DETAILED ACTION
This communication is in response to the claims filed on 09/16/2024.
Application No: 18/885,886.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice of Pre-AIA or AIA Status
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.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U. S. C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co. , 383 U. S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U. S. C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-6, 8-12, 19-23, 25-28 and 30, are rejected under 35 U. S. C. 103 as being unpatentable over Beaurepaire et al. (US 20180107216 A1) in view of WALKER et al. (US 20190107836 A1).
Regarding claim 1, Beaurepaire teaches a vehicular navigation system, the vehicular navigation system ([0018], e.g. A vehicle may be configured for one of the variety of levels of driver assistance systems. Alternatively, a vehicle may be configured for multiple levels of driver assistance systems, which may be associated with different operational modes. [0097] FIG. 6 illustrates an example vehicle 124 for collecting data regarding the vehicle parameters 203 and user parameters 205. A connected vehicle includes a communication device and an environment sensor array for reporting the surroundings of the vehicle 124 to the server 125. The connected vehicle may include an integrated communication device coupled with an in-dash navigation system. [0077] An index or score may be generated based on the number of matches and/or the degree of matches between the activity profiles and the road segment parameters), comprising:
a sensor disposed at a vehicle equipped with the vehicular navigation system, wherein the sensor is operable to capture sensor data ([0097] A connected vehicle includes a communication device and an environment sensor array for reporting the surroundings of the vehicle 124 to the server 125. The connected vehicle may include an integrated communication device coupled with an in-dash navigation system (i.e. the sensor is operable to capture sensor data));
an electronic control unit (ECU) comprising electronic circuitry and associated software ([0005], e.g. The controller (i.e. EUC) is configured to calculate a route to a destination using road segments accessed from the database and identify an activity sequence based on the at least one selection of the activity at the at least one road characteristic for the route to the destination (i.e. Fig. 6 (111) - speed sensor that measures a speed of the engine or a speed of the vehicle wheels));
wherein the electronic circuitry of the ECU comprises a data processor operable to process sensor data captured by the sensor to determine an attribute of the equipped vehicle associated with a quality of a road along which the equipped vehicle is traveling ([0111], Fig. 6 (113, 115, 116 – sensors), e.g. The activity profile may include threshold values, acceptable ranged, or minimum levels for various road characteristics. Road characteristics may include quality characteristics (e.g., surface type, roughness, known hazards, potholes), curviness of the route segment (e.g. curves per km, sharp turns), traffic levels, connectivity (e.g. cellular connection type, expected bandwidth, or other factors), noise levels (from traffic, collected from vehicle sensors, or other sources), or luminosity (e.g. expected light intensity according to daytime, incoming lights from other vehicles) (i.e. process sensor data captured by the sensor to determine road quality attributes of the equipped vehicle));
wherein the ECU determines a road quality index of the road along which the equipped vehicle is traveling based at least in part on the determined attribute of the equipped vehicle ([0073], e.g. The activity selection module 200 may first determine which road segments are flagged with autonomous driving capabilities and further limit or define the applicability to activity planning based on the route parameters 201, vehicle parameters 203, and/or user parameters 205. From this analysis of the driving assistance properties of the road segments, the activity selection module 200 may calculate and assign a metric, for example, a quality of service index, based on one or more of the segments, or the entire route. The metric may be a numeric value or a grade of the road segments' suitability for activities (i.e. the ECU determines a road quality index of the road along which the vehicle is traveling)).
Beaurepaire teaches apparatus and methods for matching activities with one or more segments. A series of road segments, which may form a route to at least one destination. However, Beaurepaire differs from the claimed invention in not specifically and clearly describing wherein
the ECU determines a navigation route from a starting location to a destination location based at least in part on the road quality index of roads connecting the starting location and the destination location.
However, in the analogous field of endeavor, WALKER teaches wherein
the ECU determines a navigation route from a starting location to a destination location based at least in part on the road quality index of roads connecting the starting location and the destination location ([0030], e.g. FIG. 4 depicts a pair of road segments, a plurality of road signs and a ramp. On an object basis and as shown in FIG. 4, a geospatial accuracy error A.sub.O, a quality prediction for existence confidence E.sub.O of the object and a quality prediction for classification confidence C.sub.A are defined for each object, that is, for each road sign, the ramp and attributes, e.g., each lane marking, of each road segment. In addition, the set of quality values including the geospatial accuracy error and the quality prediction for existence confidence may be determined for one or more validation groups including a lane lines validation group, a sign faces validation group (i.e. determining in part on the road quality index of roads). [0043] In contrast, in an instance in which the map data has a relatively low quality index, a route may be defined with greater reliance placed upon other types of sensor data including camera data and/or detector data, with less reliance placed upon the map data as a result of its lower quality index. [0044] In terms of navigation information, the quality index may additionally or alternatively allow a vehicle to select among various candidate routes in order to increase the map quality (i.e. the ECU determines a navigation route from a starting location to a destination location based on in part on the road quality index of roads)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to implement the method of WALKER within the method of Beaurepaire. The motivation to combine references is that the combined method provides an apparatus in order to facilitate the navigation of a vehicle, such as an autonomous vehicle, utilizing map data in which the quality associated with the map data is provided in a more computationally efficient manner By providing an indication as to the quality associated with the map data, the navigation of a vehicle, such as an autonomous vehicle, may take into account the quality associated with the map data in relation to determining the manner in which the map data is to be combined with other types of sensor data, thereby allowing for the navigation of the autonomous vehicle in a reliable and trustworthy manner (See WALKER [0005]).
Regarding claim 2, Beaurepaire in view of WALKER teaches all the limitations of claim 1. Beaurepaire further teaches wherein the sensor comprises a suspension pressure sensor ([0100], e.g. In some alternatives, additional sensors may be included in the vehicle 124. An engine sensor 111 may include a throttle sensor that measures a position of a throttle of the engine or a position of an accelerator pedal, a brake senor that measures a position of a braking mechanism or a brake pedal, or a speed sensor that measures a speed of the engine or a speed of the vehicle wheels. Another additional example, vehicle sensor 113, may include a steering wheel angle sensor, a speedometer sensor, or a tachometer sensor. The vehicle sensor 113 may include a microphone, an internal camera, or another sensor to detect the internal environment of the vehicle 124 (i.e. ordinary skills in the art would have installed a type of a sensor, namely, suspension pressure sensor along with other vehicle sensors as in Fig. 6)).
Regarding claim 3, Beaurepaire in view of WALKER teaches all the limitations of claim 1. Beaurepaire further teaches wherein the attribute comprises tire pressure of one or more tires of the equipped vehicle ([0100], e.g. In some alternatives, additional sensors may be included in the vehicle 124. An engine sensor 111 may include a throttle sensor that measures a position of a throttle of the engine or a position of an accelerator pedal, a brake senor that measures a position of a braking mechanism or a brake pedal, or a speed sensor that measures a speed of the engine or a speed of the vehicle wheels. Another additional example, vehicle sensor 113, may include a steering wheel angle sensor, a speedometer sensor, or a tachometer sensor. The vehicle sensor 113 may include a microphone, an internal camera, or another sensor to detect the internal environment of the vehicle 124 (i.e. ordinary skills in the art would have installed a type of a sensor, namely, tire pressure sensor along with other vehicle sensors as in Fig. 6)).
Regarding claim 4, Beaurepaire in view of WALKER teaches all the limitations of claim 1. Beaurepaire further teaches wherein the sensor comprises a gyroscope ([0132], e.g. The position circuitry 227 may also include gyroscopes, accelerometers, magnetometers, or any other device for tracking or determining movement of a mobile device (i.e. the sensor comprises a gyroscope)).
Regarding claim 5, Beaurepaire in view of WALKER teaches all the limitations of claim 4. Beaurepaire further teaches wherein the ECU, at least in part via processing of data captured by the gyroscope, determines yaw, pitch and roll of the equipped vehicle, and wherein the ECU determines the road quality index of the road along which the equipped vehicle is travelling based at least in part on the determined yaw, pitch and roll of the equipped vehicle ([0132], e.g. The position circuitry 227 may also include gyroscopes, accelerometers, magnetometers, or any other device for tracking or determining movement of a mobile device. The gyroscope is operable to detect, recognize, or measure the current orientation, or changes in orientation, of a mobile device. Gyroscope orientation change detection may operate as a measure of yaw, pitch, or roll of the mobile device (i.e. the sensor comprises a gyroscope to determines yaw, pitch and roll of the equipped vehicle). [0112] The quality of service index may be calculated based on differences between multiple driving assistance properties and corresponding activity requirements (i.e. ECU determines the road quality index of the road based at least in part on the detected gyroscope parameters)).
Regarding claim 6, Beaurepaire in view of WALKER teaches all the limitations of claim 1. Beaurepaire further teaches wherein the sensor comprises a camera ([0094], Fig. 6 (115), e.g. The activity selection module 200 may collect sensor data (e.g., camera images) in order to monitor facial expressions or biometrics of the user while performing the activity).
Regarding claim 8, Beaurepaire in view of WALKER teaches all the limitations of claim 6. Beaurepaire further teaches wherein the ECU, via processing of image data captured by the camera, detects a pothole in the road along which the equipped vehicle is traveling, and wherein the ECU determines the road quality index of the road based at least in part on the detected pothole ([0058], e.g. The route parameters 201 may include road condition data indicative of the condition of the road, including the likelihood of potholes, whether the route is smooth or rough, and/or a frequency of bumps. The road condition data may include the location of potholes or concrete slab edges. [0110] the activity profile may include threshold values, acceptable ranged, or minimum levels for various road characteristics. Road characteristics may include quality characteristics (e.g., surface type, roughness, known hazards, potholes). [0112] The quality of service index may be calculated based on differences between multiple driving assistance properties and corresponding activity requirements (i.e. ECU determines the road quality index of the road based at least in part on the detected pothole)).
Regarding claim 9, Beaurepaire in view of WALKER teaches all the limitations of claim 1. Beaurepaire further teaches wherein the determined road quality index is stored in a database of road quality indices of a plurality of roads ([0127], e.g. The databases may also include other attributes of or about the roads such as, for example, geographic coordinates, street names, address ranges, speed limits, turn restrictions at intersections, and/or other navigation related attributes (i.e. road quality index is stored in a database)).
Regarding claim 10, Beaurepaire in view of WALKER teaches all the limitations of claim 9. Beaurepaire further teaches wherein the database of road quality indices includes determined road quality indices of roads that have been traveled by the equipped vehicle ([0127], e.g. The databases may also include other attributes of or about the roads such as, for example, geographic coordinates, street names, address ranges, speed limits, turn restrictions at intersections, and/or other navigation related attributes (i.e. database stores road quality indices of roads that have been traveled by the equipped vehicle)).
Regarding claim 11, Beaurepaire in view of WALKER teaches all the limitations of claim 10. Beaurepaire further teaches wherein the ECU determines the road quality index and attributes the determined road quality index to a road in the database based at least in part on a global position system ([0099], e.g. The vehicles 124 may include a global positioning system, a dead reckoning-type system, cellular location system, or combinations of these or other systems, which may be referred to as position circuitry or a position detector. The positioning circuitry may include suitable sensing devices that measure the traveling distance, speed, direction, and so on, of the mobile device 122. The positioning system may also include a receiver and correlation chip to obtain a GPS signal. Alternatively or additionally, the one or more detectors or sensors may include an accelerometer built or embedded into or within the interior of the mobile device 122. [0112] The quality of service index may be calculated based on differences between multiple driving assistance properties and corresponding activity requirements (i.e. ECU determines the road quality index of the road based at least in part on the global position system )).
Regarding claim 12, Beaurepaire in view of WALKER teaches all the limitations of claim 9. Beaurepaire further teaches wherein the database of road quality indices includes road quality indices of roads determined by other vehicles traveling along the roads ([0113], e.g. The display 221 is an example means for displaying the routing command. The routing command may be derived from a road network or map data stored in database 133. The database 133 is an example means for storing map data including a road network (i.e. road quality indices of roads determined by other vehicles traveling along the roads also stores in the database as road network data)).
Regarding claim 19, Beaurepaire teaches a vehicular navigation system, the vehicular navigation system ([0018], e.g. A vehicle may be configured for one of the variety of levels of driver assistance systems. Alternatively, a vehicle may be configured for multiple levels of driver assistance systems, which may be associated with different operational modes. [0097] FIG. 6 illustrates an example vehicle 124 for collecting data regarding the vehicle parameters 203 and user parameters 205. A connected vehicle includes a communication device and an environment sensor array for reporting the surroundings of the vehicle 124 to the server 125. The connected vehicle may include an integrated communication device coupled with an in-dash navigation system. [0077] An index or score may be generated based on the number of matches and/or the degree of matches between the activity profiles and the road segment parameters), comprising:
a sensor disposed at a vehicle equipped with the vehicular navigation system, wherein the sensor is operable to capture sensor data ([0097] A connected vehicle includes a communication device and an environment sensor array for reporting the surroundings of the vehicle 124 to the server 125. The connected vehicle may include an integrated communication device coupled with an in-dash navigation system (i.e. the sensor is operable to capture sensor data));
an electronic control unit (ECU) comprising electronic circuitry and associated software ([0005], e.g. The controller (i.e. EUC) is configured to calculate a route to a destination using road segments accessed from the database and identify an activity sequence based on the at least one selection of the activity at the at least one road characteristic for the route to the destination (i.e. Fig. 6 (111) - speed sensor that measures a speed of the engine or a speed of the vehicle wheels));
wherein the electronic circuitry of the ECU comprises a data processor operable to process sensor data captured by the sensor to determine an attribute of the equipped vehicle associated with a quality of a road along which the equipped vehicle is traveling ([0111], Fig. 6 (113, 115, 116 – sensors), e.g. The activity profile may include threshold values, acceptable ranged, or minimum levels for various road characteristics. Road characteristics may include quality characteristics (e.g., surface type, roughness, known hazards, potholes), curviness of the route segment (e.g. curves per km, sharp turns), traffic levels, connectivity (e.g. cellular connection type, expected bandwidth, or other factors), noise levels (from traffic, collected from vehicle sensors, or other sources), or luminosity (e.g. expected light intensity according to daytime, incoming lights from other vehicles) (i.e. process sensor data captured by the sensor to determine road quality attributes of the equipped vehicle));
wherein the ECU determines a road quality index of the road along which the equipped vehicle is traveling based at least in part on the determined attribute of the equipped vehicle ([0073], e.g. The activity selection module 200 may first determine which road segments are flagged with autonomous driving capabilities and further limit or define the applicability to activity planning based on the route parameters 201, vehicle parameters 203, and/or user parameters 205. From this analysis of the driving assistance properties of the road segments, the activity selection module 200 may calculate and assign a metric, for example, a quality of service index, based on one or more of the segments, or the entire route. The metric may be a numeric value or a grade of the road segments' suitability for activities (i.e. the ECU determines a road quality index of the road along which the vehicle is traveling);
wherein the determined road quality index is stored in a database of road quality indices of a plurality of roads ([0127], e.g. The databases may also include other attributes of or about the roads such as, for example, geographic coordinates, street names, address ranges, speed limits, turn restrictions at intersections, and/or other navigation related attributes (i.e. road quality index is stored in a database));
wherein the ECU determines the road quality index and attributes the determined road quality index to a road of the plurality of roads in the database based at least in part on a global position system ([0099], e.g. The vehicles 124 may include a global positioning system, a dead reckoning-type system, cellular location system, or combinations of these or other systems, which may be referred to as position circuitry or a position detector. The positioning circuitry may include suitable sensing devices that measure the traveling distance, speed, direction, and so on, of the mobile device 122. The positioning system may also include a receiver and correlation chip to obtain a GPS signal. Alternatively or additionally, the one or more detectors or sensors may include an accelerometer built or embedded into or within the interior of the mobile device 122. [0112] The quality of service index may be calculated based on differences between multiple driving assistance properties and corresponding activity requirements (i.e. ECU determines the road quality index of the road based at least in part on the global position system )).
Beaurepaire teaches apparatus and methods for matching activities with one or more segments. A series of road segments, which may form a route to at least one destination. However, Beaurepaire differs from the claimed invention in not specifically and clearly describing wherein
the ECU determines a navigation route from a starting location to a destination location based at least in part on the stored road quality index of roads connecting the starting location and the destination location.
However, in the analogous field of endeavor, WALKER teaches wherein
the ECU determines a navigation route from a starting location to a destination location based at least in part on the stored road quality index of roads connecting the starting location and the destination location ([0030], e.g. FIG. 4 depicts a pair of road segments, a plurality of road signs and a ramp. On an object basis and as shown in FIG. 4, a geospatial accuracy error A.sub.O, a quality prediction for existence confidence E.sub.O of the object and a quality prediction for classification confidence C.sub.A are defined for each object, that is, for each road sign, the ramp and attributes, e.g., each lane marking, of each road segment. In addition, the set of quality values including the geospatial accuracy error and the quality prediction for existence confidence may be determined for one or more validation groups including a lane lines validation group, a sign faces validation group (i.e. determining in part on the road quality index of roads). [0043] In contrast, in an instance in which the map data has a relatively low quality index, a route may be defined with greater reliance placed upon other types of sensor data including camera data and/or detector data, with less reliance placed upon the map data as a result of its lower quality index. [0044] In terms of navigation information, the quality index may additionally or alternatively allow a vehicle to select among various candidate routes in order to increase the map quality (i.e. the ECU determines a navigation route from a starting location to a destination location based on in part on the road quality index of roads)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to implement the method of WALKER within the method of Beaurepaire. The motivation to combine references is that the combined method provides an apparatus in order to facilitate the navigation of a vehicle, such as an autonomous vehicle, utilizing map data in which the quality associated with the map data is provided in a more computationally efficient manner By providing an indication as to the quality associated with the map data, the navigation of a vehicle, such as an autonomous vehicle, may take into account the quality associated with the map data in relation to determining the manner in which the map data is to be combined with other types of sensor data, thereby allowing for the navigation of the autonomous vehicle in a reliable and trustworthy manner (See WALKER [0005]).
Regarding claim 20, Beaurepaire in view of WALKER teaches all the limitations of claim 19. Beaurepaire further teaches wherein the sensor comprises a suspension pressure sensor ([0100], e.g. In some alternatives, additional sensors may be included in the vehicle 124. An engine sensor 111 may include a throttle sensor that measures a position of a throttle of the engine or a position of an accelerator pedal, a brake senor that measures a position of a braking mechanism or a brake pedal, or a speed sensor that measures a speed of the engine or a speed of the vehicle wheels. Another additional example, vehicle sensor 113, may include a steering wheel angle sensor, a speedometer sensor, or a tachometer sensor. The vehicle sensor 113 may include a microphone, an internal camera, or another sensor to detect the internal environment of the vehicle 124 (i.e. ordinary skills in the art would have installed a type of a sensor, namely, suspension pressure sensor along with other vehicle sensors as in Fig. 6)).
Regarding claim 21, Beaurepaire in view of WALKER teaches all the limitations of claim 19. Beaurepaire further teaches wherein the attribute comprises tire pressure of one or more tires of the equipped vehicle ([0100], e.g. In some alternatives, additional sensors may be included in the vehicle 124. An engine sensor 111 may include a throttle sensor that measures a position of a throttle of the engine or a position of an accelerator pedal, a brake senor that measures a position of a braking mechanism or a brake pedal, or a speed sensor that measures a speed of the engine or a speed of the vehicle wheels. Another additional example, vehicle sensor 113, may include a steering wheel angle sensor, a speedometer sensor, or a tachometer sensor. The vehicle sensor 113 may include a microphone, an internal camera, or another sensor to detect the internal environment of the vehicle 124 (i.e. ordinary skills in the art would have installed a type of a sensor, namely, tire pressure sensor along with other vehicle sensors as in Fig. 6)).
Regarding claim 22, Beaurepaire in view of WALKER teaches all the limitations of claim 19. Beaurepaire further teaches wherein the sensor comprises a gyroscope ([0132], e.g. The position circuitry 227 may also include gyroscopes, accelerometers, magnetometers, or any other device for tracking or determining movement of a mobile device (i.e. the sensor comprises a gyroscope)), and
wherein the ECU, at least in part via processing of data captured by the gyroscope, determines yaw, pitch and roll of the equipped vehicle, and wherein the ECU determines the road quality index of the road along which the equipped vehicle is travelling based at least in part on the determined yaw, pitch and roll of the equipped vehicle ([0132], e.g. The position circuitry 227 may also include gyroscopes, accelerometers, magnetometers, or any other device for tracking or determining movement of a mobile device. The gyroscope is operable to detect, recognize, or measure the current orientation, or changes in orientation, of a mobile device. Gyroscope orientation change detection may operate as a measure of yaw, pitch, or roll of the mobile device (i.e. the sensor comprises a gyroscope to determines yaw, pitch and roll of the equipped vehicle). [0112] The quality of service index may be calculated based on differences between multiple driving assistance properties and corresponding activity requirements (i.e. ECU determines the road quality index of the road based at least in part on the detected gyroscope parameters)).
Regarding claim 23, Beaurepaire in view of WALKER teaches all the limitations of claim 19. Beaurepaire further teaches wherein the sensor comprises a camera ([0094], Fig. 6 (115), e.g. The activity selection module 200 may collect sensor data (e.g., camera images) in order to monitor facial expressions or biometrics of the user while performing the activity).
Regarding claim 25, Beaurepaire in view of WALKER teaches all the limitations of claim 23. Beaurepaire further teaches wherein the ECU, via processing of image data captured by the camera, detects a pothole in the road along which the equipped vehicle is traveling, and wherein the ECU determines the road quality index of the road based at least in part on the detected pothole ([0058], e.g. The route parameters 201 may include road condition data indicative of the condition of the road, including the likelihood of potholes, whether the route is smooth or rough, and/or a frequency of bumps. The road condition data may include the location of potholes or concrete slab edges. [0110] the activity profile may include threshold values, acceptable ranged, or minimum levels for various road characteristics. Road characteristics may include quality characteristics (e.g., surface type, roughness, known hazards, potholes). [0112] The quality of service index may be calculated based on differences between multiple driving assistance properties and corresponding activity requirements (i.e. ECU determines the road quality index of the road based at least in part on the detected pothole)).
Regarding claim 26, Beaurepaire teaches a vehicular navigation system, the vehicular navigation system ([0018], e.g. A vehicle may be configured for one of the variety of levels of driver assistance systems. Alternatively, a vehicle may be configured for multiple levels of driver assistance systems, which may be associated with different operational modes. [0097] FIG. 6 illustrates an example vehicle 124 for collecting data regarding the vehicle parameters 203 and user parameters 205. A connected vehicle includes a communication device and an environment sensor array for reporting the surroundings of the vehicle 124 to the server 125. The connected vehicle may include an integrated communication device coupled with an in-dash navigation system. [0077] An index or score may be generated based on the number of matches and/or the degree of matches between the activity profiles and the road segment parameters), comprising:
a sensor disposed at a vehicle equipped with the vehicular navigation system, wherein the sensor is operable to capture sensor data ([0097] A connected vehicle includes a communication device and an environment sensor array for reporting the surroundings of the vehicle 124 to the server 125. The connected vehicle may include an integrated communication device coupled with an in-dash navigation system (i.e. the sensor is operable to capture sensor data));
an electronic control unit (ECU) comprising electronic circuitry and associated software ([0005], e.g. The controller (i.e. EUC) is configured to calculate a route to a destination using road segments accessed from the database and identify an activity sequence based on the at least one selection of the activity at the at least one road characteristic for the route to the destination (i.e. Fig. 6 (111) - speed sensor that measures a speed of the engine or a speed of the vehicle wheels));
wherein the electronic circuitry of the ECU comprises a data processor operable to process sensor data captured by the sensor to determine an attribute of the equipped vehicle associated with a quality of a road along which the equipped vehicle is traveling ([0111], Fig. 6 (113, 115, 116 – sensors), e.g. The activity profile may include threshold values, acceptable ranged, or minimum levels for various road characteristics. Road characteristics may include quality characteristics (e.g., surface type, roughness, known hazards, potholes), curviness of the route segment (e.g. curves per km, sharp turns), traffic levels, connectivity (e.g. cellular connection type, expected bandwidth, or other factors), noise levels (from traffic, collected from vehicle sensors, or other sources), or luminosity (e.g. expected light intensity according to daytime, incoming lights from other vehicles) (i.e. process sensor data captured by the sensor to determine road quality attributes of the equipped vehicle));
wherein the ECU determines a road quality index of the road along which the equipped vehicle is traveling based at least in part on the determined attribute of the equipped vehicle ([0073], e.g. The activity selection module 200 may first determine which road segments are flagged with autonomous driving capabilities and further limit or define the applicability to activity planning based on the route parameters 201, vehicle parameters 203, and/or user parameters 205. From this analysis of the driving assistance properties of the road segments, the activity selection module 200 may calculate and assign a metric, for example, a quality of service index, based on one or more of the segments, or the entire route. The metric may be a numeric value or a grade of the road segments' suitability for activities (i.e. the ECU determines a road quality index of the road along which the vehicle is traveling);
wherein the determined road quality index is stored in a database of road quality indices of a plurality of roads ([0127], e.g. The databases may also include other attributes of or about the roads such as, for example, geographic coordinates, street names, address ranges, speed limits, turn restrictions at intersections, and/or other navigation related attributes (i.e. road quality index is stored in a database));
wherein the database of road quality indices includes determined road quality indices of roads that have been traveled by the equipped vehicle ([0127], e.g. The databases may also include other attributes of or about the roads such as, for example, geographic coordinates, street names, address ranges, speed limits, turn restrictions at intersections, and/or other navigation related attributes (i.e. database stores road quality indices of roads that have been traveled by the equipped vehicle));
wherein the database of road quality indices includes road quality indices of roads determined by other vehicles traveling along the roads ([0127], e.g. The databases may also include other attributes of or about the roads such as, for example, geographic coordinates, street names, address ranges, speed limits, turn restrictions at intersections, and/or other navigation related attributes (i.e. database stores road quality indices of roads that have been traveled by the equipped vehicle));
wherein the ECU determines the road quality index and attributes the determined road quality index to a road in the database based at least in part on a global position system ([0099], e.g. The vehicles 124 may include a global positioning system, a dead reckoning-type system, cellular location system, or combinations of these or other systems, which may be referred to as position circuitry or a position detector. The positioning circuitry may include suitable sensing devices that measure the traveling distance, speed, direction, and so on, of the mobile device 122. The positioning system may also include a receiver and correlation chip to obtain a GPS signal. Alternatively or additionally, the one or more detectors or sensors may include an accelerometer built or embedded into or within the interior of the mobile device 122. [0112] The quality of service index may be calculated based on differences between multiple driving assistance properties and corresponding activity requirements (i.e. ECU determines the road quality index of the road based at least in part on the global position system )).
Beaurepaire teaches apparatus and methods for matching activities with one or more segments. A series of road segments, which may form a route to at least one destination. However, Beaurepaire differs from the claimed invention in not specifically and clearly describing wherein
the ECU determines a navigation route from a starting location to a destination location based at least in part on the stored road quality index of roads connecting the starting location and the destination location.
However, in the analogous field of endeavor, WALKER teaches wherein
the ECU determines a navigation route from a starting location to a destination location based at least in part on the stored road quality index of roads connecting the starting location and the destination location ([0030], e.g. FIG. 4 depicts a pair of road segments, a plurality of road signs and a ramp. On an object basis and as shown in FIG. 4, a geospatial accuracy error A.sub.O, a quality prediction for existence confidence E.sub.O of the object and a quality prediction for classification confidence C.sub.A are defined for each object, that is, for each road sign, the ramp and attributes, e.g., each lane marking, of each road segment. In addition, the set of quality values including the geospatial accuracy error and the quality prediction for existence confidence may be determined for one or more validation groups including a lane lines validation group, a sign faces validation group (i.e. determining in part on the road quality index of roads). [0043] In contrast, in an instance in which the map data has a relatively low quality index, a route may be defined with greater reliance placed upon other types of sensor data including camera data and/or detector data, with less reliance placed upon the map data as a result of its lower quality index. [0044] In terms of navigation information, the quality index may additionally or alternatively allow a vehicle to select among various candidate routes in order to increase the map quality (i.e. the ECU determines a navigation route from a starting location to a destination location based on in part on the road quality index of roads)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to implement the method of WALKER within the method of Beaurepaire. The motivation to combine references is that the combined method provides an apparatus in order to facilitate the navigation of a vehicle, such as an autonomous vehicle, utilizing map data in which the quality associated with the map data is provided in a more computationally efficient manner By providing an indication as to the quality associated with the map data, the navigation of a vehicle, such as an autonomous vehicle, may take into account the quality associated with the map data in relation to determining the manner in which the map data is to be combined with other types of sensor data, thereby allowing for the navigation of the autonomous vehicle in a reliable and trustworthy manner (See WALKER [0005]).
Regarding claim 27, Beaurepaire in view of WALKER teaches all the limitations of claim 26. Beaurepaire further teaches wherein the attribute comprises tire pressure of one or more tires of the equipped vehicle ([0100], e.g. In some alternatives, additional sensors may be included in the vehicle 124. An engine sensor 111 may include a throttle sensor that measures a position of a throttle of the engine or a position of an accelerator pedal, a brake senor that measures a position of a braking mechanism or a brake pedal, or a speed sensor that measures a speed of the engine or a speed of the vehicle wheels. Another additional example, vehicle sensor 113, may include a steering wheel angle sensor, a speedometer sensor, or a tachometer sensor. The vehicle sensor 113 may include a microphone, an internal camera, or another sensor to detect the internal environment of the vehicle 124 (i.e. ordinary skills in the art would have installed a type of a sensor, namely, tire pressure sensor along with other vehicle sensors as in Fig. 6)).
Regarding claim 28, Beaurepaire in view of WALKER teaches all the limitations of claim 26. Beaurepaire further teaches wherein the sensor comprises a gyroscope, and wherein the ECU, at least in part via processing of data captured by the gyroscope, determines yaw, pitch and roll of the equipped vehicle, and wherein the ECU determines the road quality index of the road along which the equipped vehicle is travelling based at least in part on the determined yaw, pitch and roll of the equipped vehicle ([0132], e.g. The position circuitry 227 may also include gyroscopes, accelerometers, magnetometers, or any other device for tracking or determining movement of a mobile device. The gyroscope is operable to detect, recognize, or measure the current orientation, or changes in orientation, of a mobile device. Gyroscope orientation change detection may operate as a measure of yaw, pitch, or roll of the mobile device (i.e. the sensor comprises a gyroscope to determines yaw, pitch and roll of the equipped vehicle). [0112] The quality of service index may be calculated based on differences between multiple driving assistance properties and corresponding activity requirements (i.e. ECU determines the road quality index of the road based at least in part on the detected gyroscope parameters)).
Regarding claim 30, Beaurepaire in view of WALKER teaches all the limitations of claim 26. Beaurepaire further teaches wherein the sensor comprises a camera, and wherein the ECU, via processing of image data captured by the camera, detects a pothole in the road along which the equipped vehicle is traveling, and wherein the ECU determines the road quality index of the road based at least in part on the detected pothole ([0058], e.g. The route parameters 201 may include road condition data indicative of the condition of the road, including the likelihood of potholes, whether the route is smooth or rough, and/or a frequency of bumps. The road condition data may include the location of potholes or concrete slab edges. [0110] the activity profile may include threshold values, acceptable ranged, or minimum levels for various road characteristics. Road characteristics may include quality characteristics (e.g., surface type, roughness, known hazards, potholes). [0112] The quality of service index may be calculated based on differences between multiple driving assistance properties and corresponding activity requirements (i.e. ECU determines the road quality index of the road based at least in part on the detected pothole)).
Allowable Subject Matter
Claims 7, 13-18, 24 and 29 are objected to as being dependent upon a rejected base claim, but would be allowable, if rewritten in independent form including all of the limitations of the base claim and any intervening claims, and amending claims to overcome any objection(s) and /or rejection(s) set forth in this Office action.
Prior Art Record
The prior art made of record and not relied upon is considered pertinent
to applicant’s disclosure.
Kovarik; Joseph E. (US-20160132705-A1) - Method and System for Autonomous Vehicles.
Singhal; Abhishek (US-10410250-B2) - Vehicle autonomy level selection based on user context.
SHASHUA; AMNON (US-20200326707-A1) - CROWD SOURCING DATA FOR AUTONOMOUS VEHICLE NAVIGATION.
Prasad; Rashmi (US-20210237594-A1) - INTELLIGENT VEHICLES WITH ADVANCED VEHICLE CAMERA SYSTEMS FOR UNDERBODY HAZARD AND FOREIGN OBJECT DETECTION.
Lerner; Emily (US-11823568-B2) - Dynamic speed limit for vehicles and autonomous vehicles.
NIX AXEL (EP-3159853-A1) - SYSTEMS AND METHODS FOR ADVANCED DRIVER ASSISTANCE ANALYTICS.
GAAL ZOLTAN (EP-3696767-A1) - MEHTOD, APPARATUS, AND SYSTEM FOR PROVIDING AN INTERFACE FOR PUBLISHING SENSOR DATA REQUESTS IN CAMPAIGN MANAGEMENT PLATFORM.
WRANNE STAFFAN (EP-3716138-A1) - ROAD BOUNDARY DETERMINATION.
Conclusion
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/MAHENDRA R PATEL/ Primary Examiner, Art Unit 2645
