DETAILED ACTION
This office action is in response to RCE filed on 10/01/2025.
Claims 13, 17, 28, and 29 have been amended; claims 16, and 19-27 are previously presented; and claims 14-15 are canceled. Therefore, claims 13 and 16-29 are pending in the instant application.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/01/2025 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/22/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendments/Arguments
Applicant' s arguments filed on 08/08/2025 have been fully considered as below.
Applicant's arguments with respect to the rejection under 35 USC 103, see pages 16-21 of Remarks, have been considered but are moot in view of the new grounds of rejection provided below, in light of newly found prior art, which was necessitated based on Applicant's amendments which changed the scope of the claims.
Regarding the objection to the drawings, applicant's arguments, see pages 11-12, have been fully considered and are persuasive. The objection to the drawings has been withdrawn.
Regarding the rejection under 35 USC 112(a), applicant's arguments, see pages 12-14, have been fully considered and are persuasive. The rejection of the claims has been withdrawn.
Regarding the rejection under 35 USC 112(b), applicant's arguments, see pages 14-15, have been fully considered and are persuasive. The rejection of the claims has been withdrawn.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 13, 16-22, and 24-29 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Wang et al. (US 10198009 B2, hereinafter “Wang”).
Regarding claims 13 and 28, Wang discloses a device and a method for determining a driving route for a vehicle having an automated driving mode in which the vehicle is longitudinally and/or laterally controlled in an at least partially automated manner, and a manual driving mode in which the vehicle is longitudinally and/or laterally controlled in an at least partially manual manner by a driver of the vehicle (see at least Fig. 1, col. 3, lines 44-47, “The vehicle 100 includes multiple levels of autonomous functionality, involving different respective levels of engagement on behalf of a driver or operator of the vehicle 100”), the device comprising:
a processor (see at least Fig. 1, col. 6, “the computer system of the controller 106 includes a processor 172”), wherein the device is configured:
to determine clearance information with respect to a clearance of the automated driving mode on different roadway sections of a roadway network traveled by the vehicle (see at least Fig. 4, col. 11-12, determine level of vehicle automation are calculated and predict for each road segment throughout the selected route(s) to the destination (step 406), using the various data of step 404 such as roads, road conditions, traffic patterns, construction, weather, and so on; claim 17, “obtaining inputs pertaining to conditions of a plurality of potential routes to reach an operator-requested destination planned for a vehicle having autonomous operation capability; a sensor unit configured to at least facilitate: obtaining sensor data pertaining to an operator of the vehicle via one or more sensors; and a processor configured to at least facilitate: predicting a future level of engagement required for the operator of the vehicle, for each of the plurality of potential routes, based on the level of automated driving expected for the potential route, using the inputs”);
to determine and/or predict availability information with respect to an availability of the driver of the vehicle for manual longitudinal and/or lateral control of the vehicle during a journey from a starting point to a destination point (see at least Fig. 4, col. 11-12, lines 4-33, determine and/or predict the ability of manual driving of a driver based on a driver state and/or driver preferences, e.g. a current level of alertness of the driver, the history and/or preferences of step 404 may also be used to analyze expected driver preferences for the level of autonomous driving (and associated required level of driver engagement) during various segments of the vehicle drive for at least a desired period of time; claim 17, “monitoring a level of awareness of the operator of the vehicle based on the sensor data; and providing, for the operator, information relating, for each of the plurality of potential routes, (a) the future level of engagement required for the operator with (b) the level of awareness of the operator of the vehicle”); and
to determine the driving route through the roadway network from the starting point to the destination point based on the clearance information and the availability information (see at least Fig. 4, col. 13, lines 1-14, “Also in one embodiment, drivers are provided with a preview of automation levels and preview of drivers' responsibility on each road segment along a navigation route (or any time when not in a route), for automated vehicles. This method considers factors such as road conditions (e.g., lane marker visibility, lanes, present of other vehicles), weather conditions (e.g., snowing) and recorded other vehicles' automation system performance data. Also in one embodiment, drivers are provided with a method of selecting a schedule of preferred automation. In addition, in one embodiment, the most relevant options may be provided to the user (e.g. driver) using adaptive to variant environment and driver state information, as well as asking for the driver's confirmation”),
wherein the availability information indicates, for each time interval of a sequence of time intervals during the journey from the starting point to the destination point, whether the driver of the vehicle is available in the respective time interval to operate the vehicle in the manual driving mode (see at least Fig. 2, col. 8, lines 29-67, col. 9, lines 1-3, col. 12, lines 41-51, level of engagement of the driver for durations of time during the journey from the starting point to the destination point, e.g. a first color 210 (e.g. green) is used to depict road segments and/or durations of time in which little or no driver engagement is required when the driver appears be drowsy or relatively unresponsive; Fig. 4, col. 11, lines 19-67, col. 12, lines 1-59, driver state is repeatedly monitored to suggest a level of automation based on the driver state), and
wherein the device is further configured:
to determine, for a possible driving route, a correlation measure of a correlation between roadway sections in which the automated driving mode is cleared, and time intervals in which the driver is not available to operate the vehicle in the manual driving mode (see at least Fig. 4, col. 11, lines 19-67, col. 12, lines 1-59, driver state is repeatedly monitored to suggest a level of automation based on the driver state, “For example, by suggested alternate routes that may better comport with the driver's state or preferences (step 412). By way of example, in one embodiment, if a driver currently appears to be drowsy or relatively unresponsive, then an alternate route may be proposed and/or selected in which little or no driver engagement may be required for at least a desired period of time”; claim 15, “wherein the selected route is determined so as to match a desired level of automation for operating the vehicle at a particular time, per the preferred schedule from the operator, with the respective future level of engagement required for the operator of the vehicle at the particular time, per the predictions via the processor of the respective future levels of engagement required for the operator of the vehicle for each of the segments of each of the potential routes”);
to determine the driving route such that the correlation measure is increased (see at least col. 13, lines 59-67, col. 14, lines 1-5, ” potentially using the automation level information for other purposes such as route selection; allowing the driver to select schedules/routes with more or less automation based on current conditions (e.g., fastest, shortest, cheapest, and so on)”; col. 12, lines 41-51, “For example, by suggested alternate routes that may better comport with the driver's state or preferences (step 412). By way of example, in one embodiment, if a driver currently appears to be drowsy or relatively unresponsive, then an alternate route may be proposed and/or selected in which little or no driver engagement may be required for at least a desired period of time”); and
to cause the vehicle to travel along the driving route (see at least col. 8, operate the vehicle to travel along the selected driving route).
Regarding claim 16, Wang teaches all the limitation in claim 13. Wang further teaches wherein the device is further configured: to determine the driving route such that the correlation measure is maximized (see at least col. 13, lines 59-67, col. 14, lines 1-5, ”potentially using the automation level information for other purposes such as route selection; allowing the driver to select schedules/routes with more or less automation based on current conditions (e.g., fastest, shortest, cheapest, and so on)”; col. 12, lines 41-51, “For example, by suggested alternate routes that may better comport with the driver's state or preferences (step 412). By way of example, in one embodiment, if a driver currently appears to be drowsy or relatively unresponsive, then an alternate route may be proposed and/or selected in which little or no driver engagement may be required for at least a desired period of time”).
Regarding claims 17 and 29, Wang discloses a device and a method for determining a driving route for a vehicle having an automated driving mode in which the vehicle is longitudinally and/or laterally controlled in an at least partially automated manner, and a manual driving mode in which the vehicle is longitudinally and/or laterally controlled in an at least partially manual manner by a driver of the vehicle (see at least Fig. 1, col. 3, lines 44-47, “The vehicle 100 includes multiple levels of autonomous functionality, involving different respective levels of engagement on behalf of a driver or operator of the vehicle 100”), the device comprising:
a processor (see at least Fig. 1, col. 6, “the computer system of the controller 106 includes a processor 172”), wherein the device is configured:
to determine clearance information with respect to a clearance of the automated driving mode on different roadway sections of a roadway network traveled by the vehicle (see at least Fig. 4, col. 11-12, determine level of vehicle automation are calculated and predict for each road segment throughout the selected route(s) to the destination (step 406), using the various data of step 404 such as roads, road conditions, traffic patterns, construction, weather, and so on; claim 17, “obtaining inputs pertaining to conditions of a plurality of potential routes to reach an operator-requested destination planned for a vehicle having autonomous operation capability; a sensor unit configured to at least facilitate: obtaining sensor data pertaining to an operator of the vehicle via one or more sensors; and a processor configured to at least facilitate: predicting a future level of engagement required for the operator of the vehicle, for each of the plurality of potential routes, based on the level of automated driving expected for the potential route, using the inputs”);
to determine and/or predict availability information with respect to an availability of the driver of the vehicle for manual longitudinal and/or lateral control of the vehicle during a journey from a starting point to a destination point (see at least Fig. 4, col. 11-12, lines 4-33, determine and/or predict the ability of manual driving of a driver based on a driver state and/or driver preferences, e.g. a current level of alertness of the driver, the history and/or preferences of step 404 may also be used to analyze expected driver preferences for the level of autonomous driving (and associated required level of driver engagement) during various segments of the vehicle drive for at least a desired period of time; claim 17, “monitoring a level of awareness of the operator of the vehicle based on the sensor data; and providing, for the operator, information relating, for each of the plurality of potential routes, (a) the future level of engagement required for the operator with (b) the level of awareness of the operator of the vehicle”); and
to determine the driving route through the roadway network from the starting point to the destination point based on the clearance information and the availability information (see at least Fig. 4, col. 13, lines 1-14, “Also in one embodiment, drivers are provided with a preview of automation levels and preview of drivers' responsibility on each road segment along a navigation route (or any time when not in a route), for automated vehicles. This method considers factors such as road conditions (e.g., lane marker visibility, lanes, present of other vehicles), weather conditions (e.g., snowing) and recorded other vehicles' automation system performance data. Also in one embodiment, drivers are provided with a method of selecting a schedule of preferred automation. In addition, in one embodiment, the most relevant options may be provided to the user (e.g. driver) using adaptive to variant environment and driver state information, as well as asking for the driver's confirmation”),
wherein the availability information indicates, for each time interval of a sequence of time intervals during the journey from the starting point to the destination point:
a degree of the availability of the driver of the vehicle for the manual longitudinal and/or lateral control of the vehicle in the respective time interval (see at least Fig. 2, col. 8, lines 29-67, col. 9, lines 1-3, col. 12, lines 41-51, level of engagement of the driver for durations of time during the journey from the starting point to the destination point, e.g. a first color 210 (e.g. green) is used to depict road segments and/or durations of time in which little or no driver engagement is required when the driver appears be drowsy or relatively unresponsive; Fig. 4, col. 11, lines 19-67, col. 12, lines 1-59, driver state is repeatedly monitored to suggest a level of automation based on the driver state); and
a desired degree of automation of the vehicle in the respective time interval (see at least col. 11, lines 27-33, “Various data is obtained pertaining to the vehicle drive (step 404). In one embodiment, crowd-sourced monitoring and data analytics and historical information are obtained as part of the data of step 404. In various embodiments, the data includes various data pertaining to the vehicle operator's preferences and/or history (e.g. as to a general time for leaving for work or other destination, preferred routes, preferred levels of autonomous driving and/or driver engagement requirements at different times and/or locations, and so)”; col. 12, lines 4-34, “In addition, in certain embodiments, driver state monitoring is utilized in step 408 to suggest a level of automation based on a driver state and/or driver preferences … In addition, the history and/or preferences of step 404 may also be used to analyze expected driver preferences for the level of autonomous driving (and associated required level of driver engagement) during various segments of the vehicle drive”),
wherein the clearance information indicates, for each roadway section of a plurality of roadway sections of the roadway network, a possible degree of automation of the vehicle in the respective roadway section (see at least Fig. 4, col. 11-12, determine level of vehicle automation are calculated and predict for each road segment throughout the selected route(s) to the destination (step 406), using the various data of step 404 such as roads, road conditions, traffic patterns, construction, weather, and so on; claim 17, “obtaining inputs pertaining to conditions of a plurality of potential routes to reach an operator-requested destination planned for a vehicle having autonomous operation capability; a sensor unit configured to at least facilitate: obtaining sensor data pertaining to an operator of the vehicle via one or more sensors; and a processor configured to at least facilitate: predicting a future level of engagement required for the operator of the vehicle, for each of the plurality of potential routes, based on the level of automated driving expected for the potential route, using the inputs”); and
wherein the device is further configured:
for a possible driving route having a sequence of roadway sections, to determine a correlation measure of a correlation between possible degrees of automation on the sequence of roadway sections of the possible driving route and desired degrees of automation on the sequence of time intervals during the journey from the starting point to the destination point (see at least see at least Fig. 4, col. 11, lines 19-67, col. 12, lines 1-59, driver state is repeatedly monitored to suggest a level of automation based on the driver state, “For example, by suggested alternate routes that may better comport with the driver's state or preferences (step 412). By way of example, in one embodiment, if a driver currently appears to be drowsy or relatively unresponsive, then an alternate route may be proposed and/or selected in which little or no driver engagement may be required for at least a desired period of time”; claim 15, “wherein the selected route is determined so as to match a desired level of automation for operating the vehicle at a particular time, per the preferred schedule from the operator, with the respective future level of engagement required for the operator of the vehicle at the particular time, per the predictions via the processor of the respective future levels of engagement required for the operator of the vehicle for each of the segments of each of the potential routes”);
to determine the driving route such that the correlation measure is increased (see at least col. 13, lines 59-67, col. 14, lines 1-5, ” potentially using the automation level information for other purposes such as route selection; allowing the driver to select schedules/routes with more or less automation based on current conditions (e.g., fastest, shortest, cheapest, and so on)”; col. 12, lines 41-51, “For example, by suggested alternate routes that may better comport with the driver's state or preferences (step 412). By way of example, in one embodiment, if a driver currently appears to be drowsy or relatively unresponsive, then an alternate route may be proposed and/or selected in which little or no driver engagement may be required for at least a desired period of time”); and
to cause the vehicle to travel along the driving route (see at least col. 8, operate the vehicle to travel along the selected driving route).
Regarding claim 18, Wang teaches all the limitation in claim 17. Wang further teaches wherein: the device is further configured: to determine the driving route such that the correlation measure is maximized (see at least col. 13, lines 59-67, col. 14, lines 1-5, ”potentially using the automation level information for other purposes such as route selection; allowing the driver to select schedules/routes with more or less automation based on current conditions (e.g., fastest, shortest, cheapest, and so on)”; col. 12, lines 41-51, “For example, by suggested alternate routes that may better comport with the driver's state or preferences (step 412). By way of example, in one embodiment, if a driver currently appears to be drowsy or relatively unresponsive, then an alternate route may be proposed and/or selected in which little or no driver engagement may be required for at least a desired period of time”).
Regarding claim 19, Wang teaches all the limitation in claim 13. Wang further teaches wherein the device is further configured: based on the availability information, to predict a time interval during the journey from the starting point to the destination point, in which the driver will not be available for the manual longitudinal and/or lateral control of the vehicle; and to determine the driving route based on the clearance information such that the vehicle is located in the predicted time interval in a roadway section which has clearance for the automated driving mode (see at least col. 12, lines 41-51, “For example, by suggested alternate routes that may better comport with the driver's state or preferences (step 412). By way of example, in one embodiment, if a driver currently appears to be drowsy or relatively unresponsive, then an alternate route may be proposed and/or selected in which little or no driver engagement may be required for at least a desired period of time”).
Regarding claim 20, Wang teaches all the limitation in claim 13. Wang further teaches wherein the device is further configured to determine and/or predict the availability information based on:
sensor data with respect to the driver, wherein the sensor data were acquired by one or more driver sensors of the vehicle (see at least Figs. 1, 3, col. 12, lines 4-33, “In addition, in certain embodiments, driver state monitoring is utilized in step 408 to suggest a level of automation based on a driver state and/or driver preferences. For example, in certain embodiments, driver monitoring (e.g. using the motion sensors 322, internal cameras 326, eye/head sensors 327, steering wheel sensors 328 of FIG. 3 and/or other sensors 103 of FIG. 1) may be used to monitor a current level of alertness of the driver. For example, in certain embodiments, if the operator has his or her eyes closed, is resting his or her head in a sleeping-type position, and/or has eyes that are not presently and/or actively focusing on the road, then this may indicate that the driver is not particularly alert”);
data of an infotainment system of the vehicle (see at least col. 10, lines 5-45, “user input devices 320 (e.g. switches, gesture input devices, and so on, for example corresponding to the user interface 105 of FIG. 1)”; col. 11, lines 24-46, “Various data is obtained pertaining to the vehicle drive (step 404). In one embodiment, crowd-sourced monitoring and data analytics and historical information are obtained as part of the data of step 404. In various embodiments, the data includes various data pertaining to the vehicle operator's preferences and/or history (e.g. as to a general time for leaving for work or other destination, preferred routes, preferred levels of autonomous driving and/or driver engagement requirements at different times and/or locations, and so), as well as various data pertaining to the vehicle 100 (including operation of the autonomous driving functionality), the operator (e.g. driver) thereof, and the surrounding environment (e.g. roads, road conditions, traffic patterns, construction, weather, and so on), for example corresponding to the various inputs 302, 308, 310, and 340 of FIG. 3”, col.12, lines 52-59, “In certain embodiments, the route recommendations are actively prompted to the driver through the in-vehicle infotainment system, and a request is made asking for the driver's confirmation”);
data from a personal user device of the driver (see at least col. 10, lines 21-45, “personal devices 318 such as smart phones, tablets, or other electronic devices (e.g. corresponding to device 109 of FIG. 1)”).
Regarding claim 21, Wang teaches all the limitation in claim 13. Wang further teaches wherein the device is further configured to determine and/or predict the availability information based on:
sensor data with respect to the driver, wherein the sensor data were acquired by one or more interior cameras of the vehicle (see at least Figs. 1, 3, col. 12, lines 4-33, “In addition, in certain embodiments, driver state monitoring is utilized in step 408 to suggest a level of automation based on a driver state and/or driver preferences. For example, in certain embodiments, driver monitoring (e.g. using the motion sensors 322, internal cameras 326, eye/head sensors 327, steering wheel sensors 328 of FIG. 3 and/or other sensors 103 of FIG. 1) may be used to monitor a current level of alertness of the driver. For example, in certain embodiments, if the operator has his or her eyes closed, is resting his or her head in a sleeping-type position, and/or has eyes that are not presently and/or actively focusing on the road, then this may indicate that the driver is not particularly alert”);
data of an infotainment system of the vehicle (see at least col. 10, lines 5-45, “user input devices 320 (e.g. switches, gesture input devices, and so on, for example corresponding to the user interface 105 of FIG. 1)”; col. 11, lines 24-46, “Various data is obtained pertaining to the vehicle drive (step 404). In one embodiment, crowd-sourced monitoring and data analytics and historical information are obtained as part of the data of step 404. In various embodiments, the data includes various data pertaining to the vehicle operator's preferences and/or history (e.g. as to a general time for leaving for work or other destination, preferred routes, preferred levels of autonomous driving and/or driver engagement requirements at different times and/or locations, and so), as well as various data pertaining to the vehicle 100 (including operation of the autonomous driving functionality), the operator (e.g. driver) thereof, and the surrounding environment (e.g. roads, road conditions, traffic patterns, construction, weather, and so on), for example corresponding to the various inputs 302, 308, 310, and 340 of FIG. 3”, col.12, lines 52-59, “In certain embodiments, the route recommendations are actively prompted to the driver through the in-vehicle infotainment system, and a request is made asking for the driver's confirmation”);
data from a personal user device of the driver (see at least col. 10, lines 21-45, “personal devices 318 such as smart phones, tablets, or other electronic devices (e.g. corresponding to device 109 of FIG. 1)”).
Regarding claim 22, Wang teaches all the limitation in claim 13. Wang further teaches wherein the device is further configured:
based on digital map information with respect to the roadway network (see at least col. 5, lines 34-60, a global positioning system (GPS) server providing vehicle 100 location information, a weather service and/or other service and/or server providing information regarding weather conditions, road conditions, road construction, traffic patterns, and so on), to predict a probable duration for the driving route (see at least Fig. 2, col. 8, lines 43-67, col. 9, lines 1-3, durations of time of the respective driver engagement for the driving route); and
to determine and/or predict the availability information for the probable duration (see at least col. 6, lines 4-37, determine predicted levels of engagement required by the driver for a current vehicle drive based on the types of roadways to be encountered (e.g. highways versus roads with stop signs and street lights, paved versus unpaved roads, traffic on various roads, construction on various roads, conditions of various roads [e.g. potholes, coefficient of friction, and so on], lane restrictions on various roads, accidents or events on the various roads).
Regarding claim 24, Wang teaches all the limitation in claim 13. Wang further teaches wherein:
the device is further configured to output route information with respect to the determined driving route to the driver of the vehicle (see at least Fig. 2, col. 8, lines 29-42, “a presentation is provided of two exemplary displays 200, 202 (referred to as the first display 200 and the second display 202, respectively) that may be presented on the display unit 108 for a presentation that may be provided via the display unit 108 (e.g. for viewing within the vehicle 100). The first display 200 provides the predicted levels of engagement along a route being taken by the vehicle 100 that is embedded along with a map of the route. The second display 202 provides the predicted levels of engagement instead via a linear presentation (with respect to time)”); and
the route information indicates one or more time intervals and/or one or more roadway sections during the journey along the driving route in which the vehicle can be operated in the automated driving mode or in which the vehicle has to be operated in the manual driving mode (see at least Fig. 2, col. 8, lines 43-67, col. 9, lines 1-3, durations of time of the respective driver engagement in which the vehicle can be operated in the automated driving mode or in which the vehicle has to be operated in the manual driving mode for the selected driving route).
Regarding claim 25, Wang teaches all the limitation in claim 13. Wang further teaches wherein:
the device is further configured to determine digital map information with respect to the roadway network traveled by the vehicle (see at least col. 5, lines 34-60, a global positioning system (GPS) server providing vehicle 100 location information, a weather service and/or other service and/or server providing information regarding weather conditions, road conditions, road construction, traffic patterns, and so on), and to determine the driving route based on the digital map information (see at least Fig. 4, col. 11-12, determine the driving route based on the digital map information such as the types of roadways to be encountered (e.g. highways versus roads with stop signs and street lights, paved versus unpaved roads, traffic on various roads, construction on various roads, conditions of various roads [e.g. potholes, coefficient of friction, and so on], lane restrictions on various roads, accidents or events on the various roads, various weather conditions that may affect autonomous driving [e.g. snow, ice, rain, wind, fog, and so on], and/or various other factors that may affect autonomous driving); and
the digital map information comprises the clearance information for a plurality of different roadway sections of the roadway network (see at least col. 6, lines 4-37, “In various embodiments, the controller 106 determines the level of operator engagement required by analyzing how well the autonomous functionality and associated systems of the vehicle 100 are performing (e.g. how well the various sensors 103 are performing), as well as the types of roadways to be encountered (e.g. highways versus roads with stop signs and street lights, paved versus unpaved roads, traffic on various roads, construction on various roads, conditions of various roads [e.g. potholes, coefficient of friction, and so on], lane restrictions on various roads, accidents or events on the various roads, various weather conditions that may affect autonomous driving [e.g. snow, ice, rain, wind, fog, and so on], and/or various other factors that may affect autonomous driving)”).
Regarding claim 26, Wang teaches all the limitation in claim 13. Wang further teaches wherein the device is further configured, during the journey from the starting point to the destination point:
to determine and/or predict updated availability information with respect to the availability of the driver of the vehicle for the manual longitudinal and/or lateral control of the vehicle during the journey from a current location of the vehicle to the destination point (see at least Fig. 4, col. 12, lines 4-33, “driver state monitoring is utilized in step 408 to suggest a level of automation based on a driver state and/or driver preferences. For example, in certain embodiments, driver monitoring (e.g. using the motion sensors 322, internal cameras 326, eye/head sensors 327, steering wheel sensors 328 of FIG. 3 and/or other sensors 103 of FIG. 1) may be used to monitor a current level of alertness of the driver”); and
to adapt the driving route from the current location of the vehicle to the destination point based on the updated availability information (see at least Fig. 4, col. 12, lines 41-59, suggest alternate route from the current location of the vehicle to the destination point based on the current driver state of the drive).
Regarding claim 27, Wang teaches all the limitation in claim 13. Wang further teaches wherein the device is further configured, during the journey from the starting point to the destination point, repeatedly and/or periodically (see at least Fig. 4, col. 11-12, steps 406-408-414-404 are performed repeatedly after receiving a destination entry at step 402):
to determine and/or predict updated availability information with respect to the availability of the driver of the vehicle for the manual longitudinal and/or lateral control of the vehicle during the journey from a current location of the vehicle to the destination point (see at least Fig. 4, col. 12, lines 4-33, “driver state monitoring is utilized in step 408 to suggest a level of automation based on a driver state and/or driver preferences. For example, in certain embodiments, driver monitoring (e.g. using the motion sensors 322, internal cameras 326, eye/head sensors 327, steering wheel sensors 328 of FIG. 3 and/or other sensors 103 of FIG. 1) may be used to monitor a current level of alertness of the driver”); and
to adapt the driving route from the current location of the vehicle to the destination point based on the updated availability information (see at least Fig. 4, col. 12, lines 41-59, suggest alternate route from the current location of the vehicle to the destination point based on the current driver state of the drive).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 10198009 B2, hereinafter “Wang”) as applied to claim 13 above, and further in view of Konrardy et al. (US 11441916 B1, hereinafter “Konrardy”).
Regarding claim 23, Wang teaches all the limitation in claim 13. Wang fails to specifically teach wherein the device is further configured: to determine the driving route based on a routing algorithm, in which for each possible roadway section of different possible roadway sections of the roadway network, a section weight is taken into consideration, wherein the section weight indicates a value of the respective possible roadway section for the driving route; and to determine the section weights of the different possible roadway sections based on the availability information.
Konrardy teaches, see at least Figs. 1A, 5, 7, col. 37, lines 19-44, col. 39, lines 23-43, col. 47, lines 22-64, a control system of an autonomous vehicle 10 wherein the control system is configured to determine a driving route based on calculating risks associated with a level of autonomous or semi-autonomous operation, wherein machine learning techniques maybe used to identify or estimate the magnitude of salient risk factors associated with autonomous operation feature use on the road segment, e.g. time of day, weather conditions, traffic conditions, speed, type of vehicle, types of sensors used by the vehicle, types of autonomous operation features in use, versions of autonomous operation features, interactions between autonomous operation features, autonomous operation feature settings or configurations, driver behavior, or other similar factors that may be derived from the data.
In view of Konrardy's teachings, it would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to include, with the Wang's apparatus, wherein the device is further configured: to determine the driving route based on a routing algorithm, in which for each possible roadway section of different possible roadway sections of the roadway network, a section weight is taken into consideration, wherein the section weight indicates a value of the respective possible roadway section for the driving route; and to determine the section weights of the different possible roadway sections based on the availability information, with a reasonable expectation of success. This modification would allow to generate a plurality of map tiles including indications of the scores of road segments to be displayed to a user.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TRANG DANG whose telephone number is (703)756-1049. The examiner can normally be reached Monday-Friday 8:00-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Khoi Tran can be reached at (571)272-6919. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/TRANG DANG/ Examiner, Art Unit 3656 /KHOI H TRAN/Supervisory Patent Examiner, Art Unit 3656