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 Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for matter specifically challenged in the argument.
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.
Claims 1,4,5,7,8,11,12,14,15,17,18, and 20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Sellschopp (DE 102019127974 A1) (hereinafter Sellschopp) in view of Stankoulov (US 20140162219 A1) (hereinafter Stankoulov) in further view of Hahne (DE 102023202878 A1) (hereinafter Hahne).
Regarding claim 1, Sellschopp teaches a system positioned in an autonomous vehicle(Sellschopp, paragraph 44, driving behavior of a vehicle can be detected automatically and almost in real time, so that if necessary warnings can be given [for] the operator or driver of an autonomous vehicle), the system comprising:
a plurality of sensors(Sellschopp, paragraph 15, driving situation data…transmitted by the sensors);
at least one memory configured to store instructions(Sellschopp, paragraph 10, a program code that is stored or stored on a programmable storage medium); and
at least one processor(Sellschopp, paragraph 10, the evaluation unit described can be based on at least one microprocessor) coupled to the at least one memory and configured to execute the stored instructions to perform operations comprising(Sellschopp, paragraph 10, The evaluation unit can, for example, comprise a program code that is stored or stored on a programmable storage medium and that implements the method described here):
based upon sensor data from the plurality of sensors(Sellschopp, paragraph 24, the large number of sensors advantageously provides redundancy for the acquisition of the driving behavior data and the environmental data ), determining an unsafe maneuver and a type of unsafe maneuver of a vehicle(Sellschopp discloses determining different risk assessments for a driving behavior, which corresponds to assessing driving behavior for unsafe maneuvering. For example, it determines the type of risk due to high acceleration using acceleration sensor, and frequent lane change based on different sensor. Sellschopp, paragraph 40, the resulting driving behavior, measured and used to calculate a risk score (based on the acceleration sensor)….the number of lane changes or compliance with traffic rules (e.g. traffic lights) can be used to understand the skills of the driver (or the strategy of the autopilot algorithms) for assessing the risk);
generating the risk assessment profile for the vehicle(Sellschopp, paragraph 37, the server device is designed to use the respective risk assessment to create a current risk profile for the vehicle), wherein the risk assessment profile comprises data corresponding to the unsafe maneuver ( Sellschopp discloses driving behaviors such as braking behavior and an acceleration behavior having a risk score, which corresponds to unsafe maneuver score. Sellschopp, paragraph 40, In addition, the response time can be based on a new risk or based on current driving situation data 20th and the resulting driving interaction (braking, steering) or the resulting driving behavior, measured and used to calculate a risk score (based on the acceleration sensor) or to calculate a current risk profile 30th be used )and the type of unsafe maneuver(Sellschopp discloses using different sensors to determine risk of different driving behavior which indicates the capability to determine the type of risky driving behavior(unsafe maneuver). Sellschopp, paragraph 40, In addition, the response time can be based on a new risk or based on current driving situation data 20th and the resulting driving interaction (braking, steering) or the resulting driving behavior, measured and used to calculate a risk score (based on the acceleration sensor) or to calculate a current risk profile 30th be used. Sellschopp, paragraph 24, that the sensor device acquires the driving behavior data and the environmental data by means of at least one motion sensor and / or an acceleration sensor and / or a camera and / or a distance sensor and / or a GNSS sensor (GNSS - Global Navigation Satellite System). The large number of sensors advantageously provides redundancy for the acquisition of the driving behavior data and the environmental data. Sellschopp, paragraph 17, the driving behavior data describe a braking behavior and / or a reaction time and / or an acceleration behavior and / or a steering behavior of the vehicle in the respective driving situation and / or a number of lane changes within a predetermined route section and / or time section of the journey), and a timestamp when the unsafe maneuver and the type of unsafe maneuver was performed(Sellschopp discloses recording a date and time a traffic when driving situation is recorded, indicating timestamping when high risk driving behavior is performed. Furthermore, Sellschopp records frequency of occurrence of critical driving situation in a journey, which inherently requires timestamping when the critical driving situation is performed. Sellschopp, paragraph 39, Time, date and location data are currently used as a proxy or as an approximation to determine “traffic risk exposure”. Sellschopp, paragraph 37, determines a frequency of occurrence of the critical driving situations during the journey and based on the determined frequency of occurrence for the vehicle up to the current point in time Drive undertakes a risk assessment. Sellschopp, paragraph 20, The relevant recorded parameters or the recorded driving situation data 20th can be sent to the server or the evaluation unit 14th be sent to risk parameters or the frequency of occurrence 26th critical driving situations 24 to be calculated per time window / km ); and
computing a first threat assessment score based upon the type of unsafe maneuver(Different sensors are used to determine the risk score of different driving behavior. For example, an acceleration sensor is used to calculate the risk score of braking and steering behavior. Sellschopp, paragraph 40, interaction (braking, steering) or the resulting driving behavior, measured and used to calculate a risk score (based on the acceleration sensor));
historical data including a respective timestamp of one or more types of unsafe maneuver(Sellschopp teaches a storing standard driving situation data and creating and maintaining current risk profile for the vehicle, that indicate the storing of vehicle data, which corresponds to historical data. Furthermore, as discussed above, Sellschopp discloses recording a date and time of a traffic when driving situation is recorded, indicating timestamping when high risk driving behavior is performed. Moreover, Sellschopp’s recording of frequency of occurrence of critical driving situation in a journey inherently requires timestamping when critical driving situation is performed. Sellschopp, paragraph 15, standard driving situation data sets for a completely autonomously controlled vehicle, a vehicle controlled by a human driver and / or for any mixing ratios human / autopilot are available or are stored in the database device of the evaluation unit. Sellschopp, paragraph 37, the server device is designed to use the respective risk assessment to create a current risk profile for the vehicle. Sellschopp, paragraph 39, Time, date and location data are currently used as a proxy or as an approximation to determine “traffic risk exposure”. Sellschopp, paragraph 25, determines a frequency of occurrence of the critical driving situations during the journey and based on the determined frequency of occurrence for the vehicle up to the current point in time Drive undertakes a risk assessment. Sellschopp, paragraph 45, The relevant recorded parameters or the recorded driving situation data 20th can be sent to the server or the evaluation unit 14th be sent to risk parameters or the frequency of occurrence 26th critical driving situations 24 to be calculated per time window / km) and the one or more types of unsafe maneuver( Sellschopp, paragraph 40, In addition, the response time can be based on a new risk or based on current driving situation data 20th and the resulting driving interaction (braking, steering) or the resulting driving behavior, measured and used to calculate a risk score (based on the acceleration sensor) or to calculate a current risk profile 30th be used. Sellschopp, paragraph 24, that the sensor device acquires the driving behavior data and the environmental data by means of at least one motion sensor and / or an acceleration sensor and / or a camera and / or a distance sensor and / or a GNSS sensor (GNSS - Global Navigation Satellite System). The large number of sensors advantageously provides redundancy for the acquisition of the driving behavior data and the environmental data. Sellschopp, paragraph 17, the driving behavior data describe a braking behavior and / or a reaction time and / or an acceleration behavior and / or a steering behavior of the vehicle in the respective driving situation and / or a number of lane changes within a predetermined route section and / or time section of the journey).
updating the historical data to further include data corresponding to the type of unsafe maneuver(Sellschopp discloses recognizing change in driving behavior in real-time indicating an updated data of driving behavior. The driving behavior, as discussed above, can be critical driving situation, which is similar to unsafe maneuver. Additionally, Sellschopp determines the type of risk (unsafe maneuver) using sensors such as acceleration sensor to determine whether is there is sudden acceleration. Sellschopp, paragraph 44, Significant changes in the behavior or driving behavior of a vehicle can be detected automatically and almost in real time. Sellschopp, paragraph 45, The relevant recorded parameters or the recorded driving situation data 20th can be sent to the server or the evaluation unit 14th be sent to risk parameters or the frequency of occurrence 26th critical driving situations 24 to be calculated per time window / km) and the timestamp when the type of unsafe maneuver was performed( Sellschopp’s real-time recording of driving behavior indicates updating of timestamp. Sellschopp, paragraph 44, Significant changes in the behavior or driving behavior of a vehicle can be detected automatically and almost in real time);
upon determining that the threat assessment score is at or above a threshold value, performing an action that increase safety of the autonomous vehicle(Sellschopp teaches evaluating driving situation as critical driving situation based on predetermined limits. And based the driving situation, Sellschopp teaches adjustment measures to control the vehicle. Sellschopp, paragraph 10, the evaluation unit evaluates the respective driving situation as a single critical driving situation. Preferably, respective predetermined limits of a normal range (e.g. the said value interval) are set against which the deviation is recognized. Sellschopp, paragraph 35 , The server facility 16 of the system 10 the 1 is preferably set up based on the respective risk assessment 28 or based on the result of the risk assessment 28 a current risk profile 30th for the vehicle and taking into account the current risk profile 30th an adjustment measure 32 trigger. As described above, the adjustment measure 32 comprise an intervention in an engine control of the vehicle. Sellschopp, paragraph 41, Based on the dynamic nature of the data collection, the risk parameter calculation and the risk score calculation, an adjustment measure 32 can be triggered.).
While Sellschopp teaches about generating a risk assessment profile for a driving behavior, it fails to disclose determining whether a risk assessment profile for the vehicle exists in a database, wherein the database is communicatively coupled with the at least one processor; vehicle profile with one or more vehicle identifications of the vehicle.
upon determining the risk assessment profile for the vehicle exists in the database,
retrieving, from the risk assessment profile,
computing a revised threat assessment score based upon updated historical data;
However, Stankoulov, which is in the same analogous art and that teaches about recording, monitoring, and analyzing of a driver behavior discloses determining whether a risk assessment profile for the vehicle exists in a database, (Stankoulov discloses a driver profile comprising various score for different driving behaviors which corresponds to risk assessment. Stankoulov paragraph 108, the process may then determine (at 820) whether or not there is an existing profile. Stankoulov paragraph 75, Each driver profile data element 410 may include, for example, a driver profile ID, driver scores, trip attributes, and/or other sub-elements. Driver scores (or drive style factors) may include, for instance, various scores for different observable driving characteristics. For example, a driver may have different scores for braking, acceleration, safe turning, fuel efficient driving, etc); a vehicle profile with one or more vehicle identifications of the vehicle(Stankoulov paragraph 74, each vehicle profile data element 405 may include a vehicle profile ID, vehicle information, vehicle attributes, type-specific attributes, and/or other sub-elements. Vehicle information may include, for instance, a vehicle's make, model, VIN, etc.)
upon determining the risk assessment profile for the vehicle exists in the database(Stankoulov paragraph 108, If the process determines (at 820) that a profile already exists, the process may then retrieve (at 830) the profile from the database ),
retrieving, from the risk assessment profile and (Stankoulov paragraph 108, the process may then retrieve (at 830) the profile from the database),
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Sellschopp with Stankoulov to determine the presence of existing driving behavior profile, and reevaluating the assessment score. By determining the existence of profile, it is possible to obtain various operating parameters over time so as to provide a historical driver profile record to uncover deviations from suggested driving practices, and reevaluate score based on deviations. Furthermore, it is possible to avoid redundant profile generation, saving memory.
The combination of Sellschopp with Stankoulov specifically fails to disclose computing a revised threat assessment score based upon updated historical data, the revised threat assessment score exponentially reducing the first threat assessment score according to time elapsed since the timestamp when the type of unsafe maneuver was performed.
However, Hahne, which is in the same analogous art and that teaches about detecting a driver’s inability to drive, discloses computing a revised threat assessment score based upon updated historical data (Hahne teaches evaluating a driver’s driving patterns (used to determine the driver’s inability to drive, that corresponds to the threat assessment) for a longer period of time and giving more weight to newer driving pattern, which indicates a historical driving pattern being revised with new driving pattern. Hahne, paragraph 27, The forgetting curve indicates that identified deviations between the driving behavior of another road user and the driver's own vehicle are taken into account over time and are given less weight the further back in time they lie…this advantageously ensures that individual driving patterns that deviate from those of other road users are still taken into account without restriction even after a longer period of time has passed, in order to avoid smaller deviations over longer journeys leading to a large cumulative result and thus incorrectly identifying the driver of the vehicle as unfit to drive, even though this is not actually the case. ), the revised threat assessment score exponentially reducing the first threat assessment score according to time elapsed since the timestamp when the type of unsafe maneuver was performed(Hahne discloses rate deviations between an ego vehicle and other vehicle, where the other vehicle is assumed to be standard driving behavior to compare the ego vehicle’s inability to drive. The deviation rate is similar to the score of unsafe maneuvering because as the deviation rate increases the more it indicates an abnormal driving behavior and the driver’s inability to drive. Moreover, Hahne discloses applying a forgetting curve where rate of the deviation value(score) decreases for older deviation value that than recent value. Furthermore, Hahne discusses the forgetting curve can be applied to decrease rating exponentially as more time is elapsed. Hahne, claim 7, apply a forgetting curve to the result of the summation in order to rate deviations of the driving behavior of at least one other road user from the driving behavior of the own vehicle (1) further back in time as lower for the estimation of the possible inability to drive than more recent deviations. Hahne, paragraph 27, the forgetting curve indicates that identified deviations between the driving behavior of another road user and the driver's own vehicle are taken into account over time and are given less weight the further back in time they lie. Such a forgetting curve is preferably implemented using a method of so-called "exponential forgetting", but can also use a linear decrease or other non-linear functions. ).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Sellschopp and Stankoulov with Hahne to exponentially decrease the rating(score) of an abnormal driving behavior based on the time elapsed since the occurrence of the driving behavior. By decreasing the rating of older deviation (score), it is possible to prevent the system from incorrectly determining a driver’s abnormal driving behavior based small old deviation(score) data that are accumulated over long period. (Hahne, paragraph 27, the forgetting curve indicates that identified deviations between the driving behavior of another road user and the driver's own vehicle are taken into account over time and are given less weight the further back in time they lie….this advantageously ensures that individual driving patterns that deviate from those of other road users are still taken into account without restriction even after a longer period of time has passed, in order to avoid smaller deviations over longer journeys leading to a large cumulative result and thus incorrectly identifying the driver of the vehicle as unfit to drive, even though this is not actually the case).
Regarding claim 4, the combination of Sellschopp, Stankoulov, and Hahne teaches the system of claim 1(Sellschopp, paragraph 44, driving behavior of a vehicle can be detected automatically…[for] an autonomous vehicle; Stankoulov, paragraph 108, the process may then determine (at 820) whether or not there is an existing profile; Hahne, claim 7, apply a forgetting curve to the result of the summation in order to rate deviations of the driving behavior of at least one other road user from the driving behavior of the own vehicle (1) further back in time as lower for the estimation of the possible inability to drive than more recent deviations), wherein the risk assessment profile is indexed by the one or more vehicle identifications of the vehicle(Stankoulov, paragraph 74, Each vehicle profile data element 405 may include a vehicle profile ID, vehicle information, vehicle attributes, type-specific attributes, and/or other sub-elements. Vehicle information may include, for instance, a vehicle's make, model, VIN, etc ).
Regarding claim 5, the combination of Sellschopp, Stankoulov, and Hahne teaches the system of claim 1(Sellschopp, paragraph 44, driving behavior of a vehicle can be detected automatically…[for] an autonomous vehicle; Stankoulov, paragraph 108, the process may then determine (at 820) whether or not there is an existing profile; Hahne, claim 7, apply a forgetting curve to the result of the summation in order to rate deviations of the driving behavior of at least one other road user from the driving behavior of the own vehicle (1) further back in time as lower for the estimation of the possible inability to drive than more recent deviations), wherein the type of unsafe maneuver comprises swerving without causing a lane change, a large amount of acceleration or deceleration(Stankoulov, paragraph 3, sensors such as gyroscopes, accelerometers, wheel speed pulse, etc. which allow various driving behaviors such as speeding and sudden acceleration to be determined), tailgating, changing a lane in a middle of an intersection, changing a lane without a turn signal indication, driving above a posted speed limit by a threshold speed, driving below the posted speed limit by another threshold speed, large speed differentials compared to speed of other vehicles on a road, or a frequent lane changes over a predetermined time duration(Sellschopp, paragraph 40, the number of lane changes or compliance with traffic rules (e.g. traffic lights) can be used to understand the skills of the driver (or the strategy of the autopilot algorithms) for assessing the risk).
Regarding claim 7, the combination of Sellschopp, Stankoulov, and Hahne teaches the system of claim 1(Sellschopp, paragraph 44, driving behavior of a vehicle can be detected automatically…[for] an autonomous vehicle; Stankoulov, paragraph 108, the process may then determine (at 820) whether or not there is an existing profile; Hahne, claim 7, apply a forgetting curve to the result of the summation in order to rate deviations of the driving behavior of at least one other road user from the driving behavior of the own vehicle (1) further back in time as lower for the estimation of the possible inability to drive than more recent deviations), wherein computing the revised threat assessment score based upon updated historical data further comprises computing the revised threat assessment score based on a total number of unsafe maneuvers for each of the one or more types of unsafe maneuver(Sellschopp discloses determining the frequency of each critical driving situations, which corresponds to determining the total number of unsafe maneuvers. Additionally, Sellschopp discusses that risk assessment and score is calculated based on the frequency of occurrence. Furthermore, as discussed above, Sellschopp discloses using different sensors to determine risk of different driving behavior which indicates the capability to determine the type of risky driving behavior(unsafe maneuver). Sellschopp, paragraph 37, determines a frequency of occurrence of the critical driving situations during the journey and based on the determined frequency of occurrence for the vehicle up to the current point in time Drive undertakes a risk assessment. Sellschopp, paragraph 40, In addition, the response time can be based on a new risk or based on current driving situation data 20th and the resulting driving interaction (braking, steering) or the resulting driving behavior, measured and used to calculate a risk score (based on the acceleration sensor) or to calculate a current risk profile 30th be used. Sellschopp, paragraph 24, that the sensor device acquires the driving behavior data and the environmental data by means of at least one motion sensor and / or an acceleration sensor and / or a camera and / or a distance sensor and / or a GNSS sensor (GNSS - Global Navigation Satellite System). The large number of sensors advantageously provides redundancy for the acquisition of the driving behavior data and the environmental data. Sellschopp, paragraph 17, the driving behavior data describe a braking behavior and / or a reaction time and / or an acceleration behavior and / or a steering behavior of the vehicle in the respective driving situation and / or a number of lane changes within a predetermined route section and / or time section of the journey).
Regarding claim 8, Sellschopp teaches a computer-implemented method comprising(Sellschopp, paragraph 26, The processor device of the mobile terminal is preferably designed to carry out the method steps):
determining, based upon sensor data from a plurality of sensors(Sellschopp, paragraph 24, the large number of sensors advantageously provides redundancy for the acquisition of the driving behavior data and the environmental data), an unsafe maneuver and a type of unsafe maneuver of a vehicle(Sellschopp discloses determining different risk assessments for a driving behavior, which corresponds to assessing driving behavior for unsafe maneuvering. For example, it determines the type of risk due to high acceleration using acceleration sensor, and frequent lane change based on different sensor. Sellschopp, paragraph 40, the resulting driving behavior, measured and used to calculate a risk score (based on the acceleration sensor)….the number of lane changes or compliance with traffic rules (e.g. traffic lights) can be used to understand the skills of the driver (or the strategy of the autopilot algorithms) for assessing the risk);
generating the risk assessment profile for the vehicle(Sellschopp, paragraph 37, the server device is designed to use the respective risk assessment to create a current risk profile for the vehicle), wherein the risk assessment profile comprises data corresponding to the unsafe maneuver(Sellschopp discloses driving behaviors such as braking behavior and an acceleration behavior having a risk score, which corresponds to unsafe maneuver score. Sellschopp, paragraph 40, In addition, the response time can be based on a new risk or based on current driving situation data 20th and the resulting driving interaction (braking, steering) or the resulting driving behavior, measured and used to calculate a risk score (based on the acceleration sensor) or to calculate a current risk profile 30th be used) and the type of unsafe maneuver(Sellschopp discloses using different sensors to determine risk of different driving behavior which indicates the capability to determine the type of risky driving behavior(unsafe maneuver). Sellschopp, paragraph 40, In addition, the response time can be based on a new risk or based on current driving situation data 20th and the resulting driving interaction (braking, steering) or the resulting driving behavior, measured and used to calculate a risk score (based on the acceleration sensor) or to calculate a current risk profile 30th be used. Sellschopp, paragraph 24, that the sensor device acquires the driving behavior data and the environmental data by means of at least one motion sensor and / or an acceleration sensor and / or a camera and / or a distance sensor and / or a GNSS sensor (GNSS - Global Navigation Satellite System). The large number of sensors advantageously provides redundancy for the acquisition of the driving behavior data and the environmental data. Sellschopp, paragraph 17, the driving behavior data describe a braking behavior and / or a reaction time and / or an acceleration behavior and / or a steering behavior of the vehicle in the respective driving situation and / or a number of lane changes within a predetermined route section and / or time section of the journey), and a timestamp when the unsafe maneuver and the type of unsafe maneuver was performed(Sellschopp discloses recording a date and time a traffic when driving situation is recorded, indicating timestamping when high risk driving behavior is performed. Furthermore, Sellschopp records frequency of occurrence of critical driving situation in a journey, which inherently requires timestamping when the critical driving situation is performed. Sellschopp, paragraph 39, Time, date and location data are currently used as a proxy or as an approximation to determine “traffic risk exposure”. Sellschopp, paragraph 37, determines a frequency of occurrence of the critical driving situations during the journey and based on the determined frequency of occurrence for the vehicle up to the current point in time Drive undertakes a risk assessment. Sellschopp, paragraph 20, The relevant recorded parameters or the recorded driving situation data 20th can be sent to the server or the evaluation unit 14th be sent to risk parameters or the frequency of occurrence 26th critical driving situations 24 to be calculated per time window / km); and
computing a first threat assessment score based upon the type of unsafe maneuver(Different sensors are used to determine the risk score of different driving behavior. For example, an acceleration sensor is used to calculate the risk score of braking and steering behavior. Sellschopp, paragraph 40, interaction (braking, steering) or the resulting driving behavior, measured and used to calculate a risk score (based on the acceleration sensor)); historical data including a respective timestamp of one or more types of unsafe maneuver(Sellschopp teaches a storing standard driving situation data and creating and maintaining current risk profile for the vehicle, that indicate the storing of vehicle data, which corresponds to historical data. Furthermore, as discussed above, Sellschopp discloses recording a date and time of a traffic when driving situation is recorded, indicating timestamping when high risk driving behavior is performed. Moreover, Sellschopp’s recording of frequency of occurrence of critical driving situation in a journey inherently requires timestamping when critical driving situation is performed. Sellschopp, paragraph 15, standard driving situation data sets for a completely autonomously controlled vehicle, a vehicle controlled by a human driver and / or for any mixing ratios human / autopilot are available or are stored in the database device of the evaluation unit. Sellschopp, paragraph 37, the server device is designed to use the respective risk assessment to create a current risk profile for the vehicle. Sellschopp, paragraph 39, Time, date and location data are currently used as a proxy or as an approximation to determine “traffic risk exposure”. Sellschopp, paragraph 25, determines a frequency of occurrence of the critical driving situations during the journey and based on the determined frequency of occurrence for the vehicle up to the current point in time Drive undertakes a risk assessment. Sellschopp, paragraph 45, The relevant recorded parameters or the recorded driving situation data 20th can be sent to the server or the evaluation unit 14th be sent to risk parameters or the frequency of occurrence 26th critical driving situations 24 to be calculated per time window / km) and the one or more types of unsafe maneuver(Sellschopp, paragraph 40, In addition, the response time can be based on a new risk or based on current driving situation data 20th and the resulting driving interaction (braking, steering) or the resulting driving behavior, measured and used to calculate a risk score (based on the acceleration sensor) or to calculate a current risk profile 30th be used. Sellschopp, paragraph 24, that the sensor device acquires the driving behavior data and the environmental data by means of at least one motion sensor and / or an acceleration sensor and / or a camera and / or a distance sensor and / or a GNSS sensor (GNSS - Global Navigation Satellite System). The large number of sensors advantageously provides redundancy for the acquisition of the driving behavior data and the environmental data. Sellschopp, paragraph 17, the driving behavior data describe a braking behavior and / or a reaction time and / or an acceleration behavior and / or a steering behavior of the vehicle in the respective driving situation and / or a number of lane changes within a predetermined route section and / or time section of the journey);
updating the historical data to further include data corresponding to the type of unsafe maneuver(Sellschopp discloses recognizing change in driving behavior in real-time indicating an updated data of driving behavior. The driving behavior, as discussed above, can be critical driving situation, which is similar to unsafe maneuver. Additionally, Sellschopp determines the type of risk (unsafe maneuver) using sensors such as acceleration sensor to determine whether is there is sudden acceleration. Sellschopp, paragraph 44, Significant changes in the behavior or driving behavior of a vehicle can be detected automatically and almost in real time. Sellschopp, paragraph 45, The relevant recorded parameters or the recorded driving situation data 20th can be sent to the server or the evaluation unit 14th be sent to risk parameters or the frequency of occurrence 26th critical driving situations 24 to be calculated per time window / km) and the timestamp when the type of unsafe maneuver was performed(Sellschopp’s real-time recording of driving behavior indicates updating of timestamp. Sellschopp, paragraph 44, Significant changes in the behavior or driving behavior of a vehicle can be detected automatically and almost in real time);
and upon determining that the revised threat assessment score is at or above a threshold value, performing an action that increase safety of an autonomous vehicle(Sellschopp teaches evaluating driving situation as critical driving situation based on predetermined limits. And based the driving situation, Sellschopp teaches adjustment measures to control the vehicle. Sellschopp, paragraph 10, the evaluation unit evaluates the respective driving situation as a single critical driving situation. Preferably, respective predetermined limits of a normal range (e.g. the said value interval) are set against which the deviation is recognized. Sellschopp, paragraph 35 , The server facility 16 of the system 10 the 1 is preferably set up based on the respective risk assessment 28 or based on the result of the risk assessment 28 a current risk profile 30th for the vehicle and taking into account the current risk profile 30th an adjustment measure 32 trigger. As described above, the adjustment measure 32 comprise an intervention in an engine control of the vehicle. Sellschopp, paragraph 41, Based on the dynamic nature of the data collection, the risk parameter calculation and the risk score calculation, an adjustment measure 32 can be triggered).
While Sellschopp teaches about generating a risk assessment profile for a driving behavior, it fails to disclose determining whether a risk assessment profile for the vehicle exists in a database;
upon determining that the risk assessment profile for the vehicle does not exist in the database, vehicle profile with one or more vehicle identifications of the vehicle,
upon determining the risk assessment profile for the vehicle exists in the database,
retrieving, from the risk assessment profile,
computing a revised threat assessment score based upon updated historical data; the revised threat assessment score exponentially reducing the first threat assessment score according to time elapsed since the timestamp when the type of unsafe maneuver was performed.
However, Stankoulov, which is in the same analogous art and that teaches about recording, monitoring, and analyzing of a driver behavior discloses determining whether a risk assessment profile for the vehicle exists in a database(Stankoulov discloses a driver profile comprising various score for different driving behaviors which corresponds to risk assessment. Stankoulov paragraph 108, the process may then determine (at 820) whether or not there is an existing profile. Stankoulov paragraph 75, Each driver profile data element 410 may include, for example, a driver profile ID, driver scores, trip attributes, and/or other sub-elements. Driver scores (or drive style factors) may include, for instance, various scores for different observable driving characteristics. For example, a driver may have different scores for braking, acceleration, safe turning, fuel efficient driving, etc)); upon determining that the risk assessment profile for the vehicle does not exist in the database( Stankoulov paragraph 108, If the process determines (at 820) that the requested profile does not exist, the process may generate (at 840) a new profile.), vehicle profile with one or more vehicle identifications of the vehicle(Stankoulov paragraph 74, each vehicle profile data element 405 may include a vehicle profile ID, vehicle information, vehicle attributes, type-specific attributes, and/or other sub-elements. Vehicle information may include, for instance, a vehicle's make, model, VIN, etc.)
upon determining the risk assessment profile for the vehicle exists in the database(Stankoulov paragraph 108, If the process determines (at 820) that a profile already exists, the process may then retrieve (at 830) the profile from the database), retrieving, from the risk assessment profile(Stankoulov paragraph 108, the process may then retrieve (at 830) the profile from the database).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Sellschopp with Stankoulov to determine the presence of existing driving behavior profile, and reevaluating the assessment score. By determining the existence of profile, it is possible to obtain various operating parameters over time so as to provide a historical driver profile record to uncover deviations from suggested driving practices, and reevaluate score based on deviations. Furthermore, it is possible to avoid redundant profile generation, saving memory.
The combination of Sellschopp and Stankoulov specifically fails to disclose computing a revised threat assessment score based upon updated historical data, the revised threat assessment score exponentially reducing the first threat assessment score according to time elapsed since the timestamp when the type of unsafe maneuver was performed.
However, Hahne, which is in the same analogous art and that teaches about detecting a driver’s inability to drive, discloses computing a revised threat assessment score based upon updated historical data(Hahne teaches evaluating a driver’s driving patterns (used to determine the driver’s inability to drive, that corresponds to the threat assessment) for a longer period of time and giving more weight to newer driving pattern, which indicates a historical driving pattern being revised with new driving pattern. Hahne, paragraph 27, The forgetting curve indicates that identified deviations between the driving behavior of another road user and the driver's own vehicle are taken into account over time and are given less weight the further back in time they lie…this advantageously ensures that individual driving patterns that deviate from those of other road users are still taken into account without restriction even after a longer period of time has passed, in order to avoid smaller deviations over longer journeys leading to a large cumulative result and thus incorrectly identifying the driver of the vehicle as unfit to drive, even though this is not actually the case.), the revised threat assessment score exponentially reducing the first threat assessment score according to time elapsed since the timestamp when the type of unsafe maneuver was performed(Hahne discloses rate deviations between an ego vehicle and other vehicle, where the other vehicle is assumed to be standard driving behavior to compare the ego vehicle’s inability to drive. The deviation rate is similar to the score of unsafe maneuvering because as the deviation rate increases the more it indicates an abnormal driving behavior and the driver’s inability to drive. Moreover, Hahne discloses applying a forgetting curve where rate of the deviation value(score) decreases for older deviation value that than recent value. Furthermore, Hahne discusses the forgetting curve can be applied to decrease rating exponentially as more time is elapsed. Hahne, claim 7, apply a forgetting curve to the result of the summation in order to rate deviations of the driving behavior of at least one other road user from the driving behavior of the own vehicle (1) further back in time as lower for the estimation of the possible inability to drive than more recent deviations. Hahne, paragraph 27, the forgetting curve indicates that identified deviations between the driving behavior of another road user and the driver's own vehicle are taken into account over time and are given less weight the further back in time they lie. Such a forgetting curve is preferably implemented using a method of so-called "exponential forgetting", but can also use a linear decrease or other non-linear functions.).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Sellschopp and Stankoulov with Hahne to exponentially decrease the rating(score) of an abnormal driving behavior based on the time elapsed since the occurrence of the driving behavior. By decreasing the rating of older deviation (score), it is possible to prevent the system from incorrectly determining a driver’s abnormal driving behavior based small old deviation(score) data that are accumulated over long period. (Hahne, paragraph 27, the forgetting curve indicates that identified deviations between the driving behavior of another road user and the driver's own vehicle are taken into account over time and are given less weight the further back in time they lie….this advantageously ensures that individual driving patterns that deviate from those of other road users are still taken into account without restriction even after a longer period of time has passed, in order to avoid smaller deviations over longer journeys leading to a large cumulative result and thus incorrectly identifying the driver of the vehicle as unfit to drive, even though this is not actually the case).
Regarding claim 11, the combination of Sellschopp, Stankoulov, and Hahne teaches The computer-implemented method of claim 8(Sellschopp, paragraph 44, driving behavior of a vehicle can be detected automatically…[for] an autonomous vehicle; Stankoulov, paragraph 108, the process may then determine (at 820) whether or not there is an existing profile; Hahne, claim 7, apply a forgetting curve to the result of the summation in order to rate deviations of the driving behavior of at least one other road user from the driving behavior of the own vehicle (1) further back in time as lower for the estimation of the possible inability to drive than more recent deviations), wherein the risk assessment profile is indexed by the one or more vehicle identifications of the vehicle(Stankoulov, paragraph 74, Each vehicle profile data element 405 may include a vehicle profile ID, vehicle information, vehicle attributes, type-specific attributes, and/or other sub-elements. Vehicle information may include, for instance, a vehicle's make, model, VIN, etc).
Regarding claim 12, the combination of Sellschopp, Stankoulov, and Hahne teaches the computer-implemented method of claim 8(Sellschopp, paragraph 44, driving behavior of a vehicle can be detected automatically…[for] an autonomous vehicle; Stankoulov, paragraph 108, the process may then determine (at 820) whether or not there is an existing profile; Hahne, claim 7, apply a forgetting curve to the result of the summation in order to rate deviations of the driving behavior of at least one other road user from the driving behavior of the own vehicle (1) further back in time as lower for the estimation of the possible inability to drive than more recent deviations), wherein the type of unsafe maneuver comprises swerving without causing a lane change, a large amount of acceleration or deceleration(Stankoulov, paragraph 3, sensors such as gyroscopes, accelerometers, wheel speed pulse, etc. which allow various driving behaviors such as speeding and sudden acceleration to be determined), tailgating, changing a lane in a middle of an intersection, changing a lane without a turn signal indication, driving above a posted speed limit by a threshold speed, driving below the posted speed limit by another threshold speed, large speed differentials compared to speed of other vehicles on a road, or a frequent lane changes over a predetermined time duration(Sellschopp, paragraph 40, the number of lane changes or compliance with traffic rules (e.g. traffic lights) can be used to understand the skills of the driver (or the strategy of the autopilot algorithms) for assessing the risk).
Regarding claim 14, the combination of Sellschopp, Stankoulov, and Hahne teaches the computer-implemented method of claim 8(Sellschopp, paragraph 44, driving behavior of a vehicle can be detected automatically…[for] an autonomous vehicle; Stankoulov, paragraph 108, the process may then determine (at 820) whether or not there is an existing profile; Hahne, claim 7, apply a forgetting curve to the result of the summation in order to rate deviations of the driving behavior of at least one other road user from the driving behavior of the own vehicle (1) further back in time as lower for the estimation of the possible inability to drive than more recent deviations), wherein computing the revised threat assessment score based upon updated historical data further comprises computing the revised threat assessment score based on a total number of unsafe maneuvers for each of the one or more types of unsafe maneuver(Sellschopp discloses determining the frequency of each critical driving situations, which corresponds to determining the total number of unsafe maneuvers. Additionally, Sellschopp discusses that risk assessment and score is calculated based on the frequency of occurrence. Furthermore, as discussed above, Sellschopp discloses using different sensors to determine risk of different driving behavior which indicates the capability to determine the type of risky driving behavior(unsafe maneuver). Sellschopp, paragraph 37, determines a frequency of occurrence of the critical driving situations during the journey and based on the determined frequency of occurrence for the vehicle up to the current point in time Drive undertakes a risk assessment. Sellschopp, paragraph 40, In addition, the response time can be based on a new risk or based on current driving situation data 20th and the resulting driving interaction (braking, steering) or the resulting driving behavior, measured and used to calculate a risk score (based on the acceleration sensor) or to calculate a current risk profile 30th be used. Sellschopp, paragraph 24, that the sensor device acquires the driving behavior data and the environmental data by means of at least one motion sensor and / or an acceleration sensor and / or a camera and / or a distance sensor and / or a GNSS sensor (GNSS - Global Navigation Satellite System). The large number of sensors advantageously provides redundancy for the acquisition of the driving behavior data and the environmental data. Sellschopp, paragraph 17, the driving behavior data describe a braking behavior and / or a reaction time and / or an acceleration behavior and / or a steering behavior of the vehicle in the respective driving situation and / or a number of lane changes within a predetermined route section and / or time section of the journey).
Regarding claim 15. An autonomous vehicle comprising(Sellschopp, paragraph 44, an autonomous vehicle):
a plurality of sensors(Sellschopp, paragraph 15, driving situation data…transmitted by the sensors);
at least one memory configured to store instructions(Sellschopp, paragraph 10, a program code that is stored or stored on a programmable storage medium); and
at least one processor(Sellschopp, paragraph 10, the evaluation unit described can be based on at least one microprocessor) coupled to the at least one memory and configured to execute the stored instructions to perform operations comprising(Sellschopp, paragraph 10, The evaluation unit can, for example, comprise a program code that is stored or stored on a programmable storage medium and that implements the method described here):
based upon sensor data from the plurality of sensors(Sellschopp, paragraph 24, the large number of sensors advantageously provides redundancy for the acquisition of the driving behavior data and the environmental data), determining an unsafe maneuver and a type of unsafe maneuver of a vehicle(Sellschopp discloses determining different risk assessments for a driving behavior, which corresponds to assessing driving behavior for unsafe maneuvering. For example, it determines the type of risk due to high acceleration using acceleration sensor, and frequent lane change based on different sensor. Sellschopp, paragraph 40, the resulting driving behavior, measured and used to calculate a risk score (based on the acceleration sensor)….the number of lane changes or compliance with traffic rules (e.g. traffic lights) can be used to understand the skills of the driver (or the strategy of the autopilot algorithms) for assessing the risk);
generating the risk assessment profile for the vehicle(Sellschopp, paragraph 37, the server device is designed to use the respective risk assessment to create a current risk profile for the vehicle), wherein the risk assessment profile comprises data corresponding to the unsafe maneuver(Sellschopp discloses driving behaviors such as braking behavior and an acceleration behavior having a risk score, which corresponds to unsafe maneuver score. Sellschopp, paragraph 40, In addition, the response time can be based on a new risk or based on current driving situation data 20th and the resulting driving interaction (braking, steering) or the resulting driving behavior, measured and used to calculate a risk score (based on the acceleration sensor) or to calculate a current risk profile 30th be used) and the type of unsafe maneuver(Sellschopp discloses using different sensors to determine risk of different driving behavior which indicates the capability to determine the type of risky driving behavior(unsafe maneuver). Sellschopp, paragraph 40, In addition, the response time can be based on a new risk or based on current driving situation data 20th and the resulting driving interaction (braking, steering) or the resulting driving behavior, measured and used to calculate a risk score (based on the acceleration sensor) or to calculate a current risk profile 30th be used. Sellschopp, paragraph 24, that the sensor device acquires the driving behavior data and the environmental data by means of at least one motion sensor and / or an acceleration sensor and / or a camera and / or a distance sensor and / or a GNSS sensor (GNSS - Global Navigation Satellite System). The large number of sensors advantageously provides redundancy for the acquisition of the driving behavior data and the environmental data. Sellschopp, paragraph 17, the driving behavior data describe a braking behavior and / or a reaction time and / or an acceleration behavior and / or a steering behavior of the vehicle in the respective driving situation and / or a number of lane changes within a predetermined route section and / or time section of the journey), and a timestamp when the unsafe maneuver and the type of unsafe maneuver was performed(Sellschopp discloses recording a date and time a traffic when driving situation is recorded, indicating timestamping when high risk driving behavior is performed. Furthermore, Sellschopp records frequency of occurrence of critical driving situation in a journey, which inherently requires timestamping when the critical driving situation is performed. Sellschopp, paragraph 39, Time, date and location data are currently used as a proxy or as an approximation to determine “traffic risk exposure”. Sellschopp, paragraph 37, determines a frequency of occurrence of the critical driving situations during the journey and based on the determined frequency of occurrence for the vehicle up to the current point in time Drive undertakes a risk assessment. Sellschopp, paragraph 20, The relevant recorded parameters or the recorded driving situation data 20th can be sent to the server or the evaluation unit 14th be sent to risk parameters or the frequency of occurrence 26th critical driving situations 24 to be calculated per time window / km); and
computing a first threat assessment score based upon the type of unsafe maneuver(Different sensors are used to determine the risk score of different driving behavior. For example, an acceleration sensor is used to calculate the risk score of braking and steering behavior. Sellschopp, paragraph 40, interaction (braking, steering) or the resulting driving behavior, measured and used to calculate a risk score (based on the acceleration sensor));
historical data including a respective timestamp of one or more types of unsafe maneuver(Sellschopp teaches a storing standard driving situation data and creating and maintaining current risk profile for the vehicle, that indicate the storing of vehicle data, which corresponds to historical data. Furthermore, as discussed above, Sellschopp discloses recording a date and time of a traffic when driving situation is recorded, indicating timestamping when high risk driving behavior is performed. Moreover, Sellschopp’s recording of frequency of occurrence of critical driving situation in a journey inherently requires timestamping when critical driving situation is performed. Sellschopp, paragraph 15, standard driving situation data sets for a completely autonomously controlled vehicle, a vehicle controlled by a human driver and / or for any mixing ratios human / autopilot are available or are stored in the database device of the evaluation unit. Sellschopp, paragraph 37, the server device is designed to use the respective risk assessment to create a current risk profile for the vehicle. Sellschopp, paragraph 39, Time, date and location data are currently used as a proxy or as an approximation to determine “traffic risk exposure”. Sellschopp, paragraph 25, determines a frequency of occurrence of the critical driving situations during the journey and based on the determined frequency of occurrence for the vehicle up to the current point in time Drive undertakes a risk assessment. Sellschopp, paragraph 45, The relevant recorded parameters or the recorded driving situation data 20th can be sent to the server or the evaluation unit 14th be sent to risk parameters or the frequency of occurrence 26th critical driving situations 24 to be calculated per time window / km) and the one or more types of unsafe maneuver(Sellschopp, paragraph 40, In addition, the response time can be based on a new risk or based on current driving situation data 20th and the resulting driving interaction (braking, steering) or the resulting driving behavior, measured and used to calculate a risk score (based on the acceleration sensor) or to calculate a current risk profile 30th be used. Sellschopp, paragraph 24, that the sensor device acquires the driving behavior data and the environmental data by means of at least one motion sensor and / or an acceleration sensor and / or a camera and / or a distance sensor and / or a GNSS sensor (GNSS - Global Navigation Satellite System). The large number of sensors advantageously provides redundancy for the acquisition of the driving behavior data and the environmental data. Sellschopp, paragraph 17, the driving behavior data describe a braking behavior and / or a reaction time and / or an acceleration behavior and / or a steering behavior of the vehicle in the respective driving situation and / or a number of lane changes within a predetermined route section and / or time section of the journey);
updating the historical data to further include data corresponding to the type of unsafe maneuver(Sellschopp discloses recognizing change in driving behavior in real-time indicating an updated data of driving behavior. The driving behavior, as discussed above, can be critical driving situation, which is similar to unsafe maneuver. Additionally, Sellschopp determines the type of risk (unsafe maneuver) using sensors such as acceleration sensor to determine whether is there is sudden acceleration. Sellschopp, paragraph 44, Significant changes in the behavior or driving behavior of a vehicle can be detected automatically and almost in real time. Sellschopp, paragraph 45, The relevant recorded parameters or the recorded driving situation data 20th can be sent to the server or the evaluation unit 14th be sent to risk parameters or the frequency of occurrence 26th critical driving situations 24 to be calculated per time window / km) and the timestamp when the type of unsafe maneuver was performed(Sellschopp’s real-time recording of driving behavior indicates updating of timestamp. Sellschopp, paragraph 44, Significant changes in the behavior or driving behavior of a vehicle can be detected automatically and almost in real time); and
upon determining that the revised threat assessment score is at or above a threshold value, performing an action that increase safety of the autonomous vehicle(Sellschopp teaches evaluating driving situation as critical driving situation based on predetermined limits. And based the driving situation, Sellschopp teaches adjustment measures to control the vehicle. Sellschopp, paragraph 10, the evaluation unit evaluates the respective driving situation as a single critical driving situation. Preferably, respective predetermined limits of a normal range (e.g. the said value interval) are set against which the deviation is recognized. Sellschopp, paragraph 35 , The server facility 16 of the system 10 the 1 is preferably set up based on the respective risk assessment 28 or based on the result of the risk assessment 28 a current risk profile 30th for the vehicle and taking into account the current risk profile 30th an adjustment measure 32 trigger. As described above, the adjustment measure 32 comprise an intervention in an engine control of the vehicle. Sellschopp, paragraph 41, Based on the dynamic nature of the data collection, the risk parameter calculation and the risk score calculation, an adjustment measure 32 can be triggered).
While Sellschopp teaches about generating a risk assessment profile for a driving behavior, it fails to disclose determining whether a risk assessment profile for the vehicle exists in a database, wherein the database is communicatively coupled with the at least one processor;
upon determining that the risk assessment profile for the vehicle does not exist in the database,
vehicle profile with one or more vehicle identifications of the vehicle,
upon determining the risk assessment profile for the vehicle exists in the database,
retrieving, from the risk assessment profile,
computing a revised threat assessment score based upon updated historical data, the revised threat assessment score exponentially reducing the first threat assessment score according to time elapsed since the timestamp when the type of unsafe maneuver was performed;
However, Stankoulov, which is in the same analogous art and that teaches about recording, monitoring, and analyzing of a driver behavior discloses determining whether a risk assessment profile for the vehicle exists in a database(Stankoulov discloses a driver profile comprising various score for different driving behaviors which corresponds to risk assessment. Stankoulov paragraph 108, the process may then determine (at 820) whether or not there is an existing profile. Stankoulov paragraph 75, Each driver profile data element 410 may include, for example, a driver profile ID, driver scores, trip attributes, and/or other sub-elements. Driver scores (or drive style factors) may include, for instance, various scores for different observable driving characteristics. For example, a driver may have different scores for braking, acceleration, safe turning, fuel efficient driving, etc)), wherein the database is communicatively coupled with the at least one processor(Stankoulov discloses a database and a processor that processes data in memory and permanent storage device, indicating a communication between the processor and the database. Stankoulov paragraph 168, The processor 2110 may, in order to execute the processes of some embodiments, retrieve instructions to execute and data to process from components such as system memory 2115, ROM 2120, and permanent storage device 214. Stankoulov paragraph 108, If the process determines (at 820) that a profile already exists, the process may then retrieve (at 830) the profile from the database );
upon determining that the risk assessment profile for the vehicle does not exist in the database(Stankoulov paragraph 108, If the process determines (at 820) that the requested profile does not exist, the process may generate (at 840) a new profile),
vehicle profile with one or more vehicle identifications of the vehicle(Stankoulov paragraph 74, each vehicle profile data element 405 may include a vehicle profile ID, vehicle information, vehicle attributes, type-specific attributes, and/or other sub-elements. Vehicle information may include, for instance, a vehicle's make, model, VIN, etc.),
upon determining the risk assessment profile for the vehicle exists in the database(Stankoulov paragraph 108, If the process determines (at 820) that a profile already exists, the process may then retrieve (at 830) the profile from the database),
retrieving, from the risk assessment profile(Stankoulov paragraph 108, the process may then retrieve (at 830) the profile from the database),
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Sellschopp with Stankoulov to determine the presence of existing driving behavior profile, and reevaluating the assessment score. By determining the existence of profile, it is possible to obtain various operating parameters over time so as to provide a historical driver profile record to uncover deviations from suggested driving practices, and reevaluate score based on deviations. Furthermore, it is possible to avoid redundant profile generation, saving memory.
The combination of Sellschopp with Stankoulov specifically fails to disclose computing a revised threat assessment score based upon updated historical data, the revised threat assessment score exponentially reducing the first threat assessment score according to time elapsed since the timestamp when the type of unsafe maneuver was performed;
However, Hahne, which is in the same analogous art and that teaches about detecting a driver’s inability to drive, discloses computing a revised threat assessment score based upon updated historical data(Hahne teaches evaluating a driver’s driving patterns (used to determine the driver’s inability to drive, that corresponds to the threat assessment) for a longer period of time and giving more weight to newer driving pattern, which indicates a historical driving pattern being revised with new driving pattern. Hahne, paragraph 27, The forgetting curve indicates that identified deviations between the driving behavior of another road user and the driver's own vehicle are taken into account over time and are given less weight the further back in time they lie…this advantageously ensures that individual driving patterns that deviate from those of other road users are still taken into account without restriction even after a longer period of time has passed, in order to avoid smaller deviations over longer journeys leading to a large cumulative result and thus incorrectly identifying the driver of the vehicle as unfit to drive, even though this is not actually the case.), the revised threat assessment score exponentially reducing the first threat assessment score according to time elapsed since the timestamp when the type of unsafe maneuver was performed (Hahne discloses rate deviations between an ego vehicle and other vehicle, where the other vehicle is assumed to be standard driving behavior to compare the ego vehicle’s inability to drive. The deviation rate is similar to the score of unsafe maneuvering because as the deviation rate increases the more it indicates an abnormal driving behavior and the driver’s inability to drive. Moreover, Hahne discloses applying a forgetting curve where rate of the deviation value(score) decreases for older deviation value that than recent value. Furthermore, Hahne discusses the forgetting curve can be applied to decrease rating exponentially as more time is elapsed. Hahne, claim 7, apply a forgetting curve to the result of the summation in order to rate deviations of the driving behavior of at least one other road user from the driving behavior of the own vehicle (1) further back in time as lower for the estimation of the possible inability to drive than more recent deviations. Hahne, paragraph 27, the forgetting curve indicates that identified deviations between the driving behavior of another road user and the driver's own vehicle are taken into account over time and are given less weight the further back in time they lie. Such a forgetting curve is preferably implemented using a method of so-called "exponential forgetting", but can also use a linear decrease or other non-linear functions.);
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Sellschopp and Stankoulov with Hahne to exponentially decrease the rating(score) of an abnormal driving behavior based on the time elapsed since the occurrence of the driving behavior. By decreasing the rating of older deviation (score), it is possible to prevent the system from incorrectly determining a driver’s abnormal driving behavior based small old deviation(score) data that are accumulated over long period. (Hahne, paragraph 27, the forgetting curve indicates that identified deviations between the driving behavior of another road user and the driver's own vehicle are taken into account over time and are given less weight the further back in time they lie….this advantageously ensures that individual driving patterns that deviate from those of other road users are still taken into account without restriction even after a longer period of time has passed, in order to avoid smaller deviations over longer journeys leading to a large cumulative result and thus incorrectly identifying the driver of the vehicle as unfit to drive, even though this is not actually the case).
Regarding claim 17, the combination of Sellschopp, Stankoulov, and Hahne teaches the autonomous vehicle of claim 15(Sellschopp, paragraph 44, driving behavior of a vehicle can be detected automatically…[for] an autonomous vehicle; Stankoulov, paragraph 108, the process may then determine (at 820) whether or not there is an existing profile; Hahne, claim 7, apply a forgetting curve to the result of the summation in order to rate deviations of the driving behavior of at least one other road user from the driving behavior of the own vehicle (1) further back in time as lower for the estimation of the possible inability to drive than more recent deviations), wherein the risk assessment profile is indexed by the one or more vehicle identifications of the vehicle(Stankoulov, paragraph 74, Each vehicle profile data element 405 may include a vehicle profile ID, vehicle information, vehicle attributes, type-specific attributes, and/or other sub-elements. Vehicle information may include, for instance, a vehicle's make, model, VIN, etc).
Regarding claim 18, the combination of Sellschopp, Stankoulov, and Hahne teaches the autonomous vehicle of claim 15(Sellschopp, paragraph 44, driving behavior of a vehicle can be detected automatically…[for] an autonomous vehicle; Stankoulov, paragraph 108, the process may then determine (at 820) whether or not there is an existing profile; Hahne, claim 7, apply a forgetting curve to the result of the summation in order to rate deviations of the driving behavior of at least one other road user from the driving behavior of the own vehicle (1) further back in time as lower for the estimation of the possible inability to drive than more recent deviations), wherein the type of unsafe maneuver comprises swerving without causing a lane change, a large amount of acceleration or deceleration(Stankoulov, paragraph 3, sensors such as gyroscopes, accelerometers, wheel speed pulse, etc. which allow various driving behaviors such as speeding and sudden acceleration to be determined), tailgating, changing a lane in a middle of an intersection, changing a lane without a turn signal indication, driving above a posted speed limit by a threshold speed, driving below the posted speed limit by another threshold speed, large speed differentials compared to speed of other vehicles on a road, or a frequent lane changes over a predetermined time duration(Sellschopp, paragraph 40, the number of lane changes or compliance with traffic rules (e.g. traffic lights) can be used to understand the skills of the driver (or the strategy of the autopilot algorithms) for assessing the risk).
Regarding claim 20, the combination of Sellschopp, Stankoulov, and Hahne teaches the autonomous vehicle of claim 15(Sellschopp, paragraph 44, driving behavior of a vehicle can be detected automatically…[for] an autonomous vehicle; Stankoulov, paragraph 108, the process may then determine (at 820) whether or not there is an existing profile; Hahne, claim 7, apply a forgetting curve to the result of the summation in order to rate deviations of the driving behavior of at least one other road user from the driving behavior of the own vehicle (1) further back in time as lower for the estimation of the possible inability to drive than more recent deviations), wherein computing the revised threat assessment score based upon updated historical data further comprises computing the revised threat assessment score based on a total number of unsafe maneuvers for each of the one or more types of unsafe maneuver(Sellschopp discloses determining the frequency of each critical driving situations, which corresponds to determining the total number of unsafe maneuvers. Additionally, Sellschopp discusses that risk assessment and score is calculated based on the frequency of occurrence. Furthermore, as discussed above, Sellschopp discloses using different sensors to determine risk of different driving behavior which indicates the capability to determine the type of risky driving behavior(unsafe maneuver). Sellschopp, paragraph 37, determines a frequency of occurrence of the critical driving situations during the journey and based on the determined frequency of occurrence for the vehicle up to the current point in time Drive undertakes a risk assessment. Sellschopp, paragraph 40, In addition, the response time can be based on a new risk or based on current driving situation data 20th and the resulting driving interaction (braking, steering) or the resulting driving behavior, measured and used to calculate a risk score (based on the acceleration sensor) or to calculate a current risk profile 30th be used. Sellschopp, paragraph 24, that the sensor device acquires the driving behavior data and the environmental data by means of at least one motion sensor and / or an acceleration sensor and / or a camera and / or a distance sensor and / or a GNSS sensor (GNSS - Global Navigation Satellite System). The large number of sensors advantageously provides redundancy for the acquisition of the driving behavior data and the environmental data. Sellschopp, paragraph 17, the driving behavior data describe a braking behavior and / or a reaction time and / or an acceleration behavior and / or a steering behavior of the vehicle in the respective driving situation and / or a number of lane changes within a predetermined route section and / or time section of the journey).
Claims 2, 3, 9, 10, and 16 are rejected under 35 U.S.C. 103(a) as being unpatentable over Sellschopp (DE 102019127974 A1) (hereinafter Sellschopp) in view of Stankoulov (US 20140162219 A1) (hereinafter Stankoulov) in further view of Hahne (DE 102023202878 A1) (hereinafter Hahne) in further view of Simoncini (US 20210354704 A1) (hereinafter Simoncini)..
Regarding claim 2, the combination of Sellschopp, Stankoulov, and Hahne teaches the system of claim 1( Sellschopp, paragraph 44, driving behavior of a vehicle can be detected automatically…[for] an autonomous vehicle; Stankoulov, paragraph 108, the process may then determine (at 820) whether or not there is an existing profile; Hahne, claim 7, apply a forgetting curve to the result of the summation in order to rate deviations of the driving behavior of at least one other road user from the driving behavior of the own vehicle (1) further back in time as lower for the estimation of the possible inability to drive than more recent deviations),
While the combination of Sellschopp, Stankoulov, and Hahne teaches about determining the type of unsafe driving behavior, it specifically fails to disclose a system wherein the determining the unsafe maneuver and the type of unsafe maneuver comprises determining the unsafe maneuver and the type of unsafe maneuver using a neural network-based approach or machine learning techniques.
However, Simoncini, which is in the same analogous art and that teaches about the classification of unsafe maneuvers of a vehicle, discloses a system wherein the determining the unsafe maneuver and the type of unsafe maneuver comprises determining the unsafe maneuver and the type of unsafe maneuver using a neural network-based approach or machine learning techniques(Simoncin discloses using classification model(machine learning ) to determine unsafe maneuvering. Simoncini, paragraph 25, Based at least in part on telematics data, results of processing using the computer vision model, and/or the like, maneuver classification platform 206 may use a vehicle maneuver classification model to determine whether a performed vehicle maneuver is to be classified as an unsafe maneuver. Simoncini, paragraph 37, based on the vehicle maneuver classification model, maneuver classification platform 206 can assign respective weights to one or more features concerning the harsh driving event (e.g., a presence of an object, a speed, an acceleration, a steering angle, and/or the like) to assign the category to the harsh driving event ).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Sellschopp, Stankoulov, and Hahne with Simoncini to determine unsafe maneuvering and the category of unsafe maneuvering based using machine learning model. By using machine learning technique to determine and classify unsafe maneuvering, it is possible to recognize and identify tens, hundreds, thousands, or millions of features and/or feature values for tens, hundreds, thousands, or millions of observations, thereby increasing accuracy and consistency and reducing delay associated with detecting and classifying an unsafe maneuver of a vehicle.
Regarding claim 3, the combination of Sellschopp, Stankoulov, Hahne and Simoncini teaches the system of claim 2(Sellschopp, paragraph 44, driving behavior of a vehicle can be detected automatically…[for] an autonomous vehicle; Stankoulov, paragraph 108, the process may then determine (at 820) whether or not there is an existing profile; Hahne, claim 7, apply a forgetting curve to the result of the summation in order to rate deviations of the driving behavior of at least one other road user from the driving behavior of the own vehicle (1) further back in time as lower for the estimation of the possible inability to drive than more recent deviations; Simoncini, paragraph 25, maneuver classification platform 206 may use a vehicle maneuver classification model to determine whether a performed vehicle maneuver is to be classified as an unsafe maneuver), wherein the neural network-based approach includes using a deep learning neural network or a convolutional neural network(Simoncini, paragraph 62, the machine learning model may employ a different machine learning algorithm…such as….neural network algorithm (e.g., a convolutional neural network algorithm), a deep learning algorithm, and/or the like.).
Regarding claim 9, the combination of Sellschopp, Stankoulov, and Hahne teaches the computer-implemented method of claim 8(Sellschopp, paragraph 44, driving behavior of a vehicle can be detected automatically…[for] an autonomous vehicle; Stankoulov, paragraph 108, the process may then determine (at 820) whether or not there is an existing profile; Hahne, claim 7, apply a forgetting curve to the result of the summation in order to rate deviations of the driving behavior of at least one other road user from the driving behavior of the own vehicle (1) further back in time as lower for the estimation of the possible inability to drive than more recent deviations),
While the combination of Sellschopp, Stankoulov, and Hahne teaches about determining the type of unsafe driving behavior, it specifically fails to disclose a method wherein the determining the unsafe maneuver and the type of unsafe maneuver comprises determining the unsafe maneuver and the type of unsafe maneuver using a neural network-based approach or machine learning techniques.
However, Simoncini, which is in the same analogous art and that teaches about the classification of unsafe maneuvers of a vehicle, discloses a method wherein the determining the unsafe maneuver and the type of unsafe maneuver comprises determining the unsafe maneuver and the type of unsafe maneuver using a neural network-based approach or machine learning techniques(Simoncin discloses using classification model(machine learning ) to determine unsafe maneuvering. Simoncini, paragraph 25, Based at least in part on telematics data, results of processing using the computer vision model, and/or the like, maneuver classification platform 206 may use a vehicle maneuver classification model to determine whether a performed vehicle maneuver is to be classified as an unsafe maneuver. Simoncini, paragraph 37, based on the vehicle maneuver classification model, maneuver classification platform 206 can assign respective weights to one or more features concerning the harsh driving event (e.g., a presence of an object, a speed, an acceleration, a steering angle, and/or the like) to assign the category to the harsh driving event).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Sellschopp, Stankoulov, and Hahne with Simoncini to determine unsafe maneuvering and the category of unsafe maneuvering based using machine learning model. By using machine learning technique to determine and classify unsafe maneuvering, it is possible to recognize and identify tens, hundreds, thousands, or millions of features and/or feature values for tens, hundreds, thousands, or millions of observations, thereby increasing accuracy and consistency and reducing delay associated with detecting and classifying an unsafe maneuver of a vehicle.
Regarding claim 10, the combination of Sellschopp, Stankoulov, Hahne, and Simoncini teaches the computer-implemented method of claim 9(Sellschopp, paragraph 44, driving behavior of a vehicle can be detected automatically…[for] an autonomous vehicle; Stankoulov, paragraph 108, the process may then determine (at 820) whether or not there is an existing profile; Hahne, claim 7, apply a forgetting curve to the result of the summation in order to rate deviations of the driving behavior of at least one other road user from the driving behavior of the own vehicle (1) further back in time as lower for the estimation of the possible inability to drive than more recent deviations; Simoncini, paragraph 25, maneuver classification platform 206 may use a vehicle maneuver classification model to determine whether a performed vehicle maneuver is to be classified as an unsafe maneuver), wherein the neural network-based approach includes using a deep learning neural network or a convolutional neural network(Simoncini, paragraph 62, the machine learning model may employ a different machine learning algorithm…such as….neural network algorithm (e.g., a convolutional neural network algorithm), a deep learning algorithm, and/or the like).
Regarding claim 16, the combination of Sellschopp, Stankoulov, and Hahne teaches the autonomous vehicle of claim 15(Sellschopp, paragraph 44, driving behavior of a vehicle can be detected automatically…[for] an autonomous vehicle; Stankoulov, paragraph 108, the process may then determine (at 820) whether or not there is an existing profile; Hahne, claim 7, apply a forgetting curve to the result of the summation in order to rate deviations of the driving behavior of at least one other road user from the driving behavior of the own vehicle (1) further back in time as lower for the estimation of the possible inability to drive than more recent deviations),
While the combination of Sellschopp, Stankoulov, and Hahne teaches about determining the type of unsafe driving behavior, it specifically fails to disclose a system wherein the determining the unsafe maneuver and the type of unsafe maneuver comprises determining the unsafe maneuver and the type of unsafe maneuver using a neural network-based approach or machine learning techniques, wherein the neural network-based approach includes using a deep learning neural network or a convolutional neural network.
However, Simoncini, which is in the same analogous art and that teaches about the classification of unsafe maneuvers of a vehicle, discloses a system wherein the determining the unsafe maneuver and the type of unsafe maneuver comprises determining the unsafe maneuver and the type of unsafe maneuver using a neural network-based approach or machine learning techniques(Simoncin discloses using classification model(machine learning ) to determine unsafe maneuvering. Simoncini, paragraph 25, Based at least in part on telematics data, results of processing using the computer vision model, and/or the like, maneuver classification platform 206 may use a vehicle maneuver classification model to determine whether a performed vehicle maneuver is to be classified as an unsafe maneuver. Simoncini, paragraph 37, based on the vehicle maneuver classification model, maneuver classification platform 206 can assign respective weights to one or more features concerning the harsh driving event (e.g., a presence of an object, a speed, an acceleration, a steering angle, and/or the like) to assign the category to the harsh driving even), wherein the neural network-based approach includes using a deep learning neural network or a convolutional neural network(Simoncini, paragraph 62, the machine learning model may employ a different machine learning algorithm…such as….neural network algorithm (e.g., a convolutional neural network algorithm), a deep learning algorithm, and/or the like).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Sellschopp, Stankoulov, and Hahne with Simoncini to determine unsafe maneuvering and the category of unsafe maneuvering based using machine learning model. By using machine learning technique to determine and classify unsafe maneuvering, it is possible to recognize and identify tens, hundreds, thousands, or millions of features and/or feature values for tens, hundreds, thousands, or millions of observations, thereby increasing accuracy and consistency and reducing delay associated with detecting and classifying an unsafe maneuver of a vehicle.
Claims 6, 13, and 19 are rejected under 35 U.S.C. 103(a) as being unpatentable over Sellschopp (DE 102019127974 A1) (hereinafter Sellschopp) in view of Stankoulov (US 20140162219 A1) (hereinafter Stankoulov) in further view of Hahne (DE 102023202878 A1) (hereinafter Hahne) in further view of Yamane (US 20210233183 A1) (hereinafter Yamane2)..
Regarding claim 6, the combination of Sellschopp, Stankoulov, and Hahne teaches the system of claim 1(Sellschopp, paragraph 44, driving behavior of a vehicle can be detected automatically…[for] an autonomous vehicle; Stankoulov, paragraph 108, the process may then determine (at 820) whether or not there is an existing profile; Hahne, claim 7, apply a forgetting curve to the result of the summation in order to rate deviations of the driving behavior of at least one other road user from the driving behavior of the own vehicle (1) further back in time as lower for the estimation of the possible inability to drive than more recent deviations), wherein the at least one processor is further configured to:
recompute the revised threat assessment score based upon the further updated historical data(As discussed above, Hahne teaches evaluating a driver’s driving patterns (used to determine the driver’s inability to drive, that corresponds to the threat assessment) for a longer period of time and giving more weight to newer driving pattern, which indicates a historical driving pattern being revised with new driving pattern. Hahne, paragraph 27, The forgetting curve indicates that identified deviations between the driving behavior of another road user and the driver's own vehicle are taken into account over time and are given less weight the further back in time they lie…this advantageously ensures that individual driving patterns that deviate from those of other road users are still taken into account without restriction even after a longer period of time has passed, in order to avoid smaller deviations over longer journeys leading to a large cumulative result and thus incorrectly identifying the driver of the vehicle as unfit to drive, even though this is not actually the case.).
The combination of Sellschopp, Stankoulov, and Hahne specifically fails to disclose a system to further update the historical data to remove or delete data corresponding to the type of unsafe maneuver and the timestamp when the type of unsafe maneuver was performed upon lapse of a predetermined time duration.
However, Yamane2, which is in the same analogous art and that teaches about an information processing device, discloses a system to further update the historical data to remove or delete data corresponding to the type of unsafe maneuver and the timestamp when the type of unsafe maneuver was performed upon lapse of a predetermined time duration and (Yamane2 discloses occurrence time (timestamp) of driving behavior such as sudden braking, sudden starting, which are unsafe maneuvers. Moreover, Yamane2 discloses deleting the driving behavior information from a database after a predetermined time. Yamane2, paragraph 25, the first information that indicates the behavior history of a user during driving of a vehicle is, for example, the history information on predetermined events that occur while the user is driving. The predetermined events include, for example, the detection of the occurrence of a speed measurement cycle, sudden braking, sudden starting, sudden steering, and horn sounding and an event such as a behavior to give way to a pedestrian, a behavior to allow another vehicle to merge into traffic, and the like. In this case, the first information includes, for example, an event that occurs and the occurrence time of the event. Yamane2, paragraph 59, when the occurrence of any of the predetermined events described above is detected, the driving behavior information acquisition unit 21 generates the driving behavior information. The driving behavior information includes, for example, the identification information on the user, identification information on the vehicle 20, type of event, and event occurrence time Yamane2, paragraph 65, the driving behavior information management unit 11 reads, from the driving behavior information DB 13, the driving behavior information on a specified user and for a predetermined period. Furthermore, the driving behavior information management unit 11 may delete the driving behavior information that has been stored, for example, for a predetermined time or longer from the driving behavior information DB 13.).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Sellschopp, Stankoulov, and Hahne with Yamane2 to update the stored data (historical driving behavior) by deleting outdated data after a predetermined time or longer. By removing outdated driving behavior data after predetermined time and replacing it with newer data, it is possible to maintain the freshness of a database. Furthermore, it improves memory optimization which increases the performance of the system.
Regarding claim 13, the combination of Sellschopp, Stankoulov, and Hahne teaches The computer-implemented method of claim 8(Sellschopp, paragraph 44, driving behavior of a vehicle can be detected automatically…[for] an autonomous vehicle; Stankoulov, paragraph 108, the process may then determine (at 820) whether or not there is an existing profile; Hahne, claim 7, apply a forgetting curve to the result of the summation in order to rate deviations of the driving behavior of at least one other road user from the driving behavior of the own vehicle (1) further back in time as lower for the estimation of the possible inability to drive than more recent deviations), further comprising:
recomputing the threat assessment score to revise based upon the updated historical data(As discussed above, Hahne teaches evaluating a driver’s driving patterns (used to determine the driver’s inability to drive, that corresponds to the threat assessment) for a longer period of time and giving more weight to newer driving pattern, which indicates a historical driving pattern being revised with new driving pattern. Hahne, paragraph 27, The forgetting curve indicates that identified deviations between the driving behavior of another road user and the driver's own vehicle are taken into account over time and are given less weight the further back in time they lie…this advantageously ensures that individual driving patterns that deviate from those of other road users are still taken into account without restriction even after a longer period of time has passed, in order to avoid smaller deviations over longer journeys leading to a large cumulative result and thus incorrectly identifying the driver of the vehicle as unfit to drive, even though this is not actually the case.).
The combination of Sellschopp, Stankoulov, and Hahne specifically fails to disclose a method of further updating the historical data to remove or delete data corresponding to the type of unsafe maneuver and the timestamp when the type of unsafe maneuver was performed upon lapse of a predetermined time duration;
However, Yamane2, which is in the same analogous art and that teaches about an information processing device, discloses a method of further updating the historical data to remove or delete data corresponding to the type of unsafe maneuver and the timestamp when the type of unsafe maneuver was performed upon lapse of a predetermined time duration; and(Yamane2 discloses occurrence time (timestamp) of driving behavior such as sudden braking, sudden starting, which are unsafe maneuvers. Moreover, Yamane2 discloses deleting the driving behavior information from a database after a predetermined time. Yamane2, paragraph 25, the first information that indicates the behavior history of a user during driving of a vehicle is, for example, the history information on predetermined events that occur while the user is driving. The predetermined events include, for example, the detection of the occurrence of a speed measurement cycle, sudden braking, sudden starting, sudden steering, and horn sounding and an event such as a behavior to give way to a pedestrian, a behavior to allow another vehicle to merge into traffic, and the like. In this case, the first information includes, for example, an event that occurs and the occurrence time of the event. Yamane2, paragraph 59, when the occurrence of any of the predetermined events described above is detected, the driving behavior information acquisition unit 21 generates the driving behavior information. The driving behavior information includes, for example, the identification information on the user, identification information on the vehicle 20, type of event, and event occurrence time Yamane2, paragraph 65, the driving behavior information management unit 11 reads, from the driving behavior information DB 13, the driving behavior information on a specified user and for a predetermined period. Furthermore, the driving behavior information management unit 11 may delete the driving behavior information that has been stored, for example, for a predetermined time or longer from the driving behavior information DB 13.).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Sellschopp, Stankoulov, and Hahne with Yamane2 to update the stored data (historical driving behavior) by deleting outdated data after a predetermined time or longer. By removing outdated driving behavior data after predetermined time and replacing it with newer data, it is possible to maintain the freshness of a database. Furthermore, it improves memory optimization which increases the performance of the system.
Regarding claim 19, the combination of Sellschopp, Stankoulov, and Hahne teaches the autonomous vehicle of claim 15(Sellschopp, paragraph 44, driving behavior of a vehicle can be detected automatically…[for] an autonomous vehicle; Stankoulov, paragraph 108, the process may then determine (at 820) whether or not there is an existing profile; Hahne, claim 7, apply a forgetting curve to the result of the summation in order to rate deviations of the driving behavior of at least one other road user from the driving behavior of the own vehicle (1) further back in time as lower for the estimation of the possible inability to drive than more recent deviations), wherein the at least one processor is further configured to: recompute the revised threat assessment score to revise based upon the further updated historical data(As discussed above, Hahne teaches evaluating a driver’s driving patterns (used to determine the driver’s inability to drive, that corresponds to the threat assessment) for a longer period of time and giving more weight to newer driving pattern, which indicates a historical driving pattern being revised with new driving pattern. Hahne, paragraph 27, The forgetting curve indicates that identified deviations between the driving behavior of another road user and the driver's own vehicle are taken into account over time and are given less weight the further back in time they lie…this advantageously ensures that individual driving patterns that deviate from those of other road users are still taken into account without restriction even after a longer period of time has passed, in order to avoid smaller deviations over longer journeys leading to a large cumulative result and thus incorrectly identifying the driver of the vehicle as unfit to drive, even though this is not actually the case.).
The combination of Sellschopp, Stankoulov, and Hahne specifically fails to disclose a system to further update the historical data to remove or delete data corresponding to the type of unsafe maneuver and the timestamp when the type of unsafe maneuver was performed upon lapse of a predetermined time duration;
However, Yamane2, which is in the same analogous art and that teaches about an information processing device, discloses a system to further update the historical data to remove or delete data corresponding to the type of unsafe maneuver and the timestamp when the type of unsafe maneuver was performed upon lapse of a predetermined time duration; and(Yamane2 discloses occurrence time (timestamp) of driving behavior such as sudden braking, sudden starting, which are unsafe maneuvers. Moreover, Yamane2 discloses deleting the driving behavior information from a database after a predetermined time. Yamane2, paragraph 25, the first information that indicates the behavior history of a user during driving of a vehicle is, for example, the history information on predetermined events that occur while the user is driving. The predetermined events include, for example, the detection of the occurrence of a speed measurement cycle, sudden braking, sudden starting, sudden steering, and horn sounding and an event such as a behavior to give way to a pedestrian, a behavior to allow another vehicle to merge into traffic, and the like. In this case, the first information includes, for example, an event that occurs and the occurrence time of the event. Yamane2, paragraph 59, when the occurrence of any of the predetermined events described above is detected, the driving behavior information acquisition unit 21 generates the driving behavior information. The driving behavior information includes, for example, the identification information on the user, identification information on the vehicle 20, type of event, and event occurrence time Yamane2, paragraph 65, the driving behavior information management unit 11 reads, from the driving behavior information DB 13, the driving behavior information on a specified user and for a predetermined period. Furthermore, the driving behavior information management unit 11 may delete the driving behavior information that has been stored, for example, for a predetermined time or longer from the driving behavior information DB 13.).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Sellschopp, Stankoulov, and Hahne with Yamane2 to update the stored data (historical driving behavior) by deleting outdated data after a predetermined time or longer. By removing outdated driving behavior data after predetermined time and replacing it with newer data, it is possible to maintain the freshness of a database. Furthermore, it improves memory optimization which increases the performance of the system.
Prior Art of Record
The prior art made of record and not relied upon is considered pertinent to applicant’s
disclosure.
Hao(CN 106157695 A) discloses a system to update the historical data to remove or delete data corresponding to the type of unsafe maneuver and the timestamp when the type of unsafe maneuver was performed upon lapse of a predetermined time duration(Hao, paragraph 92, in order to reduce the network load of the service platform, and improves the subsequent prompt efficiency of driver, in the database only storing driving behavior of the driver closest to the present time in a predetermined duration, in order to convenient for distinguishing, the pre-set time of the called first predetermined time. will not be deleted from the database from the driving behavior of the driver closest to the present time of the first predetermined time period).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BESUFEKAD LEMMA TESSEMA whose telephone number is (571)272-6850. The examiner can normally be reached Monday - Friday 9:00 am - 5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hunter Lonsberry can be reached at 5712727298. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/BESUFEKAD LEMMA TESSEMA/Examiner, Art Unit 3665
/HUNTER B LONSBERRY/Supervisory Patent Examiner, Art Unit 3665
Prosecution Insights