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 claim(s) 05/19/2026 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 any teaching or matter specifically challenged in the argument.
Applicant's arguments filed, 05/19/2026, regarding the 35 U.S.C. 101 rejection set forth by the previous office action have been fully considered and the 35 U.S.C. 101 rejection has been withdrawn accordingly.
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 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.
Claim(s) 1, 4-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Biondo US-8315775-B2, Bush US-10552695-B1, Seaman US-20180257653-A1, and Hall US-11204682-B1 in view of Kuras US-20190176826-A1.
1. Biondo US-8315775-B2 discloses A method for adapting ***operation*** of an advanced driver assistance system (ADAS) equipped vehicle, the method comprising:
(Biondo [c.18 l.50] a cruise control system may determine whether or not to accelerate or decelerate (and if so, a plan for implementing the acceleration or deceleration) toward a target speed based on the geographical location of the motor vehicle and information relating to features of a route which the motor vehicle is following (or potential routes which the motor vehicle may follow).
Bush US-10552695-B1 discloses in a similar invention a consider for “…adapting a response of an advanced driver assistance system (ADAS) equipped vehicle”;
(Bush [c.3 l.30] an alert should be provided to the driver. The predictive distraction distribution can be a graphical or numerical data scheme representing a crash or near crash probability for a given glance location or glance transition based on prior data.)
(Bush [c.23 l.10] Accordingly, after step 1218, the method 1200 will revert back to step 510 of the method 500 to alert the driver if the glance-saliency divergence is greater than the glance-saliency divergence threshold. In such a situation, the driver may be distracted, tired, or non-attentive.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include adapting a response of an advanced driver assistance system (ADAS) equipped vehicle with a reasonable expectation for success, as taught by Bush, for the benefit of storing data related to automated alert(s) and responses during hazardous operations.
collecting telemetry data from a plurality of remote vehicles(Biondo [c.7 l.30] Vehicle to vehicle communication 150; block 214), the telemetry data including crash events and ***transient anomaly*** events relative to locations on a map;
(Biondo [c.6 l.45] navigation data storage 144 also or alternatively may store one or more other types of information, including information relating to features of …, and information relating to transient anomalies transient anomalies and transient anomaly locations (e.g., construction zones, temporary speed limits, incident scenes (e.g., accident scenes, roadblocks, and so on),…)
(Biondo [c.5 l.63] Navigation system interface 141… in response to the requests or otherwise initiated by the external navigation system (e.g., maps, routes, address information, potential destination information, topographical information, information relating to the physical features of a roadway, information relating to traffic control devices and/or posted speed limits, information relating to transient anomalies, and so on). Position/navigation processing subsystem 142 may use some of the received signals in order to convey route information (e.g., via a display) to the driver.)
Bush US-10552695-B1 discloses in a similar invention a consider for “…telemetry data including near-crash events”;
(Bush [c.15 l.13] The baseline includes normal driving given particular glance locations, whereas the crash/near crash includes …, or in a near crash scenario. …, instances of near crash may occur when the driver makes a corrective maneuver to avoid a crash (e.g., swerve or maximum braking). In a particular embodiment, the prior data was obtained or otherwise derived from the Virginia Tech Transportation Institute (VTTI) 100-Car data. Additionally, in this embodiment, the determination of crash/near crash may follow the VTTI standards and statistical analysis parameters for determining crash/near crash and baseline. In some embodiments, the prior data for substep 504.sub.1 may come from the driver of vehicle 12 or other vehicle drivers.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include telemetry data including near-crash events with a reasonable expectation for success, as taught by Bush, for the benefit of storing data related to steering and brake maneuvers during hazardous operations.
collecting environmental data of the locations on the map, the environmental data indicative of inherent characteristics of the locations including road curvature, road grade, road surface, visibility(e.g. snow, fog, hills, elevation, edges), and weather conditions;
([Biondo c.6 l.45] … including information relating to features of proximate topography (e.g., elevations, elevation contours, landforms, locations of hill or mountain summits, locations of inflection points between hills, locations of edges of valleys, locations of edges of plateaus, and so on), physical features of a roadway (changes in curvature, turn radii, grades), … current weather related information (e.g., snow, rain, fog, roadway ice, temperature, and so on)), among other things...)
([Biondo claim.8] relating to features of the route which the motor vehicle is following from a position/navigation system; and wherein the determining the upcoming geographical feature is based on the information relating to the features.)
performing an analysis of the telemetry data relative to the environmental data of the locations to determine crash risk factors(e.g. roadway curvature, traffic patterns, and intersection configurations.) that correlate to an increased crash risk;
([Biondo c.7 l.5] processing and control subsystem 108 is adapted to receive and analyze the geographical location determined by position/navigation processing subsystem 142 and/or some or all of the information stored in navigation data storage 144. During times when the cruise control function is being implemented, processing and control subsystem 108 is further adapted to determine, based on the geographical location and/or the information, whether the motor vehicle is approaching a portion of a road over which the motor vehicle should travel at a slower speed than the target speed, according to various embodiments. )
([Biondo c.13 l.45] a comparison between the geographical location and the features of the route indicates that the motor vehicle is approaching a portion of a road … motor vehicle should travel at a lower speed than the target speed may include, …, or an incident scene (e.g., an accident scene, roadblock, and so on)), a portion of a road having a turn or a curve with a curve radius that is less than a threshold, and a portion of a road having a weather-related anomaly that warrants traveling at a speed that is lower than the target speed (e.g., there is snow, sleet, rain, fog, roadway ice or an excessively cold or hot temperature), among other things. Information used in making this determination may include, for example, information defining physical features of a roadway, speed limit information, locations of traffic control devices, states and timing information relating to traffic control devices, information relating to transient anomalies, and transient anomaly locations.)
determining areas of increased crash risk based on the crash risk factors;
([Biondo c.13 l.45] … a portion of a road having a turn or a curve with a curve radius that is less than a threshold, and a portion of a road having a weather-related anomaly that warrants traveling at a speed that is lower than the target speed (e.g., there is snow, sleet, rain, fog, roadway ice or an excessively cold or hot temperature), among other things. Information used in making this determination may include, for example, information defining physical features of a roadway, speed limit information, locations of traffic control devices, states and timing information relating to traffic control devices, information relating to transient anomalies, and transient anomaly locations.)
determining a location of the ADAS equipped vehicle relative to the map;
([Biondo claim.12] a positioning system interface adapted to receive signals from at least one external infrastructure component; and a processing and control subsystem adapted to determine a route with which the motor vehicle is traveling, to determine a geographical location along the route of the motor vehicle based on the signals, to determine an upcoming geographical feature based on the geographical location along the route of the motor vehicle)
([Biondo c.6 l.15] …adapted to analyze the signals received by positioning system interface 140 in order to determine a geographical location of the motor vehicle)
collecting vehicle data from one or more sensors mounted on the ADAS equipped vehicle, the vehicle data including vehicle ***data***, ****, tire pressure, ****, and ***on-board sensor data***;
([Biondo c.8 l.10] on-board sensors 126 may include one or more sensors selected from a group that includes …, a tire pressure sensor 174.)
Seaman US-20180257653-A1 discloses in a similar invention a consideration for collecting vehicle data including “…vehicle load[0050], tire wear[0028-30], tire temperature”;
(Seaman [0050] … CTU controller 108 can provide a notification of a reduction of a load capacity of the CTU 100. The notification can be sent to the vehicle controller 120 (or to a driver of the vehicle 104), to cause the vehicle 104 (either the vehicle controller 120 or a driver) to not accept cargo loading of the CTU 100 from exceeding a specified weight. In the CTU 100 without a compromised component, the CTU 100 may accept cargo loading up to a first specified weight. Once a component having a compromised condition is detected, then the CTU 100 may accept cargo loading up to a second specified weight that is less than the first specified weight...)
(Seaman [0028-30] A sensor for detecting tire wear can monitor an amount of tread left on the tire. The tire wear sensor can be an optical sensor that can detect reflected light (reflected by the tire in response to light emitted by a light source on the CTU 100 or ambient light) to determine a depth of the tire tread… to prevent the likelihood of a blowout of the tire.)
(Seaman [0031] the heat due to friction of the worn bearings can cause a tire blowout if the temperature of the wheel becomes too high. The wheel sensor can include a temperature sensor to detect a temperature of the wheel. If the temperature of the wheel exceeds a specified temperature threshold, then the wheel sensor can output an indication to the CTU controller 108, which can cause a corrective action to be taken.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include vehicle data including vehicle load, tire wear, and tire temperature with a reasonable expectation for success, as taught by Seaman, for the benefit of storing data related stress upon vehicle capacities (e.g. load weight) and condition of tire effectiveness to prevent the likelihood of a blowout of the tire.
Hall US-11204682-B1 discloses in a similar invention a consideration for collecting vehicle data including “…trailering status(e.g. whether or not the car is operable and thereby able to trailer any load associated with the vehicle), spare tire mounted”;
(Hall [c.9 l.30] the data objective may relate to or comprise, for example and without limitation: …; incident detail(s) such as whether the requested service resulted from an accident or stalled while driving; vehicle state data such as whether the car is operable, where the car is, whether the car has a flat tire, whether a spare is available, ...)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include vehicle data including trailering status and spare tire mounted with a reasonable expectation for success, as taught by Hall, for the benefit of storing data related vehicle capacities and resources used in addressing hazardous event operations (e.g. operability, repair(s), resources, tires, weight/load).
calculating an increased risk of vehicle crash index (IRVCI) based on the ADAS equipped vehicle’s location relative to the areas of increased crash risk and the vehicle data; and
([Biondo c.12 l.55] block 216, a determination is made (e.g., by the processing and control subsystem) whether the vehicle should accelerate (i.e., when the actual speed is less than the target speed) or decelerate (i.e., when the actual speed is greater than the target speed). According to an embodiment, this determination is made based on the geographical location of the motor vehicle and some or all of the information relating to features of the route which the motor vehicle is following (or potentially may follow). More particularly, with regard to determining whether or not the motor vehicle should accelerate, a determination is made whether the motor vehicle is approaching a feature of the route that may cause the motor vehicle to accelerate due to the force of gravity or whether a feature of the route is present that warrants a slower speed than the target speed. With regard to determining whether or not the motor vehicle should decelerate, a determination is made whether the motor vehicle is approaching a feature of the route that may cause the motor vehicle to decelerate due to the force of gravity or whether a feature of the route is present that warrants avoiding a deceleration maneuver.)
adapting the response of the ADAS equipped vehicle **** based on the IRVCI, ****.
([Biondo c.12 l.55] block 216, a determination is made (e.g., by the processing and control subsystem) whether the vehicle should accelerate (i.e., when the actual speed is less than the target speed) or decelerate (i.e., when the actual speed is greater than the target speed). According to an embodiment, this determination is made based on the geographical location of the motor vehicle and some or all of the information relating to features of the route which the motor vehicle is following)
([Biondo c.12 l.55] More particularly, with regard to determining whether or not the motor vehicle should accelerate, a determination is made whether the motor vehicle is approaching a feature of the route that may cause the motor vehicle to accelerate due to the force of gravity or whether a feature of the route is present that warrants a slower speed than the target speed. With regard to determining whether or not the motor vehicle should decelerate, a determination is made whether the motor vehicle is approaching a feature of the route that may cause the motor vehicle to decelerate due to the force of gravity or whether a feature of the route is present that warrants avoiding a deceleration maneuver.))
Kuras US-20190176826-A1 discloses in a similar invention field of endeavor, a consideration for propulsion control systems with varying aggressiveness of response configured to control a response of a vehicle “…by at least adapting a response aggressiveness of a plurality of actuator devices of the ADAS equipped vehicle [[based on data]], wherein the plurality of actuator devices are associated with at least one of a propulsion system, a transmission system, a steering system, and a brake system of the vehicle, and wherein adapting the response aggressiveness includes adjusting an intensity of at least one actuator-level control parameter of the plurality of actuator devices by which the plurality of actuator devices apply the response of the ADAS equipped vehicle.”;
(Kuras [0050] Propulsion system controls 24 may, however, impose a limit on adjustment of the continuously variable transmission 28 and/or power source 18 in response to operator requests for acceleration, deceleration, and directional shifts. By imposing such a limit, propulsion system controls 24 may avoid excessive levels of acceleration and jerk in certain operational situations, and select higher levels of jerk/acceleration/deceleration limits in other operational situations, such as an aggressive braking situation, a directional shift situation, and a blade load shedding (BLS) situation. Propulsion system controls 24 may control jerk levels as discussed above by implementing output control commands to adjust torque output from power source 18 and/or CVT 28 in a manner that controls the acceleration and/or jerk of mobile machine 10 differently in some circumstances than in other circumstances. By doing so, propulsion system controls 24 may allow relatively high levels of acceleration and jerk in circumstances where the operator expects and desires aggressive response from propulsion control system 12, while limiting acceleration and jerk to lower values in circumstances where the operator does not expect or desire such aggressive operation.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include at least adapting a response aggressiveness of a plurality of actuator devices of the ADAS equipped vehicle based on data, wherein the plurality of actuator devices are associated with at least one of a propulsion system, a transmission system, a steering system, and a brake system of the vehicle, and wherein adapting the response aggressiveness includes adjusting an intensity of at least one actuator-level control parameter of the plurality of actuator devices by which the plurality of actuator devices apply the response of the ADAS equipped vehicle with a reasonable expectation for success, as taught by Kuras, for the benefit of controlling user comfort, vehicle jerk, and desired aggressive responses according to operational/user conditions.
4. Biondo discloses The method of claim 1, further comprising classifying the telemetry data as ***anomaly/incident events***.
([Biondo c.13 l.53] are not limited to …, a portion of the road at which a transient anomaly is present (e.g., a construction zone, a temporary speed limit that is lower than the target speed, or an incident scene (e.g., an accident scene, roadblock, and so on)), a portion of a road having a turn or a curve with a curve radius that is less than a threshold, and a portion of a road having a weather-related anomaly that warrants traveling at a speed that is lower than the target speed (e.g., there is snow, sleet, rain, fog, roadway ice or an excessively cold or hot temperature), among other things. Information used in making this determination may include, for example, information defining physical features of a roadway, speed limit information, locations of traffic control devices, states and timing information relating to traffic control devices, information relating to transient anomalies, and transient anomaly locations.)
Bush US-10552695-B1 discloses in a similar invention a consider for classifying the telemetry data “…as the near-crash events when the remote vehicles activate alerts that are indicative of a near crash”;
(Bush [c.3 l.30] an alert should be provided to the driver. The predictive distraction distribution can be a graphical or numerical data scheme representing a crash or near crash probability for a given glance location or glance transition based on prior data.)
(Bush [c.23 l.10] Accordingly, after step 1218, the method 1200 will revert back to step 510 of the method 500 to alert the driver if the glance-saliency divergence is greater than the glance-saliency divergence threshold. In such a situation, the driver may be distracted, tired, or non-attentive.)
(Bush [c.15 l.13] The baseline includes normal driving given particular glance locations, whereas the crash/near crash includes …, or in a near crash scenario. …, instances of near crash may occur when the driver makes a corrective maneuver to avoid a crash (e.g., swerve or maximum braking). In a particular embodiment, the prior data was obtained or otherwise derived from the Virginia Tech Transportation Institute (VTTI) 100-Car data. Additionally, in this embodiment, the determination of crash/near crash may follow the VTTI standards and statistical analysis parameters for determining crash/near crash and baseline. In some embodiments, the prior data for substep 504.sub.1 may come from the driver of vehicle 12 or other vehicle drivers.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include classify telemetry data as the near-crash events when the remote vehicles activate alerts that are indicative of a near crash with a reasonable expectation for success, as taught by Bush, for the benefit of storing data related to steering and brake maneuvers during hazardous operations.
5. Biondo discloses The method of claim 1, further comprising classifying the telemetry data as ***anomaly/incident events***
([Biondo c.13 l.53] are not limited to …, a portion of the road at which a transient anomaly is present (e.g., a construction zone, a temporary speed limit that is lower than the target speed, or an incident scene (e.g., an accident scene, roadblock, and so on)), a portion of a road having a turn or a curve with a curve radius that is less than a threshold, and a portion of a road having a weather-related anomaly that warrants traveling at a speed that is lower than the target speed (e.g., there is snow, sleet, rain, fog, roadway ice or an excessively cold or hot temperature), among other things. Information used in making this determination may include, for example, information defining physical features of a roadway, speed limit information, locations of traffic control devices, states and timing information relating to traffic control devices, information relating to transient anomalies, and transient anomaly locations.)
Bush US-10552695-B1 discloses in a similar invention a consider for classifying the telemetry data “…the near crash events when the remote vehicles activate automatic vehicle responses(e.g. automated alerts in response to hazardous conditions) that are indicative of a near crash”;
(Bush [c.23 l.10] Accordingly, after step 1218, the method 1200 will revert back to step 510 of the method 500 to alert the driver if the glance-saliency divergence is greater than the glance-saliency divergence threshold. In such a situation, the driver may be distracted, tired, or non-attentive.)
(Bush [c.15 l.13] The baseline includes normal driving given particular glance locations, whereas the crash/near crash includes …, or in a near crash scenario. …, instances of near crash may occur when the driver makes a corrective maneuver to avoid a crash (e.g., swerve or maximum braking). In a particular embodiment, the prior data was obtained or otherwise derived from the Virginia Tech Transportation Institute (VTTI) 100-Car data. Additionally, in this embodiment, the determination of crash/near crash may follow the VTTI standards and statistical analysis parameters for determining crash/near crash and baseline. In some embodiments, the prior data for substep 504.sub.1 may come from the driver of vehicle 12 or other vehicle drivers.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include classify telemetry data as the near crash events when the remote vehicles activate automatic vehicle responses that are indicative of a near crash with a reasonable expectation for success, as taught by Bush, for the benefit of storing data related to automated alert(s) and responses during hazardous operations.
6. Biondo discloses The method of claim 1, wherein the crash risk factors include roadway curvature(e.g. curvature, turn radii, grades), traffic patterns(timing information), and intersection configurations(states and timing information relating to traffic control devices).
([Biondo c.6 l.45] navigation data storage 144 also or alternatively may store one or more other types of information, including information relating to features of proximate topography (e.g., elevations, elevation contours, landforms, locations of hill or mountain summits, locations of inflection points between hills, locations of edges of valleys, locations of edges of plateaus, and so on), physical features of a roadway (changes in curvature, turn radii, grades), locations of traffic control devices (including stop lights, stop signs, and posted speed limits), states and timing information relating to traffic control devices (e.g., traffic light states (e.g., red, yellow, green states) and timing information (e.g., projected times for state changes)), and information relating to transient anomalies transient anomalies and transient anomaly locations (e.g., construction zones, temporary speed limits, incident scenes (e.g., accident scenes, roadblocks, and so on), current weather related information (e.g., snow, rain, fog, roadway ice, temperature, and so on)), among other things. Some or all of this information alternatively may be stored elsewhere (e.g., in the RAM of data storage 114 or in an external location). According to an embodiment, navigation data storage 144 also may be adapted to store computer-readable instructions (e.g., program code) associated with the functions performed by position/navigation processing subsystem 142.)
7. Biondo discloses The method of claim 1, further comprising locating the crash risk factors relative to the map.
([Biondo c.6 l.45] navigation data storage 144 also or alternatively may store one or more other types of information, including information relating to features of …, and information relating to transient anomalies transient anomalies and transient anomaly locations (e.g., construction zones, temporary speed limits, incident scenes (e.g., accident scenes, roadblocks, and so on),…)
([Biondo c.5 l.63] Navigation system interface 141… in response to the requests or otherwise initiated by the external navigation system (e.g., maps, routes, address information, potential destination information, topographical information, information relating to the physical features of a roadway, information relating to traffic control devices and/or posted speed limits, information relating to transient anomalies, and so on). Position/navigation processing subsystem 142 may use some of the received signals in order to convey route information (e.g., via a display) to the driver.)
8. Biondo discloses The method of claim 1, further comprising adapting the ADAS equipped vehicle’s ***automatic operations***.
([Biondo c.12 l.55] block 216, a determination is made (e.g., by the processing and control subsystem) whether the vehicle should accelerate (i.e., when the actual speed is less than the target speed) or decelerate (i.e., when the actual speed is greater than the target speed). According to an embodiment, this determination is made based on the geographical location of the motor vehicle and some or all of the information relating to features of the route which the motor vehicle is following)
Bush US-10552695-B1 discloses in a similar invention a consider for adapting the ADAS equipped vehicle’s “…response timing(the timing of the alert response configured to be adapted/controlled according to collected data)”;
(Bush [c.3 l.30] an alert should be provided to the driver. The predictive distraction distribution can be a graphical or numerical data scheme representing a crash or near crash probability for a given glance location or glance transition based on prior data.)
(Bush [c.23 l.10] Accordingly, after step 1218, the method 1200 will revert back to step 510 of the method 500 to alert the driver if the glance-saliency divergence is greater than the glance-saliency divergence threshold. In such a situation, the driver may be distracted, tired, or non-attentive.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include adapting the ADAS equipped vehicle’s response timing with a reasonable expectation for success, as taught by Bush, for the benefit of storing data related to automated alert(s) and responses during hazardous operations.
Claim(s) 2-3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Biondo US-8315775-B2, Bush US-10552695-B1, Seaman US-20180257653-A1, Hall US-11204682-B1, and Kuras US-20190176826-A1 as applied to claim 1 above and further in view of GILBERT-EYRES US-20220383420-A1.
2. Biondo discloses The method of claim 1, further comprising collecting the telemetry data from a ***external system***.
([Biondo c.7 l.20] one or more interfaces adapted to communicate wirelessly with a system that is external to the motor vehicle (other than a navigation system or positioning system). According to various embodiments, wireless interfaces 124 may include one or more interfaces selected from a group that includes a vehicle-to-vehicle communication system interface 150 and a vehicle-to-infrastructure communication system interface 152.)
GILBERT-EYRES US-20220383420-A1 discloses in a similar invention a consideration for collecting telemetry data from a “…cloud”;
(GILBERT-EYRES [0056; 0062] After the crash event, the vehicle transmits vehicle data, health data and/or diagnostic data to the remote server one or more times… The module may include one or more interface circuits. In some examples, the interface circuits may include wired or wireless interfaces that are connected to a local area network (LAN), the Internet, a wide area network (WAN), or combinations thereof. ... … server (also known as remote, or cloud) module may accomplish some functionality…)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include the use of a cloud server with a reasonable expectation for success, as taught by GILBERT-EYRES, for the benefit of storing data in a remote external server accessible through wireless communication.
3. Biondo discloses The method of claim 1, further comprising classifying the telemetry data as ***anomaly/incident events***.
([Biondo c.13 l.53] are not limited to …, a portion of the road at which a transient anomaly is present (e.g., a construction zone, a temporary speed limit that is lower than the target speed, or an incident scene (e.g., an accident scene, roadblock, and so on)), a portion of a road having a turn or a curve with a curve radius that is less than a threshold, and a portion of a road having a weather-related anomaly that warrants traveling at a speed that is lower than the target speed (e.g., there is snow, sleet, rain, fog, roadway ice or an excessively cold or hot temperature), among other things. Information used in making this determination may include, for example, information defining physical features of a roadway, speed limit information, locations of traffic control devices, states and timing information relating to traffic control devices, information relating to transient anomalies, and transient anomaly locations.)
GILBERT-EYRES US-20220383420-A1 discloses in a similar invention a consideration for classifying the telemetry data as “…the crash events when remote vehicles collide”;
(GILBERT-EYRES [0056; 0062] After the crash event, the vehicle transmits vehicle data, health data and/or diagnostic data to the remote server one or more times…)
(GILBERT-EYRES [0033] The vehicle 100 includes a controller 106 storing vehicle data, diagnostic codes and/or other information that is generated during normal operation. After a crash event has occurred, additional vehicle data is collected and automatically transmitted to a remote server.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include classifying telemetry data the crash event with a reasonable expectation for success, as taught by GILBERT-EYRES, for the benefit of storing data related to crash incidents for analyses by associated operators (e.g. owner, manufacturer, insurance agency, repair facility).
Claim(s) 10, 13-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Biondo US-8315775-B2, Bush US-10552695-B1, Rose US-20220032921-A1, Seaman US-20180257653-A1, Hall US-11204682-B1, and Kuras US-20190176826-A1 in view of Xi US-20170297564-A1.
10. Biondo US-8315775-B2 discloses A method for adapting ***operation*** of an advanced driver assistance system (ADAS) equipped vehicle, the method comprising:
([Biondo c.18 l.50] a cruise control system may determine whether or not to accelerate or decelerate (and if so, a plan for implementing the acceleration or deceleration) toward a target speed based on the geographical location of the motor vehicle and information relating to features of a route which the motor vehicle is following (or potential routes which the motor vehicle may follow).
Bush US-10552695-B1 discloses in a similar invention a consider for “…adapting a response of an advanced driver assistance system (ADAS) equipped vehicle”;
(Bush [c.3 l.30] an alert should be provided to the driver. The predictive distraction distribution can be a graphical or numerical data scheme representing a crash or near crash probability for a given glance location or glance transition based on prior data.)
(Bush [c.23 l.10] Accordingly, after step 1218, the method 1200 will revert back to step 510 of the method 500 to alert the driver if the glance-saliency divergence is greater than the glance-saliency divergence threshold. In such a situation, the driver may be distracted, tired, or non-attentive.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include adapting a response of an advanced driver assistance system (ADAS) equipped vehicle with a reasonable expectation for success, as taught by Bush, for the benefit of storing data related to automated alert(s) and responses during hazardous operations.
****creating a driver profile, wherein the driver profile is based on collected driving habits and collected driving preferences, wherein the driving habits of the driver include vehicle speed, vehicle acceleration, vehicle deceleration, and steering inputs over a period of time;****
Rose US-20220032921-A1 discloses in a similar invention field of endeavor, a consideration for “… creating a driver profile[0054], wherein the driver profile is based on collected driving habits([0024]) and collected driving preferences(a driver behavior module 320, a behavior characterization module 330, and a risk assessment module 340), wherein the driving habits of the driver include vehicle speed, vehicle acceleration, vehicle deceleration(low speed driving, high speed driving, and speed profiles), and steering inputs(lane changes, drifting across lane markers, drifting onto the shoulder, and the like) over a period of time”;
(Rose [0024] driver habits, such as following distance (or tailgating), lane changes, sudden accelerations, sudden deceleration,)
(Rose [0052-53] … The driver behavior module 320 of the VCS 40 receives the captured sensor data and may identify one or more driver behaviors 321-323. By way of example, the driver behavior module 320 may analyze the sensor data to identify driver behavior 321, which includes, for example, sharp turns, sudden braking, and/or sudden acceleration. The driver behavior module 320 also may analyze the sensor data to identify driver behavior 322, which includes, for example, low speed driving, high speed driving, and speed profiles. Finally, the driver behavior module 320 may analyze the sensor data to identify driver behavior 323, which includes, for example, tailgating, lane changes, drifting across lane markers, drifting onto the shoulder, and the like.)
(Rose [0054] risk assessment module 340 of the VCS 40 may use the microscopic behavior category 331, the macroscopic behavior category 332, and the historical behavior record category 333 to determine a driver profile that may include a risk assessment for the driver of the vehicle system 10.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include creating a driver profile, wherein the driver profile is based on collected driving habits and collected driving preferences, wherein the driving habits of the driver include vehicle speed, vehicle acceleration, vehicle deceleration, and steering inputs over a period of time with a reasonable expectation for success, as taught by Rose, for the benefit of storing data related vehicle operator habits used in addressing operational conditions to include hazardous event(s).
****calculating a driver’s historical aggressiveness metric (D-HAM) by comparing the driving habits to a statistical mean;****
Rose US-20220032921-A1 discloses in a similar invention field of endeavor, a consideration for “… calculating a driver’s historical aggressiveness metric(a risk assessment for the driver) (D-HAM) by comparing the driving habits to a statistical mean([0052-53] e.g. a statistical frequency, severity, pattern)”;
Examiner’s Interpretation: See paragraph [0052] of the present application for an interpretation of a statistical means, included below for reference;
“At step 210, the driver profile 48 is used to calculate a driver’s historical aggressiveness metric (D-HAM). The D-HAM is calculated by comparing the driver profile 48 to a statistical mean. The statistical mean is pre-programmed in the ADAS 12 and stored in the controller 20. The statistical mean identifies average driving patterns for the driver 16 based on the driver profile 48. The method 200 proceeds to step 212.”
(Rose [0054; 0060-0061] The risk assessment module 340 of the VCS 40 may use the microscopic behavior category 331, the macroscopic behavior category 332, and the historical behavior record category 333 to determine a driver profile that may include a risk assessment for the driver of the vehicle system 10. … may characterize each of the selected risk-exposing behaviors using an RFS principle in which: i) a Recency value (V.sub.R) indicates how recently a driver exhibited a particular behavior; ii) a Frequency value (V.sub.F) indicates how often a driver exhibits this behavior feature; and iii) a Severity value (V.sub.M) indicates how severe the behavior is.)
(Rose [0052-53] The behavior characterization module 330 of the VCS 40 may then analyze the identified driver behaviors 321-323 …. a historical behavior record category 333 associated with the driver. The microscopic behavior category 331 may include speeding, reckless driving, distracted driving, and the like. … periods during which a driver engages in more dangerous driving and/or roads where the driver engages in more dangerous driving. The historical behavior record category 333 classifies the driver behaviors according to patterns, occurrences, frequencies, and the like.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include calculating a driver’s historical aggressiveness metric (D-HAM) by comparing the driving habits to a statistical mean with a reasonable expectation for success, as taught by Rose, for the benefit of storing data related vehicle operator habits used in addressing operational conditions to include hazardous event(s) and further configured to store a metric against which measured inputs are compared in order to identify abnormal operational conditions.
collecting vehicle data from one or more sensors mounted on the ADAS equipped vehicle, the vehicle data including vehicle ***data***, ****, tire pressure, ****, and ***on-board sensor data***;
([Biondo c.8 l.10] on-board sensors 126 may include one or more sensors selected from a group that includes …, a tire pressure sensor 174.)
Seaman US-20180257653-A1 discloses in a similar invention a consideration for collecting vehicle data including “…vehicle load[0050], tire wear[0028-30], tire temperature”;
(Seaman [0050] … CTU controller 108 can provide a notification of a reduction of a load capacity of the CTU 100. The notification can be sent to the vehicle controller 120 (or to a driver of the vehicle 104), to cause the vehicle 104 (either the vehicle controller 120 or a driver) to not accept cargo loading of the CTU 100 from exceeding a specified weight. In the CTU 100 without a compromised component, the CTU 100 may accept cargo loading up to a first specified weight. Once a component having a compromised condition is detected, then the CTU 100 may accept cargo loading up to a second specified weight that is less than the first specified weight...)
(Seaman [0028-30] A sensor for detecting tire wear can monitor an amount of tread left on the tire. The tire wear sensor can be an optical sensor that can detect reflected light (reflected by the tire in response to light emitted by a light source on the CTU 100 or ambient light) to determine a depth of the tire tread… to prevent the likelihood of a blowout of the tire.)
(Seaman [0031] the heat due to friction of the worn bearings can cause a tire blowout if the temperature of the wheel becomes too high. The wheel sensor can include a temperature sensor to detect a temperature of the wheel. If the temperature of the wheel exceeds a specified temperature threshold, then the wheel sensor can output an indication to the CTU controller 108, which can cause a corrective action to be taken.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include vehicle data including vehicle load, tire wear, and tire temperature with a reasonable expectation for success, as taught by Seaman, for the benefit of storing data related stress upon vehicle capacities (e.g. load weight) and condition of tire effectiveness to prevent the likelihood of a blowout of the tire.
Hall US-11204682-B1 discloses in a similar invention a consideration for collecting vehicle data including “…trailering status(e.g. whether or not the car is operable and thereby able to trailer any load associated with the vehicle), spare tire mounted”;
([Biondo c.9 l.30] the data objective may relate to or comprise, for example and without limitation: …; incident detail(s) such as whether the requested service resulted from an accident or stalled while driving; vehicle state data such as whether the car is operable, where the car is, whether the car has a flat tire, whether a spare is available, ...)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include vehicle data including trailering status and spare tire mounted with a reasonable expectation for success, as taught by Hall, for the benefit of storing data related vehicle capacities and resources used in addressing hazardous event operations (e.g. operability, repair(s), resources, tires, weight/load).
collecting environmental data of locations on a map, the environmental data indicative of inherent characteristics of the locations including road curvature, road grade, road surface, visibility, and weather conditions;
([Biondo c.6 l.45] … including information relating to features of proximate topography (e.g., elevations, elevation contours, landforms, locations of hill or mountain summits, locations of inflection points between hills, locations of edges of valleys, locations of edges of plateaus, and so on), physical features of a roadway (changes in curvature, turn radii, grades), … current weather related information (e.g., snow, rain, fog, roadway ice, temperature, and so on)), among other things...)
([Biondo claim.8] relating to features of the route which the motor vehicle is following from a position/navigation system; and wherein the determining the upcoming geographical feature is based on the information relating to the features.)
collecting real-time vehicle inputs from the driver including speed, ***vehicle data***, and steering inputs relative to the map;
([Biondo c.7 l.40] position/navigation processing subsystem 142 relating to the motor vehicle's geographical location, current speed, target speed, direction of motion, and so on...)
([Biondo c.9 l.12] Steering wheel sensor 172 includes a device adapted to measure the rotation angle of the motor vehicle's steering wheel, and to provide information defining the detected angle to the processing and control system 108.)
([Biondo c.6 l.15] …adapted to analyze the signals received by positioning system interface 140 in order to determine a geographical location of the motor vehicle)
Rose US-20220032921-A1 discloses in a similar invention field of endeavor, a consideration for “…collecting real-time[0060] vehicle inputs from the driver including …, acceleration, deceleration, …”;
(Rose [0060] In the vehicle system 10, the individual sensors in the sensors module 188 continually detect N events in real time… an accelerometer may detect a sudden deceleration or acceleration)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include collecting real-time vehicle inputs from the driver including acceleration and deceleration in real time with a reasonable expectation for success, as taught by Rose, for the benefit of storing data related vehicle operator habits used in addressing operational conditions to include hazardous event(s).
performing an analysis of the vehicle data and the environmental data to determine crash risk factors ***based on input data***;
([Biondo c.13 l.45] … a portion of a road having a turn or a curve with a curve radius that is less than a threshold, and a portion of a road having a weather-related anomaly that warrants traveling at a speed that is lower than the target speed (e.g., there is snow, sleet, rain, fog, roadway ice or an excessively cold or hot temperature), among other things. Information used in making this determination may include, for example, information defining physical features of a roadway, speed limit information, locations of traffic control devices, states and timing information relating to traffic control devices, information relating to transient anomalies, and transient anomaly locations.)
Rose US-20220032921-A1 discloses in a similar invention field of endeavor, a consideration for “…performing an analysis of the vehicle data … when the vehicle inputs deviate from the D-HAM”;
(Rose [claim.2] wherein the driver monitoring system is configured to compare the at least one identified driver behavior to a threshold value and, in response to the comparison, to transmit to the driver a warning message.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include performing an analysis of the vehicle data when the vehicle inputs deviate from thresholds with a reasonable expectation for success, as taught by Rose, for the benefit of storing a metric against which measured inputs are compared in order to identify abnormal operational conditions.
calculating ***vehicle operations*** from crash risk factors based on *** input data***; and
([Biondo c.12 l.55] More particularly, with regard to determining whether or not the motor vehicle should accelerate, a determination is made whether the motor vehicle is approaching a feature of the route that may cause the motor vehicle to accelerate due to the force of gravity or whether a feature of the route is present that warrants a slower speed than the target speed. With regard to determining whether or not the motor vehicle should decelerate, a determination is made whether the motor vehicle is approaching a feature of the route that may cause the motor vehicle to decelerate due to the force of gravity or whether a feature of the route is present that warrants avoiding a deceleration maneuver.))
Xi US-20170297564-A1 discloses in a similar invention field of endeavor, a consideration for “…calculating a driver’s predicted aggressiveness metric (D-PAM) … based on deviation of the D-HAM”;
(Xi [0039] The input information (visual information, tactile information and vehicle state parameters etc.) is processed via the driver model for identifying and predicting the behavior of the driver, and then the vehicle control system parameters are tuned and modified in real time according to the driver's expected value output from the driver model, allowing to make the vehicle performance be changed with the driver characteristics and make the vehicle performance meet the driver's driving behavior characteristic more simultaneously, meeting the driver's driving demands, reducing the driver workload, and avoiding traffic accidents.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include calculating a driver’s predicted behavior based on deviation from historical behavior with a reasonable expectation for success, as taught by Xi, for the benefit of meeting the driver's driving demands, reducing the driver workload, and avoiding traffic accidents.
adapting the response of the ADAS equipped vehicle **** based on the [[data]], ****.
([Biondo c.12 l.55] block 216, a determination is made (e.g., by the processing and control subsystem) whether the vehicle should accelerate (i.e., when the actual speed is less than the target speed) or decelerate (i.e., when the actual speed is greater than the target speed). According to an embodiment, this determination is made based on the geographical location of the motor vehicle and some or all of the information relating to features of the route which the motor vehicle is following)
([Biondo c.12 l.55] More particularly, with regard to determining whether or not the motor vehicle should accelerate, a determination is made whether the motor vehicle is approaching a feature of the route that may cause the motor vehicle to accelerate due to the force of gravity or whether a feature of the route is present that warrants a slower speed than the target speed. With regard to determining whether or not the motor vehicle should decelerate, a determination is made whether the motor vehicle is approaching a feature of the route that may cause the motor vehicle to decelerate due to the force of gravity or whether a feature of the route is present that warrants avoiding a deceleration maneuver.))
Kuras US-20190176826-A1 discloses in a similar invention field of endeavor, a consideration for propulsion control systems with varying aggressiveness of response configured to control a response of a vehicle “…by at least adapting a response aggressiveness of a plurality of actuator devices of the ADAS equipped vehicle [[based on data]], wherein the plurality of actuator devices are associated with at least one of a propulsion system, a transmission system, a steering system, and a brake system of the vehicle, and wherein adapting the response aggressiveness includes adjusting an intensity of at least one actuator-level control parameter of the plurality of actuator devices by which the plurality of actuator devices apply the response of the ADAS equipped vehicle.”;
(Kuras [0050] Propulsion system controls 24 may, however, impose a limit on adjustment of the continuously variable transmission 28 and/or power source 18 in response to operator requests for acceleration, deceleration, and directional shifts. By imposing such a limit, propulsion system controls 24 may avoid excessive levels of acceleration and jerk in certain operational situations, and select higher levels of jerk/acceleration/deceleration limits in other operational situations, such as an aggressive braking situation, a directional shift situation, and a blade load shedding (BLS) situation. Propulsion system controls 24 may control jerk levels as discussed above by implementing output control commands to adjust torque output from power source 18 and/or CVT 28 in a manner that controls the acceleration and/or jerk of mobile machine 10 differently in some circumstances than in other circumstances. By doing so, propulsion system controls 24 may allow relatively high levels of acceleration and jerk in circumstances where the operator expects and desires aggressive response from propulsion control system 12, while limiting acceleration and jerk to lower values in circumstances where the operator does not expect or desire such aggressive operation.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include at least adapting a response aggressiveness of a plurality of actuator devices of the ADAS equipped vehicle based on data, wherein the plurality of actuator devices are associated with at least one of a propulsion system, a transmission system, a steering system, and a brake system of the vehicle, and wherein adapting the response aggressiveness includes adjusting an intensity of at least one actuator-level control parameter of the plurality of actuator devices by which the plurality of actuator devices apply the response of the ADAS equipped vehicle with a reasonable expectation for success, as taught by Kuras, for the benefit of controlling user comfort, vehicle jerk, and desired aggressive responses according to operational/user conditions.
Xi US-20170297564-A1 discloses in a similar invention field of endeavor, a consideration for “…adapting the response of the … vehicle based on the D-PAM”;
(Xi [0039] The input information (visual information, tactile information and vehicle state parameters etc.) is processed via the driver model for identifying and predicting the behavior of the driver, and then the vehicle control system parameters are tuned and modified in real time according to the driver's expected value output from the driver model, allowing to make the vehicle performance be changed with the driver characteristics and make the vehicle performance meet the driver's driving behavior characteristic more simultaneously, meeting the driver's driving demands, reducing the driver workload, and avoiding traffic accidents.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include adapting the response of the vehicle based driver behavior with a reasonable expectation for success, as taught by Xi, for the benefit of meeting the driver's driving demands, reducing the driver workload, and avoiding traffic accidents.
13. Biondo discloses The method of claim 10, wherein calculating ***operational data***.
([Biondo c.12 l.55] block 216, a determination is made (e.g., by the processing and control subsystem) whether the vehicle should accelerate (i.e., when the actual speed is less than the target speed) or decelerate (i.e., when the actual speed is greater than the target speed). According to an embodiment, this determination is made based on the geographical location of the motor vehicle and some or all of the information relating to features of the route which the motor vehicle is following (or potentially may follow).)
Rose US-20220032921-A1 discloses in a similar invention field of endeavor, a consideration for “… the D-HAM includes comparing the driver’s speed to the statistical mean”;
Examiner’s Interpretation: See paragraph [0052] of the present application for an interpretation of a statistical means, included below for reference;
“At step 210, the driver profile 48 is used to calculate a driver’s historical aggressiveness metric (D-HAM). The D-HAM is calculated by comparing the driver profile 48 to a statistical mean. The statistical mean is pre-programmed in the ADAS 12 and stored in the controller 20. The statistical mean identifies average driving patterns for the driver 16 based on the driver profile 48. The method 200 proceeds to step 212.”
(Rose [0054; 0060-0061] The risk assessment module 340 of the VCS 40 may use the microscopic behavior category 331, the macroscopic behavior category 332, and the historical behavior record category 333 to determine a driver profile that may include a risk assessment for the driver of the vehicle system 10. … may characterize each of the selected risk-exposing behaviors using an RFS principle in which: i) a Recency value (V.sub.R) indicates how recently a driver exhibited a particular behavior; ii) a Frequency value (V.sub.F) indicates how often a driver exhibits this behavior feature; and iii) a Severity value (V.sub.M) indicates how severe the behavior is.)
(Rose [0052-53] The behavior characterization module 330 of the VCS 40 may then analyze the identified driver behaviors 321-323 …. a historical behavior record category 333 associated with the driver. The microscopic behavior category 331 may include speeding, reckless driving, distracted driving, and the like. … periods during which a driver engages in more dangerous driving and/or roads where the driver engages in more dangerous driving. The historical behavior record category 333 classifies the driver behaviors according to patterns, occurrences, frequencies, and the like.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include calculating a value for comparing the driver’s speed to a statistical mean with a reasonable expectation for success, as taught by Rose, for the benefit of storing data related vehicle operator habits used in addressing operational conditions to include hazardous event(s) and further configured to store a metric against which measured inputs are compared in order to identify abnormal operational conditions.
14. Biondo discloses The method of claim 10, wherein calculating ***operational data***.
([Biondo c.12 l.55] block 216, a determination is made (e.g., by the processing and control subsystem) whether the vehicle should accelerate (i.e., when the actual speed is less than the target speed) or decelerate (i.e., when the actual speed is greater than the target speed). According to an embodiment, this determination is made based on the geographical location of the motor vehicle and some or all of the information relating to features of the route which the motor vehicle is following (or potentially may follow).)
Rose US-20220032921-A1 discloses in a similar invention field of endeavor, a consideration for “… calculating the D-HAM includes comparing the driver’s acceleration to the statistical mean”;
Examiner’s Interpretation: See paragraph [0052] of the present application for an interpretation of a statistical means, included below for reference;
“At step 210, the driver profile 48 is used to calculate a driver’s historical aggressiveness metric (D-HAM). The D-HAM is calculated by comparing the driver profile 48 to a statistical mean. The statistical mean is pre-programmed in the ADAS 12 and stored in the controller 20. The statistical mean identifies average driving patterns for the driver 16 based on the driver profile 48. The method 200 proceeds to step 212.”
(Rose [0047] The accelerometers may detect sudden accelerations, sudden decelerations (i.e., braking), and sudden or exaggerated lateral movements, indicating swerving or sharp turns.)
(Rose [0054; 0060-0061] The risk assessment module 340 of the VCS 40 may use the microscopic behavior category 331, the macroscopic behavior category 332, and the historical behavior record category 333 to determine a driver profile that may include a risk assessment for the driver of the vehicle system 10. … may characterize each of the selected risk-exposing behaviors using an RFS principle in which: i) a Recency value (V.sub.R) indicates how recently a driver exhibited a particular behavior; ii) a Frequency value (V.sub.F) indicates how often a driver exhibits this behavior feature; and iii) a Severity value (V.sub.M) indicates how severe the behavior is.)
(Rose [0052-53] The behavior characterization module 330 of the VCS 40 may then analyze the identified driver behaviors 321-323 …. a historical behavior record category 333 associated with the driver. The microscopic behavior category 331 may include speeding, reckless driving, distracted driving, and the like. … periods during which a driver engages in more dangerous driving and/or roads where the driver engages in more dangerous driving. The historical behavior record category 333 classifies the driver behaviors according to patterns, occurrences, frequencies, and the like.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include calculating a value for comparing the driver’s acceleration to the statistical mean with a reasonable expectation for success, as taught by Rose, for the benefit of storing data related vehicle operator habits used in addressing operational conditions to include hazardous event(s) and further configured to store a metric against which measured inputs are compared in order to identify abnormal operational conditions.
15. Biondo discloses The method of claim 10, wherein calculating ***operational data***.
([Biondo c.12 l.55] block 216, a determination is made (e.g., by the processing and control subsystem) whether the vehicle should accelerate (i.e., when the actual speed is less than the target speed) or decelerate (i.e., when the actual speed is greater than the target speed). According to an embodiment, this determination is made based on the geographical location of the motor vehicle and some or all of the information relating to features of the route which the motor vehicle is following (or potentially may follow).)
Rose US-20220032921-A1 discloses in a similar invention field of endeavor, a consideration for “… includes comparing the driver’s deceleration to the statistical mean”;
Examiner’s Interpretation: See paragraph [0052] of the present application for an interpretation of a statistical means, included below for reference;
“At step 210, the driver profile 48 is used to calculate a driver’s historical aggressiveness metric (D-HAM). The D-HAM is calculated by comparing the driver profile 48 to a statistical mean. The statistical mean is pre-programmed in the ADAS 12 and stored in the controller 20. The statistical mean identifies average driving patterns for the driver 16 based on the driver profile 48. The method 200 proceeds to step 212.”
(Rose [0047] The accelerometers may detect sudden accelerations, sudden decelerations (i.e., braking), and sudden or exaggerated lateral movements, indicating swerving or sharp turns.)
(Rose [0054; 0060-0061] The risk assessment module 340 of the VCS 40 may use the microscopic behavior category 331, the macroscopic behavior category 332, and the historical behavior record category 333 to determine a driver profile that may include a risk assessment for the driver of the vehicle system 10. … may characterize each of the selected risk-exposing behaviors using an RFS principle in which: i) a Recency value (V.sub.R) indicates how recently a driver exhibited a particular behavior; ii) a Frequency value (V.sub.F) indicates how often a driver exhibits this behavior feature; and iii) a Severity value (V.sub.M) indicates how severe the behavior is.)
(Rose [0052-53] The behavior characterization module 330 of the VCS 40 may then analyze the identified driver behaviors 321-323 …. a historical behavior record category 333 associated with the driver. The microscopic behavior category 331 may include speeding, reckless driving, distracted driving, and the like. … periods during which a driver engages in more dangerous driving and/or roads where the driver engages in more dangerous driving. The historical behavior record category 333 classifies the driver behaviors according to patterns, occurrences, frequencies, and the like.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include calculating a value for comparing the driver’s deceleration to a statistical mean with a reasonable expectation for success, as taught by Rose, for the benefit of storing data related vehicle operator habits used in addressing operational conditions to include hazardous event(s) and further configured to store a metric against which measured inputs are compared in order to identify abnormal operational conditions.
16. Biondo discloses The method of claim 10, wherein calculating ***operational data***.
([Biondo c.12 l.55] block 216, a determination is made (e.g., by the processing and control subsystem) whether the vehicle should accelerate (i.e., when the actual speed is less than the target speed) or decelerate (i.e., when the actual speed is greater than the target speed). According to an embodiment, this determination is made based on the geographical location of the motor vehicle and some or all of the information relating to features of the route which the motor vehicle is following (or potentially may follow).)
Rose US-20220032921-A1 discloses in a similar invention field of endeavor, a consideration for “…calculating the D-HAM includes comparing the driver’s steering inputs to the statistical mean”;
Examiner’s Interpretation: See paragraph [0052] of the present application for an interpretation of a statistical means, included below for reference;
“At step 210, the driver profile 48 is used to calculate a driver’s historical aggressiveness metric (D-HAM). The D-HAM is calculated by comparing the driver profile 48 to a statistical mean. The statistical mean is pre-programmed in the ADAS 12 and stored in the controller 20. The statistical mean identifies average driving patterns for the driver 16 based on the driver profile 48. The method 200 proceeds to step 212.”
(Rose [0047] The accelerometers may detect sudden accelerations, sudden decelerations (i.e., braking), and sudden or exaggerated lateral movements, indicating swerving or sharp turns.)
(Rose [0054; 0060-0061] The risk assessment module 340 of the VCS 40 may use the microscopic behavior category 331, the macroscopic behavior category 332, and the historical behavior record category 333 to determine a driver profile that may include a risk assessment for the driver of the vehicle system 10. … may characterize each of the selected risk-exposing behaviors using an RFS principle in which: i) a Recency value (V.sub.R) indicates how recently a driver exhibited a particular behavior; ii) a Frequency value (V.sub.F) indicates how often a driver exhibits this behavior feature; and iii) a Severity value (V.sub.M) indicates how severe the behavior is.)
(Rose [0052-53] The behavior characterization module 330 of the VCS 40 may then analyze the identified driver behaviors 321-323 …. a historical behavior record category 333 associated with the driver. The microscopic behavior category 331 may include speeding, reckless driving, distracted driving, and the like. … periods during which a driver engages in more dangerous driving and/or roads where the driver engages in more dangerous driving. The historical behavior record category 333 classifies the driver behaviors according to patterns, occurrences, frequencies, and the like.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include comparing the driver’s steering inputs to a statistical mean with a reasonable expectation for success, as taught by Rose, for the benefit of storing data related vehicle operator habits used in addressing operational conditions to include hazardous event(s) and further configured to store a metric against which measured inputs are compared in order to identify abnormal operational conditions.
17. Biondo discloses The method of claim 10, wherein detecting ***deviation in*** vehicle inputs ***within a range***.
([Biondo c.10 l.58] a determination is made (e.g., by the processing and control subsystem) whether a difference between the target speed and the actual speed exceeds a threshold (i.e., whether the absolute value of (target speed-actual speed)>threshold).
Rose US-20220032921-A1 discloses in a similar invention field of endeavor, a consideration for “…detecting deviation of the D-HAM occurs when the vehicle inputs fall outside a particular range from the D-HAM”;
(Rose [0011] the driver monitoring system, in response to a determination that at least one identified driver behavior exceeds the threshold value, transmits to an owner of the vehicle system a message informing the owner that the driver has exceed the threshold value.)
(Rose [0052-53] The behavior characterization module 330 of the VCS 40 may then analyze the identified driver behaviors 321-323 …. a historical behavior record category 333 associated with the driver. The microscopic behavior category 331 may include speeding, reckless driving, distracted driving, and the like. … periods during which a driver engages in more dangerous driving and/or roads where the driver engages in more dangerous driving. The historical behavior record category 333 classifies the driver behaviors according to patterns, occurrences, frequencies, and the like.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include detecting deviation of behavior when a vehicle inputs fall outside a particular range predetermined thresholds with a reasonable expectation for success, as taught by Rose, for the benefit of storing data related vehicle operator habits used in addressing operational conditions to include hazardous event(s) and further configured to store a metric against which measured inputs are compared in order to identify abnormal operational conditions.
18. Biondo discloses The method of claim 10, further comprising adapting the ADAS equipped vehicle’s ***automatic operations***.
([Biondo c.12 l.55] block 216, a determination is made (e.g., by the processing and control subsystem) whether the vehicle should accelerate (i.e., when the actual speed is less than the target speed) or decelerate (i.e., when the actual speed is greater than the target speed). According to an embodiment, this determination is made based on the geographical location of the motor vehicle and some or all of the information relating to features of the route which the motor vehicle is following)
Bush US-10552695-B1 discloses in a similar invention a consider for adapting the ADAS equipped vehicle’s “…response timing(the timing of the alert response configured to be adapted/controlled according to collected data)”;
(Bush [c.3 l.30] an alert should be provided to the driver. The predictive distraction distribution can be a graphical or numerical data scheme representing a crash or near crash probability for a given glance location or glance transition based on prior data.)
(Bush [c.23 l.10] Accordingly, after step 1218, the method 1200 will revert back to step 510 of the method 500 to alert the driver if the glance-saliency divergence is greater than the glance-saliency divergence threshold. In such a situation, the driver may be distracted, tired, or non-attentive.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include adapting the ADAS equipped vehicle’s response timing with a reasonable expectation for success, as taught by Bush, for the benefit of storing data related to automated alert(s) and responses during hazardous operations.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Biondo US-8315775-B2, Bush US-10552695-B1, Rose US-20220032921-A1, Seaman US-20180257653-A1, Hall US-11204682-B1, Kuras US-20190176826-A1, and Xi US-20170297564-A1, as applied to claim 10 above and further in view of Hayes US-20180059687-A1.
11. Biondo discloses The method of claim 10, wherein **** the driver has input into the ADAS.
(user interface subsystem 110)
Hayes US-20180059687-A1 discloses in a similar invention field of endeavor, a consideration for wherein “…the driving preferences include settings the driver has input into the ADAS.”;
(Hayes [0098] The application may allow a user to enter one or more preferences, which may be accessed through one or more parts of the application (e.g., by pressing a button, such as button 741). A user preference input may allow a user to create or maintain one or more driver profiles. The preferences may allow for an association to be created between one or more vehicles and one or more drivers. The preferences may allow for inputting information regarding a vehicle and/or driver associated with a fleet.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include driving preferences which include settings with a reasonable expectation for success, as taught by Hayes, for the benefit of meeting the driver's driving demands, reducing the driver workload, and increasing comfortability.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Biondo US-8315775-B2, Bush US-10552695-B1, Rose US-20220032921-A1, Seaman US-20180257653-A1, Hall US-11204682-B1, Kuras US-20190176826-A1, and Xi US-20170297564-A1, as applied to claim 10 above and further in view of Tzirkel-Hancock US-20170217445-A1.
12. Biondo discloses The method of claim 10, further comprising collecting ***input*** data from a plurality of ***on-board*** sensors located ***on*** the ADAS equipped vehicle.
Tzirkel-Hancock US-20170217445-A1 discloses in a similar invention field of endeavor, a consideration for “…collecting biometric data from a plurality of cabin sensors located within.”;
(Tzirkel-Hancock [0082] Other example sensor sub-systems 60 include the mentioned cabin sensors (60.sub.1, 60.sub.2, etc.) configured and arranged (e.g., positioned and fitted in the vehicle) to sense activity, people, cabin environmental conditions, or other features relating to the interior of the vehicle. Example cabin sensors (60.sub.1, 60.sub.2, etc.) include microphones, in-vehicle visual-light cameras, seat-weight sensors, passenger salinity, retina or other passenger characteristics, biometrics, or physiological measures, and/or the environment about the vehicle 10.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include collecting biometric data from a plurality of cabin sensors located within with a reasonable expectation for success, as taught by Tzirkel-Hancock, for the benefit of monitoring alertness and health of an operator of a vehicle.
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Biondo US-8315775-B2, Bush US-10552695-B1, Seaman US-20180257653-A1, Hall US-11204682-B1, Rose US-20220032921-A1, and Kuras US-20190176826-A1 in view of Xi US-20170297564-A1.
20. Biondo US-8315775-B2 discloses A method for adapting ***operation*** of an advanced driver assistance system (ADAS) equipped vehicle, the method comprising:
([Biondo c.18 l.50] a cruise control system may determine whether or not to accelerate or decelerate (and if so, a plan for implementing the acceleration or deceleration) toward a target speed based on the geographical location of the motor vehicle and information relating to features of a route which the motor vehicle is following (or potential routes which the motor vehicle may follow).
Bush US-10552695-B1 discloses in a similar invention a consider for “…adapting a response of an advanced driver assistance system (ADAS) equipped vehicle”;
(Bush [c.3 l.30] an alert should be provided to the driver. The predictive distraction distribution can be a graphical or numerical data scheme representing a crash or near crash probability for a given glance location or glance transition based on prior data.)
(Bush [c.23 l.10] Accordingly, after step 1218, the method 1200 will revert back to step 510 of the method 500 to alert the driver if the glance-saliency divergence is greater than the glance-saliency divergence threshold. In such a situation, the driver may be distracted, tired, or non-attentive.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include adapting a response of an advanced driver assistance system (ADAS) equipped vehicle with a reasonable expectation for success, as taught by Bush, for the benefit of storing data related to automated alert(s) and responses during hazardous operations.
collecting telemetry data from a plurality of remote vehicles([c.7 l.30] Vehicle to vehicle communication 150; block 214), the telemetry data including crash events and ***transient anomaly*** events relative to locations on a map;
([Biondo c.6 l.45] navigation data storage 144 also or alternatively may store one or more other types of information, including information relating to features of …, and information relating to transient anomalies transient anomalies and transient anomaly locations (e.g., construction zones, temporary speed limits, incident scenes (e.g., accident scenes, roadblocks, and so on),…)
([Biondo c.5 l.63] Navigation system interface 141… in response to the requests or otherwise initiated by the external navigation system (e.g., maps, routes, address information, potential destination information, topographical information, information relating to the physical features of a roadway, information relating to traffic control devices and/or posted speed limits, information relating to transient anomalies, and so on). Position/navigation processing subsystem 142 may use some of the received signals in order to convey route information (e.g., via a display) to the driver.)
Bush US-10552695-B1 discloses in a similar invention a consider for “…telemetry data including near-crash events”;
(Bush [c.15 l.13] The baseline includes normal driving given particular glance locations, whereas the crash/near crash includes …, or in a near crash scenario. …, instances of near crash may occur when the driver makes a corrective maneuver to avoid a crash (e.g., swerve or maximum braking). In a particular embodiment, the prior data was obtained or otherwise derived from the Virginia Tech Transportation Institute (VTTI) 100-Car data. Additionally, in this embodiment, the determination of crash/near crash may follow the VTTI standards and statistical analysis parameters for determining crash/near crash and baseline. In some embodiments, the prior data for substep 504.sub.1 may come from the driver of vehicle 12 or other vehicle drivers.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include telemetry data including near-crash events with a reasonable expectation for success, as taught by Bush, for the benefit of storing data related to steering and brake maneuvers during hazardous operations.
collecting environmental data of the locations on the map, the environmental data indicative of inherent characteristics of the locations including road curvature, road grade, road surface, visibility(e.g. snow, fog, hills, elevation, edges), and weather conditions;
([Biondo c.6 l.45] … including information relating to features of proximate topography (e.g., elevations, elevation contours, landforms, locations of hill or mountain summits, locations of inflection points between hills, locations of edges of valleys, locations of edges of plateaus, and so on), physical features of a roadway (changes in curvature, turn radii, grades), … current weather related information (e.g., snow, rain, fog, roadway ice, temperature, and so on)), among other things...)
([Biondo claim.8] relating to features of the route which the motor vehicle is following from a position/navigation system; and wherein the determining the upcoming geographical feature is based on the information relating to the features.)
performing an analysis of the telemetry data relative to the environmental data of the locations to determine crash risk factors(e.g. roadway curvature, traffic patterns, and intersection configurations.) that correlate to an increased crash risk;
([Biondo c.7 l.5] processing and control subsystem 108 is adapted to receive and analyze the geographical location determined by position/navigation processing subsystem 142 and/or some or all of the information stored in navigation data storage 144. During times when the cruise control function is being implemented, processing and control subsystem 108 is further adapted to determine, based on the geographical location and/or the information, whether the motor vehicle is approaching a portion of a road over which the motor vehicle should travel at a slower speed than the target speed, according to various embodiments. )
([Biondo c.13 l.45] a comparison between the geographical location and the features of the route indicates that the motor vehicle is approaching a portion of a road … motor vehicle should travel at a lower speed than the target speed may include, …, or an incident scene (e.g., an accident scene, roadblock, and so on)), a portion of a road having a turn or a curve with a curve radius that is less than a threshold, and a portion of a road having a weather-related anomaly that warrants traveling at a speed that is lower than the target speed (e.g., there is snow, sleet, rain, fog, roadway ice or an excessively cold or hot temperature), among other things. Information used in making this determination may include, for example, information defining physical features of a roadway, speed limit information, locations of traffic control devices, states and timing information relating to traffic control devices, information relating to transient anomalies, and transient anomaly locations.)
determining areas of increased crash risk based on the crash risk factors;
([Biondo c.13 l.45] … a portion of a road having a turn or a curve with a curve radius that is less than a threshold, and a portion of a road having a weather-related anomaly that warrants traveling at a speed that is lower than the target speed (e.g., there is snow, sleet, rain, fog, roadway ice or an excessively cold or hot temperature), among other things. Information used in making this determination may include, for example, information defining physical features of a roadway, speed limit information, locations of traffic control devices, states and timing information relating to traffic control devices, information relating to transient anomalies, and transient anomaly locations.)
determining a location of the ADAS equipped vehicle relative to the map;
([Biondo claim.12] a positioning system interface adapted to receive signals from at least one external infrastructure component; and a processing and control subsystem adapted to determine a route with which the motor vehicle is traveling, to determine a geographical location along the route of the motor vehicle based on the signals, to determine an upcoming geographical feature based on the geographical location along the route of the motor vehicle)
([Biondo c.6 l.15] …adapted to analyze the signals received by positioning system interface 140 in order to determine a geographical location of the motor vehicle)
collecting vehicle data from one or more sensors mounted on the ADAS equipped vehicle, the vehicle data including vehicle ***data***, ****, tire pressure, ****, and ***on-board sensor data***;
([Biondo c.8 l.10] on-board sensors 126 may include one or more sensors selected from a group that includes …, a tire pressure sensor 174.)
Seaman US-20180257653-A1 discloses in a similar invention a consideration for collecting vehicle data including “…vehicle load[0050], tire wear[0028-30], tire temperature”;
(Seaman [0050] … CTU controller 108 can provide a notification of a reduction of a load capacity of the CTU 100. The notification can be sent to the vehicle controller 120 (or to a driver of the vehicle 104), to cause the vehicle 104 (either the vehicle controller 120 or a driver) to not accept cargo loading of the CTU 100 from exceeding a specified weight. In the CTU 100 without a compromised component, the CTU 100 may accept cargo loading up to a first specified weight. Once a component having a compromised condition is detected, then the CTU 100 may accept cargo loading up to a second specified weight that is less than the first specified weight...)
(Seaman [0028-30] A sensor for detecting tire wear can monitor an amount of tread left on the tire. The tire wear sensor can be an optical sensor that can detect reflected light (reflected by the tire in response to light emitted by a light source on the CTU 100 or ambient light) to determine a depth of the tire tread… to prevent the likelihood of a blowout of the tire.)
(Seaman [0031] the heat due to friction of the worn bearings can cause a tire blowout if the temperature of the wheel becomes too high. The wheel sensor can include a temperature sensor to detect a temperature of the wheel. If the temperature of the wheel exceeds a specified temperature threshold, then the wheel sensor can output an indication to the CTU controller 108, which can cause a corrective action to be taken.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include vehicle data including vehicle load, tire wear, and tire temperature with a reasonable expectation for success, as taught by Seaman, for the benefit of storing data related stress upon vehicle capacities (e.g. load weight) and condition of tire effectiveness to prevent the likelihood of a blowout of the tire.
Hall US-11204682-B1 discloses in a similar invention a consideration for collecting vehicle data including “…trailering status(e.g. whether or not the car is operable and thereby able to trailer any load associated with the vehicle), spare tire mounted”;
(Hall [c.9 l.30] the data objective may relate to or comprise, for example and without limitation: …; incident detail(s) such as whether the requested service resulted from an accident or stalled while driving; vehicle state data such as whether the car is operable, where the car is, whether the car has a flat tire, whether a spare is available, ...)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include vehicle data including trailering status and spare tire mounted with a reasonable expectation for success, as taught by Hall, for the benefit of storing data related vehicle capacities and resources used in addressing hazardous event operations (e.g. operability, repair(s), resources, tires, weight/load).
calculating an increased risk of vehicle crash index (IRVCI) based on the ADAS equipped vehicle’s location relative to the areas of increased crash risk and the vehicle data;
([Biondo c.12 l.55] block 216, a determination is made (e.g., by the processing and control subsystem) whether the vehicle should accelerate (i.e., when the actual speed is less than the target speed) or decelerate (i.e., when the actual speed is greater than the target speed). According to an embodiment, this determination is made based on the geographical location of the motor vehicle and some or all of the information relating to features of the route which the motor vehicle is following (or potentially may follow). More particularly, with regard to determining whether or not the motor vehicle should accelerate, a determination is made whether the motor vehicle is approaching a feature of the route that may cause the motor vehicle to accelerate due to the force of gravity or whether a feature of the route is present that warrants a slower speed than the target speed. With regard to determining whether or not the motor vehicle should decelerate, a determination is made whether the motor vehicle is approaching a feature of the route that may cause the motor vehicle to decelerate due to the force of gravity or whether a feature of the route is present that warrants avoiding a deceleration maneuver.)
****creating a driver profile, wherein the driver profile is based on collected driving habits, biometric data, and collected driving preferences, wherein the driving habits of the driver include vehicle speed, vehicle acceleration, vehicle deceleration, and steering inputs over a period of time;****
Rose US-20220032921-A1 discloses in a similar invention field of endeavor, a consideration for “… creating a driver profile[0054], wherein the driver profile is based on collected driving habits([0024]) and collected driving preferences(a driver behavior module 320, a behavior characterization module 330, and a risk assessment module 340), wherein the driving habits of the driver include vehicle speed, vehicle acceleration, vehicle deceleration(low speed driving, high speed driving, and speed profiles), and steering inputs(lane changes, drifting across lane markers, drifting onto the shoulder, and the like) over a period of time”;
(Rose [0024] driver habits, such as following distance (or tailgating), lane changes, sudden accelerations, sudden deceleration,)
(Rose [0052-53] … The driver behavior module 320 of the VCS 40 receives the captured sensor data and may identify one or more driver behaviors 321-323. By way of example, the driver behavior module 320 may analyze the sensor data to identify driver behavior 321, which includes, for example, sharp turns, sudden braking, and/or sudden acceleration. The driver behavior module 320 also may analyze the sensor data to identify driver behavior 322, which includes, for example, low speed driving, high speed driving, and speed profiles. Finally, the driver behavior module 320 may analyze the sensor data to identify driver behavior 323, which includes, for example, tailgating, lane changes, drifting across lane markers, drifting onto the shoulder, and the like.)
(Rose [0054] risk assessment module 340 of the VCS 40 may use the microscopic behavior category 331, the macroscopic behavior category 332, and the historical behavior record category 333 to determine a driver profile that may include a risk assessment for the driver of the vehicle system 10.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include creating a driver profile, wherein the driver profile is based on collected driving habits and collected driving preferences, wherein the driving habits of the driver include vehicle speed, vehicle acceleration, vehicle deceleration, and steering inputs over a period of time with a reasonable expectation for success, as taught by Rose, for the benefit of storing data related vehicle operator habits used in addressing operational conditions to include hazardous event(s).
****calculating a driver’s historical aggressiveness metric (D-HAM) by comparing the driving habits to a statistical mean;****
Rose US-20220032921-A1 discloses in a similar invention field of endeavor, a consideration for “… calculating a driver’s historical aggressiveness metric(a risk assessment for the driver) (D-HAM) by comparing the driving habits to a statistical mean([0052-53] e.g. a statistical frequency, severity, pattern, or average)”;
Examiner’s Interpretation: See paragraph [0052] of the present application for an interpretation of a statistical means, included below for reference;
“At step 210, the driver profile 48 is used to calculate a driver’s historical aggressiveness metric (D-HAM). The D-HAM is calculated by comparing the driver profile 48 to a statistical mean. The statistical mean is pre-programmed in the ADAS 12 and stored in the controller 20. The statistical mean identifies average driving patterns for the driver 16 based on the driver profile 48. The method 200 proceeds to step 212.”
collecting real-time vehicle inputs from the driver including speed, ***vehicle data***, and steering inputs relative to the map;
([Biondo c.7 l.40] position/navigation processing subsystem 142 relating to the motor vehicle's geographical location, current speed, target speed, direction of motion, and so on...)
([Biondo c.9 l.12] Steering wheel sensor 172 includes a device adapted to measure the rotation angle of the motor vehicle's steering wheel, and to provide information defining the detected angle to the processing and control system 108.)
([Biondo c.6 l.15] …adapted to analyze the signals received by positioning system interface 140 in order to determine a geographical location of the motor vehicle)
Rose US-20220032921-A1 discloses in a similar invention field of endeavor, a consideration for “…collecting real-time[0060] vehicle inputs from the driver including …, acceleration, deceleration, …”;
(Rose [0060] In the vehicle system 10, the individual sensors in the sensors module 188 continually detect N events in real time… an accelerometer may detect a sudden deceleration or acceleration)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include collecting real-time vehicle inputs from the driver including acceleration and deceleration in real time with a reasonable expectation for success, as taught by Rose, for the benefit of storing data related vehicle operator habits used in addressing operational conditions to include hazardous event(s).
performing an analysis of the vehicle data and the environmental data to determine crash risk factors ***based on input data***;
([Biondo c.13 l.45] … a portion of a road having a turn or a curve with a curve radius that is less than a threshold, and a portion of a road having a weather-related anomaly that warrants traveling at a speed that is lower than the target speed (e.g., there is snow, sleet, rain, fog, roadway ice or an excessively cold or hot temperature), among other things. Information used in making this determination may include, for example, information defining physical features of a roadway, speed limit information, locations of traffic control devices, states and timing information relating to traffic control devices, information relating to transient anomalies, and transient anomaly locations.)
Rose US-20220032921-A1 discloses in a similar invention field of endeavor, a consideration for “…performing an analysis of the vehicle data … when the vehicle inputs deviate from the D-HAM”;
(Rose [claim.2] wherein the driver monitoring system is configured to compare the at least one identified driver behavior to a threshold value and, in response to the comparison, to transmit to the driver a warning message.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include performing an analysis of the vehicle data when the vehicle inputs deviate from thresholds with a reasonable expectation for success, as taught by Rose, for the benefit of storing a metric against which measured inputs are compared in order to identify abnormal operational conditions.
calculating ***vehicle operations*** from crash risk factors based on *** input data***; and
([Biondo c.12 l.55] More particularly, with regard to determining whether or not the motor vehicle should accelerate, a determination is made whether the motor vehicle is approaching a feature of the route that may cause the motor vehicle to accelerate due to the force of gravity or whether a feature of the route is present that warrants a slower speed than the target speed. With regard to determining whether or not the motor vehicle should decelerate, a determination is made whether the motor vehicle is approaching a feature of the route that may cause the motor vehicle to decelerate due to the force of gravity or whether a feature of the route is present that warrants avoiding a deceleration maneuver.))
Xi US-20170297564-A1 discloses in a similar invention field of endeavor, a consideration for “…calculating a driver’s predicted aggressiveness metric (D-PAM) … based on deviation of the D-HAM”;
(Xi [0039] The input information (visual information, tactile information and vehicle state parameters etc.) is processed via the driver model for identifying and predicting the behavior of the driver, and then the vehicle control system parameters are tuned and modified in real time according to the driver's expected value output from the driver model, allowing to make the vehicle performance be changed with the driver characteristics and make the vehicle performance meet the driver's driving behavior characteristic more simultaneously, meeting the driver's driving demands, reducing the driver workload, and avoiding traffic accidents.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include calculating a driver’s predicted behavior based on deviation from historical behavior with a reasonable expectation for success, as taught by Xi, for the benefit of meeting the driver's driving demands, reducing the driver workload, and avoiding traffic accidents.
adapting the response of the ADAS equipped vehicle **** based on the [[data]], ****.
([Biondo c.12 l.55] block 216, a determination is made (e.g., by the processing and control subsystem) whether the vehicle should accelerate (i.e., when the actual speed is less than the target speed) or decelerate (i.e., when the actual speed is greater than the target speed). According to an embodiment, this determination is made based on the geographical location of the motor vehicle and some or all of the information relating to features of the route which the motor vehicle is following)
([Biondo c.12 l.55] More particularly, with regard to determining whether or not the motor vehicle should accelerate, a determination is made whether the motor vehicle is approaching a feature of the route that may cause the motor vehicle to accelerate due to the force of gravity or whether a feature of the route is present that warrants a slower speed than the target speed. With regard to determining whether or not the motor vehicle should decelerate, a determination is made whether the motor vehicle is approaching a feature of the route that may cause the motor vehicle to decelerate due to the force of gravity or whether a feature of the route is present that warrants avoiding a deceleration maneuver.))
Kuras US-20190176826-A1 discloses in a similar invention field of endeavor, a consideration for propulsion control systems with varying aggressiveness of response configured to control a response of a vehicle “…by at least adapting a response aggressiveness of a plurality of actuator devices of the ADAS equipped vehicle [[based on data]], wherein the plurality of actuator devices are associated with at least one of a propulsion system, a transmission system, a steering system, and a brake system of the vehicle, and wherein adapting the response aggressiveness includes adjusting an intensity of at least one actuator-level control parameter of the plurality of actuator devices by which the plurality of actuator devices apply the response of the ADAS equipped vehicle.”;
(Kuras [0050] Propulsion system controls 24 may, however, impose a limit on adjustment of the continuously variable transmission 28 and/or power source 18 in response to operator requests for acceleration, deceleration, and directional shifts. By imposing such a limit, propulsion system controls 24 may avoid excessive levels of acceleration and jerk in certain operational situations, and select higher levels of jerk/acceleration/deceleration limits in other operational situations, such as an aggressive braking situation, a directional shift situation, and a blade load shedding (BLS) situation. Propulsion system controls 24 may control jerk levels as discussed above by implementing output control commands to adjust torque output from power source 18 and/or CVT 28 in a manner that controls the acceleration and/or jerk of mobile machine 10 differently in some circumstances than in other circumstances. By doing so, propulsion system controls 24 may allow relatively high levels of acceleration and jerk in circumstances where the operator expects and desires aggressive response from propulsion control system 12, while limiting acceleration and jerk to lower values in circumstances where the operator does not expect or desire such aggressive operation.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include at least adapting a response aggressiveness of a plurality of actuator devices of the ADAS equipped vehicle based on data, wherein the plurality of actuator devices are associated with at least one of a propulsion system, a transmission system, a steering system, and a brake system of the vehicle, and wherein adapting the response aggressiveness includes adjusting an intensity of at least one actuator-level control parameter of the plurality of actuator devices by which the plurality of actuator devices apply the response of the ADAS equipped vehicle with a reasonable expectation for success, as taught by Kuras, for the benefit of controlling user comfort, vehicle jerk, and desired aggressive responses according to operational/user conditions.
Xi US-20170297564-A1 discloses in a similar invention field of endeavor, a consideration for “…adapting the response of the … vehicle based on the D-PAM”;
(Xi [0039] The input information (visual information, tactile information and vehicle state parameters etc.) is processed via the driver model for identifying and predicting the behavior of the driver, and then the vehicle control system parameters are tuned and modified in real time according to the driver's expected value output from the driver model, allowing to make the vehicle performance be changed with the driver characteristics and make the vehicle performance meet the driver's driving behavior characteristic more simultaneously, meeting the driver's driving demands, reducing the driver workload, and avoiding traffic accidents.)
It would have been obvious to one of ordinary skill in the art before the time the instant application was effectively filed to adapt the modified system of Biondo to include adapting the response of the vehicle based driver behavior with a reasonable expectation for success, as taught by Xi, for the benefit of meeting the driver's driving demands, reducing the driver workload, and avoiding traffic accidents.
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 extension fee 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 date of this final action.
It should be noted that there exists prior art which is pertinent to significant though unclaimed features of the defined invention or directed to the state of art. The following is a brief description of relevant prior art cited but not applied:
Chiron US-20190340850-A1 discloses in a similar invention a consider for “…telemetry data including near-crash events”;
(Chiron [claim.3] an accident report according to claim 1, wherein the data representing a vehicle parameter comprises at least one of the following data: a status of an airbag of the vehicle, a status of an electronic stability program of the vehicle, a location of the vehicle, a timestamp indicating the moment of activation of the airbag or of the electronic stability program, a mileage, a log of alerts, fault codes, a steering wheel angle, and a pressure on the brake pedal.)
See PTO-892: Notice of references cited.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW JOHN MOSCOLA whose telephone number is (571)272-6944.
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/M.J.M./Examiner, Art Unit 3663
/TYLER J LEE/Primary Examiner, Art Unit 3663
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