081] host vehicle may be automobile including an engine or motor which drive wheels and therefore necessarily includes a drivetrain transferring power from the engine or motor to the wheels.)
a power source in communication with the drivetrain; ([0081] host vehicle may be automobile including an engine or motor, which supply power.)
a plurality of sensors: ([0055] at least one sensor or camera of host vehicles senses vehicles within the environment around the host vehicle, including ahead, behind, and on both sides of the host vehicle. This requires a plurality of perception images for each direction over time. At least one sensors particularly includes multiple sensors, such as two or more.)
a controller in communication with the plurality of sensors and configured to: ([0056], [0101] autonomous driving apparatus contains processor which communicates with sensors and performs operations.)
receive a plurality of perception images of an area surrounding a vehicle with at least one sensor; ([0055] at least one sensor or camera of host vehicles senses vehicles within the environment around the host vehicle, including ahead, behind, and on both sides of the host vehicle. This requires a plurality of perception images for each direction over time.)
detect at least one perception task from the plurality of perception images, wherein the at least one perception task includes identifying a neighboring vehicle; ([0055] area surrounding the host vehicle is analyzed to determine other vehicles and their appearances, including particularly stability of appearance of a particular part of each nearby vehicle, deterioration of nearby vehicles, type of nearby vehicles, and freight load stability of nearby vehicles, where each of these are perception tasks that identify particular nearby vehicles.)
recognize a plurality of vehicle descriptors of the neighboring vehicle in the plurality of perception images; ([0055], [0064], [0065] area surrounding the host vehicle is analyzed to determine other vehicles and their appearances, including particularly driving behavior, stability of appearance of a particular part of each nearby vehicle, deterioration of nearby vehicles, type of nearby vehicles, and freight load stability of nearby vehicles, where the results of each of these are descriptors of neighboring vehicles in the plurality of images. [0074] type of the target vehicle may particularly be recognized as the model and production year of the target vehicle.)
associate one of a plurality of predetermined vehicle descriptor sets with a corresponding one of a plurality of cluster values, wherein each of the plurality of cluster values is associated with a risk factor and ([0074] accident rate for each identified type of vehicle may be determined from a server of an insurance company, which is used to determine an associated risk, [0068]-[0075] appearance characteristic contains risks based on stability, appearance of freight, deterioration of the target vehicle, type of the target vehicle, and age of the target vehicle, as well as accident rate of the type of vehicle, where each appearance characteristic is determined using deep learning networks trained on saved images. These form vehicle descriptor sets of accident rate and each type of appearance characteristic for each type of vehicle, and thereby a plurality of predetermined vehicle descriptor sets, including an association of one of the plurality. [0098] server searches database for risks of vehicles near the host vehicle and selects a target vehicle from vehicles within a range of the location of the host vehicle, then determines the risk for the target vehicle. This has cluster values of the host vehicle’s locations over time, which is used to select a range within which target vehicles are determined, thereby clustering about the location value of the host vehicle, which has associated risk based on the identified presences, types, and features of nearby vehicles set as target vehicles.)
associate the risk factor associated with the neighboring vehicle by mapping the plurality of vehicle descriptors recognized for the neighboring vehicle onto one of the plurality of predetermined vehicle descriptor sets and assigning the risk factor to the neighboring vehicle associated with one of the plurality of cluster values; ([0098] server searches database for risks of vehicles near the host vehicle and selects a target vehicle from vehicles within a range of the location of the host vehicle, transmitting, risk, identifier, driving characteristic, and appearance characteristic, and then determines the risk of the target vehicle. [0057], [0068]-[0075] appearance characteristic contains risks based on stability, appearance of freight, deterioration of the target vehicle, type of the target vehicle, and age of the target vehicle, as well as accident rate of the type of vehicle. Risk is similarly based on driving characteristic. Each of the driving characteristic and the appearance characteristic provide a risk factor, as well as each component of the driving characteristic and the appearance characteristic, such as stability, appearance of freight, deterioration, type, and age, which is done by mapping the recognized descriptors of these characteristics to particular vehicles and determining the risk of each factor, using the clustering location of the host vehicle to select the target vehicles.)
plan a path for the vehicle based on the at least one perception task and the risk factor; ([0058] vehicle control is adjusted based on determined risk from perceived target vehicles in environment around the host vehicle, which includes reducing speed and setting a path for the vehicle to then follow.) and
execute the path for the vehicle. ([0058] vehicle control is adjusted based on determined risk from perceived target vehicles in environment around the host vehicle, which includes reducing speed and setting a path for the vehicle to then follow. [0077], [0099] in steps 202 and 803, control of vehicle is executed to operate according to planned control.)
Hyun also teaches an accident rate for each identified type of vehicle may be determined from a server of an insurance company, which is used to determine an associated risk ([0074]). The server searches the database for risks of vehicles near the host vehicle and selects a target vehicle from vehicles within a range of the location of the host vehicle, then determines the risk for the target vehicle ([0098]). This has cluster values of the host vehicle’s locations over time, which is used to select a target vehicle within a range, thereby clustering about the location value of the host vehicle, which has associated risk based on the identified presences, types, and features of nearby vehicles set as target vehicles. As this is a risk score determined from an insurance company server, it includes an insurance risk score.
Hyun does not teach:
the cluster value is determined based on a weighted average of at least one insurance risk score and an additional risk score for the corresponding one of the plurality of predetermined vehicle descriptor sets;
However, Blackham teaches determining an overall risk score as a weighted average of individual risk scores with a table for each individual risk score ([0037]-[0043]). The overall risk score is a value at which risk is clustered and thereby serves as a cluster value corresponding to one of a plurality of vehicle descriptor sets of a particular vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the risk determining system of Hyun, by incorporating the teachings of Blackham, such that an overall risk score is determined as a cluster value by weighted average of risk scores, including at least an insurance risk score and an additional risk score, associated with both the individual locations of the host vehicle and reflecting accessed insurance data corresponding to one particular set of descriptors for one particular vehicle of the sets of vehicle descriptors.
The motivation to do so is that, as acknowledged by Blackham, this allows for increased accuracy and reliability of insurance risk scores ([0007]).
Claims 19 and 21-23 are rejected under 35 U.S.C. 103 as being unpatentable over Hyun, in view of Blackham, in further view of Chatni et al. (WO 2022081083).
In regards to claim 19, Hyun, as modified by Blackham, teaches the storage medium of claim 14.
Hyun also teaches driving behavior is analyzed to determine the risk associated with behavior from time analyzed distance and speed, where this time analysis forms a report of the risk associated with driving behavior characteristics ([0064], [0065]).
Blackham also teaches determining an overall risk score as a weighted average of individual risk scores with a table for each individual risk score ([0037]-[0043]). The overall risk score is a value at which risk is clustered and thereby serves as a cluster value corresponding to one of a plurality of vehicle descriptor sets of a particular vehicle.
Hyun, as modified by Blackham, does not teach:
wherein the additional risk score is determined from at least one report risk score based on a police report relating to a vehicle type of the neighboring vehicle and the police report includes statistical information regarding a likelihood of the neighboring vehicle speeding or being involved in a traffic violation;
wherein the at least one insurance risk score includes a plurality of insurance risk scores corresponding to one of the plurality of predetermined vehicle descriptor sets and the risk factor associated with a corresponding one of the cluster values is determined based on the plurality of insurance risk scores and the police report; and
wherein plurality of predetermined vehicle descriptor sets wherein the risk factor associated with each of the plurality of cluster values is determined from a statistics table identifying each of the plurality of predetermined vehicle descriptor sets with the plurality of insurance risk scores and at least one report risk score based on the police report.
However, Chatni teaches a probability of future speeding of a vehicle and driver may be transmitted and communicated through a speeding report ([0031]). This is a police report as it is a report of illegal activity that concerns police.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the risk determining instructions of Hyun, as already modified by Blackham, by further incorporating the teachings of Blackham and incorporating the teachings of Chatni, such that a report of probability of speeding is determined which is a report of police required activity that contains statistical information, which is further incorporated into the determination of overall risk by weighted average of the insurance risk score and additional risk score corresponding to individual locations of the vehicle and included within the accessed insurance data as a table entry corresponding to one particular set of descriptors for each particular vehicle of the set of vehicle descriptors.
The motivation to determine such an overall risk score is the same as acknowledged by Blackham in regards to claim 1. The motivation to retrieve a probability of speeding report is that, as acknowledged by Chatni, this allows for efficiently detecting speeding ([0002]), which one of ordinary skill would have recognized allows for improving safety by better accounting for speeding.
In regards to claim 21, Hyun, as modified by Blackham, teaches the method of claim 7.
Hyun, as modified by Blackham, does not teach:
wherein the at least one report risk score is based on a police report relating to a vehicle type of the neighboring vehicle and the police report includes statistical information regarding a likelihood of the at least one neighboring vehicle speeding or being involved in a traffic violation.
However, Chatni teaches a probability of future speeding of a vehicle and driver may be transmitted and communicated through a speeding report ([0031]). This is a police report as it is a report of illegal activity that concerns police.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the risk determining method of Hyun, as already modified by Blackham, by incorporating the teachings of Chatni, such that a report of probability of speeding is determined which is a report of police required activity that contains statistical information and incorporated into the retrieval of risk of neighboring vehicles.
The motivation to do so is the same as acknowledged by Chatni in regards to claim 19.
In regards to claim 22, Hyun, as modified by Blackham and Chatni, teaches the method of claim 21.
Hyun also teaches driving behavior is analyzed to determine the risk associated with behavior from time analyzed distance and speed, where this time analysis forms a report of the risk associated with driving behavior characteristics ([0064], [0065]).
Blackham also teaches determining an overall risk score as a weighted average of individual risk scores with a table for each individual risk score ([0037]-[0043]). The overall risk score is a value at which risk is clustered and thereby serves as a cluster value corresponding to one of a plurality of vehicle descriptor sets of a particular vehicle.
Chatni teaches a probability of future speeding of a vehicle and driver may be transmitted and communicated through a speeding report ([0031]). This is a police report as it is a report of illegal activity that concerns police.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the risk determining method of Hyun, as already modified by Blackham and Chatni, by further incorporating the teachings of Blackham and Chatni, such that a report of probability of speeding is determined which is a report of police required activity that contains statistical information, which is further incorporated into the determination of overall risk by weighted average of the insurance risk score and additional risk score corresponding to individual locations of the vehicle and included within the accessed insurance data as a table entry corresponding to one particular set of descriptors for each particular vehicle of the set of vehicle descriptors.
The motivations to do so are the same as acknowledged by Blackham in regards to claim 1 and Chatni in regards to claim 19.
In regards to claim 23, Hyun, as modified by Blackham, teaches the vehicle system of claim 20.
Claim 23 recites a storage medium having substantially the same features of claim 19 above, therefore claim 23 is rejected for the same reasons as claim 19.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yamaguchi et al. (US 20200168099) teaches recognizing a vehicle that is conducting hazardous driving in images and determine an associated risk.
Malkes et al. (US 20190333156) teaches calculating a risk weighted average to determine an overall risk within a table using insurance related risk.
Yoo et al. (US 20190263401) teaches determining risk according to type of an external vehicle.
Non-patent Literature Wenzel et al. “The effects of vehicle model and driver behavior on risk” teaches determining risk associated with different types of vehicles.
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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/MATTHIAS S WEISFELD/Examiner, Art Unit 3661
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