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 .
Status of Claims
Claims 1-20 are pending in this application.
Claims 1-9 and 12-20 are presented as currently amended claims.
Claims 10-11 are presented as original claims.
No claims are newly presented.
No claims are newly cancelled.
Claim Objections
Claim 20 is objected to because of the following informalities: the word “the” appears twice consecutively. Appropriate correction is required.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-3, 5-7, 9-11, 13-15, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Beaurepaire et al. (US 20220119010 A1) in view of Shenoy et al. (US 10625674 B2) (the combination of which will be referred to as 'combination Beaurepaire' hereinafter). As regards the individual claims:
Regarding claim 1, Beaurepaire teaches a:
vehicle avoidance computer system (Beaurepaire: ¶ 007; computer program code configured to, with the at least one processor, cause the at least one processor to: receive a request for routing instructions for a vehicle) for detecting, alerting, and avoidance of road hazards, the vehicle avoidance computer system (Beaurepaire: ¶ 022; methods for predicting, reacting to, and avoiding high risk areas.) comprising at least one processor in communication with at least one memory device, wherein the at least one processor programmed to: (Beaurepaire: ¶ 007; processor; and at least one memory including computer program code) receive first sensor data associated with a plurality of roadways; detect an obstacle in a first roadway of the plurality of roadways based upon the first sensor data; (Beaurepaire: ¶ 064; system 121 may identify multiple different possible hazardous locations for the vehicle based on, for example, the data acquired from the first vehicle at act A110, other vehicles, sensors, and historical data) (Beaurepaire: ¶ 071; puddle of standing water may be identified by a vehicle and stored in the geographic database 123) . . . and (ii) to cause rerouting of the travel path of a corresponding vehicle of the determined plurality of vehicles to avoid the obstacle. (Beaurepaire: ¶ 138; server 125 is configured to predict the risk of hydroplaning at specific location(s) and design/generate routes or paths in light the risk.)
Beaurepaire is silent or does not teach: . . . determine, based upon second sensor data of sensors of a plurality of vehicles traveling on the first roadway, that the plurality of vehicles each have a travel path along a route associated with a respective threat level of impact with the obstacle in the first roadway; and transmit information about the obstacle to each of the determined plurality of vehicles including instructions for each of the determined plurality of vehicles to avoid impact with the obstacle wherein the instructions are configured (i) based upon a respective threat level and respective second sensor data for each of the determined plurality of vehicles; . . . however, Shenoy does teach:
. . . determine, based upon second sensor data of sensors (Shenoy: ¶ 014; Col. 3, Lns. 47-49; received set of data may comprise sensor data of the one or more vehicles) of a plurality of vehicles traveling on the first roadway, (Shenoy: [514a – 514c])that the plurality of vehicles each have a travel path along a route associated with a respective threat level of impact with the obstacle in the first roadway; (Shenoy: ¶ 015; Col. 3, Lns. 64-65; sensor that generates the sensor data of the one or more vehicles and/or detects the obstacle information along the path.) and transmit information about the obstacle to each of the determined plurality of vehicles (Shenoy: ¶ 101; Col. 24, Lns. 32-39; CPU of each of the first set of vehicles 514, may be configured to update the corresponding MSDs generated by each of the first set of vehicles 514.) including instructions for each of the determined plurality of vehicles to avoid impact with the obstacle (Shenoy: ¶ 014; Col. 3, Lns. 44-49; method for generation of a preventive alert. The method may comprise receipt of a set of data from one or more vehicles. The received set of data may comprise sensor data of the one or more vehicles, road conditions of a path, and/or obstacle information along a path of the one or more vehicles) wherein the instructions are configured (i) based upon a respective threat level (Shenoy: ¶ 069; Col. 17, Lns. 54-58; other values may further indicate danger level information of the one or more stationary and/or slow-moving obstacles. Such danger level information may comprise a predicted likelihood for a collision) and respective second sensor data for each of the determined plurality of vehicles (Shenoy: ¶ 014; Col. 3, Lns. 47-49; received set of data may comprise sensor data of the one or more vehicles) . . .
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Shenoy with the teachings of Beaurepaire because doing so would result in the predicable benefit of improving road safety because it is “desirable to provide a real-time, preventive alert to the drivers of a set of vehicles approaching towards a hazardous portion of a road.” (Shenoy: ¶ 003; Col 1, Lns. 38-40).
Regarding claim 2, as detailed above, combination Beaurepaire teaches the invention as detailed with respect to claim 1. Beaurepaire further teaches:
wherein the determined plurality of vehicles are configured to activate at least one alert action to notify a driver of the corresponding vehicle of the obstacle in the first roadway. (Beaurepaire: ¶ 023; vehicles may issue warnings for the driver based on the position of the vehicle either on a roadway or within a road network system)
Regarding claim 3, as detailed above, combination Beaurepaire teaches the invention as detailed with respect to claim 1. Beaurepaire further discloses:
wherein the determined plurality of vehicles are configured to activate at least one autonomous and/or semi-autonomous control system in the corresponding vehicle to react to the obstacle in the first roadway. (Beaurepaire: ¶ 032; autonomous vehicle or HAD may be configured to receive routing instructions from a mapping system 121 and automatically perform an action in furtherance of the instructions)
Regarding claim 5, as detailed above, combination Beaurepaire teaches the invention as detailed with respect to claim 1. Beaurepaire further discloses:
wherein the computer system receives the first sensor data from sensors of a plurality of vehicles traveling on the plurality of roadways (Beaurepaire: ¶ 064; mapping system 121 may identify multiple different possible hazardous locations for the vehicle based on, for example, the data acquired from the first vehicle at act A110, other vehicles, sensors, and historical data. The risk model generated at A120 may be kept up to date by acquiring real time data from vehicles as they pass by or traverse each hazardous location)
Regarding claim 6, as detailed above, combination Beaurepaire teaches the invention as detailed with respect to claim 1. Beaurepaire further discloses:
wherein the computer system receives sensor data from one or more infrastructure sensors associated with the plurality of roadways. (Beaurepaire: ¶ 036; mapping system 121 may be configured to acquire and process data relating to roadway or vehicle conditions. For example, the mapping system 121 may receive and input data such as vehicle data, user data, weather data, road condition data, road works data, traffic feeds, etc. The data may be historical, real-time, or predictive.)
(Beaurepaire: ¶ 047; data may include water level estimates based on weather or human related events (burst pipe, open hydrant, etc.))
Regarding claim 7, as detailed above, combination Beaurepaire teaches the invention as detailed with respect to claim 1. Beaurepaire further discloses:
wherein the at least one processor is further programmed to determine the plurality of vehicles traveling on the first roadway where the obstacle in the first roadway potentially will impact each of the plurality of vehicles based upon the travel paths for each of the plurality of vehicles (Beaurepaire: ¶ 061; a puddle of standing water may be identified by the first vehicle and stored in the geographic database 123 using the lane geometry provided by the lane model layer.) (Beaurepaire: ¶ 064; system 121 applies the risk model to the second vehicle for the portion of the lane of the roadway. The mapping system 121 may identify multiple different possible hazardous locations for the vehicle based on, for example, the data acquired from the first vehicle)
And Shenoy teaches: and a respective proximity of each of the plurality of vehicles to the obstacle (Shenoy: Clm. 6; alert information is indicative of a threat of said collision in at least one of a threshold distance from said first vehicle, or said path of travel of said first vehicle.) (Examiner note: where each participant vehicle [514a – 514c] can operate as a first vehicle from its perspective)
Regarding claim 9, Beaurepaire teaches a computer-based method for
implemented on a computer device including at least one processor in communication with at least one memory device, the method comprising: (Beaurepaire: ¶ 029; mapping system 121 may include a database 123 (also referred to as a geographic database 123 or map database) and a server) receiving sensor data associated with a plurality of roadways; detecting an obstacle in a first roadway of the plurality of roadways based upon the first sensor data; (Beaurepaire: ¶ 064; system 121 may identify multiple different possible hazardous locations for the vehicle based on, for example, the data acquired from the first vehicle at act A110, other vehicles, sensors, and historical data) (Beaurepaire: ¶ 071; puddle of standing water may be identified by a vehicle and stored in the geographic database 123) . . . and (ii) to cause rerouting of the travel path of a corresponding vehicle of the determined plurality of vehicles to avoid the obstacle. (Beaurepaire: ¶ 138; server 125 is configured to predict the risk of hydroplaning at specific location(s) and design/generate routes or paths in light the risk.)
Beaurepaire is silent or does not teach: . . . determining, based upon second sensor data of sensors of a plurality of vehicles traveling on the first roadway, that the plurality of vehicles each have a travel path along a route associated with a respective threat level of impact with the obstacle in the first roadway; and transmitting information about the obstacle to each of the determined plurality of vehicles including instructions for each of the determined plurality of vehicles to avoid impact with the obstacle, wherein the instructions are configured (i) based upon a respective threat level and respective second sensor data for each of the determined plurality of vehicles. . . ; . . . however, Shenoy does teach:
determining, based upon second sensor data of sensors (Shenoy: ¶ 014; Col. 3, Lns. 47-49; received set of data may comprise sensor data of the one or more vehicles)of a plurality of vehicles traveling on the first roadway, (Shenoy: [514a – 514c])that the plurality of vehicles each have a travel path along a route associated with a respective threat level of impact with the obstacle in the first roadway; (Shenoy: ¶ 015; Col. 3, Lns. 64-65; sensor that generates the sensor data of the one or more vehicles and/or detects the obstacle information along the path.) and transmitting information about the obstacle to each of the determined plurality of vehicles (Shenoy: ¶ 101; Col. 24, Lns. 32-39; CPU of each of the first set of vehicles 514, may be configured to update the corresponding MSDs generated by each of the first set of vehicles 514.) including instructions for each of the determined plurality of vehicles to avoid impact with the obstacle, (Shenoy: ¶ 014; Col. 3, Lns. 44-49; method for generation of a preventive alert. The method may comprise receipt of a set of data from one or more vehicles. The received set of data may comprise sensor data of the one or more vehicles, road conditions of a path, and/or obstacle information along a path of the one or more vehicles) wherein the instructions are configured (i) based upon a respective threat level (Shenoy: ¶ 069; Col. 17, Lns. 54-58; other values may further indicate danger level information of the one or more stationary and/or slow-moving obstacles. Such danger level information may comprise a predicted likelihood for a collision)and respective second sensor data for each of the determined plurality of vehicles (Shenoy: ¶ 014; Col. 3, Lns. 47-49; received set of data may comprise sensor data of the one or more vehicles) . . .
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Shenoy with the teachings of Beaurepaire because doing so would result in the predicable benefit of improving road safety because it is “desirable to provide a real-time, preventive alert to the drivers of a set of vehicles approaching towards a hazardous portion of a road.” (Shenoy: ¶ 003; Col 1, Lns. 38-40).
Regarding claim 10, as detailed above, combination Beaurepaire teaches the invention as detailed with respect to claim 9. Beaurepaire further discloses:
wherein the determined plurality of vehicles are configured to activate at least one alert action to notify a driver of the corresponding vehicle of the obstacle in the first roadway. (Beaurepaire: ¶ 023; vehicles may issue warnings for the driver based on the position of the vehicle either on a roadway or within a road network system)
Regarding claim 11, as detailed above, combination Beaurepaire teaches the invention as detailed with respect to claim 9. Beaurepaire further discloses:
wherein the determined plurality of vehicles are configured to activate at least one autonomous and/or semi-autonomous control systems in the corresponding vehicle to react to the obstacle in the first roadway. (Beaurepaire: ¶ 032; autonomous vehicle or HAD may be configured to receive routing instructions from a mapping system 121 and automatically perform an action in furtherance of the instructions)
Regarding claim 13, as detailed above, combination Beaurepaire teaches the invention as detailed with respect to claim 9. Beaurepaire further discloses:
wherein the computer device receives the first sensor data from sensors of a plurality of roadways. (Beaurepaire: ¶ 064; mapping system 121 may identify multiple different possible hazardous locations for the vehicle based on, for example, the data acquired from the first vehicle at act A110, other vehicles, sensors, and historical data. The risk model generated at A120 may be kept up to date by acquiring real time data from vehicles as they pass by or traverse each hazardous location)
Regarding claim 14, as detailed above, combination Beaurepaire teaches the invention as detailed with respect to claim 9. Beaurepaire further discloses:
wherein the computer device receives the first sensor data from one or more infrastructure sensors associated with the plurality of roadways. (Beaurepaire: ¶ 036; mapping system 121 may be configured to acquire and process data relating to roadway or vehicle conditions. For example, the mapping system 121 may receive and input data such as vehicle data, user data, weather data, road condition data, road works data, traffic feeds, etc. The data may be historical, real-time, or predictive.) (Beaurepaire: ¶ 047; data may include water level estimates based on weather or human related events (burst pipe, open hydrant, etc.))
Regarding claim 15, as detailed above, combination Beaurepaire teaches the invention as detailed with respect to claim 9. Beaurepaire further discloses:
further comprising determining the plurality of vehicles traveling on the first roadway where the obstacle in the first roadway potentially will impact each of the plurality of vehicles based upon travel paths for each of the plurality of vehicles. (Beaurepaire: ¶ 061; a puddle of standing water may be identified by the first vehicle and stored in the geographic database 123 using the lane geometry provided by the lane model layer.) (Beaurepaire: ¶ 064; system 121 applies the risk model to the second vehicle for the portion of the lane of the roadway. The mapping system 121 may identify multiple different possible hazardous locations for the vehicle based on, for example, the data acquired from the first vehicle)
And Shenoy teaches: and a respective proximity of each of the plurality of vehicles to the obstacle. (Shenoy: Clm. 6; alert information is indicative of a threat of said collision in at least one of a threshold distance from said first vehicle, or said path of travel of said first vehicle.) (Examiner note: where each participant vehicle [514a – 514c] can operate as a first vehicle from its perspective)
Regarding claim 17, Beaurepaire teaches a non-transitory computer-readable storage medium having computer-executable instructions embodied thereon
wherein when executed by a processor associated with a vehicle avoidance computer system (Beaurepaire: ¶ 007; computer) configured for detecting, alerting, and avoidance of road hazards and coupled to at least memory device, (Beaurepaire: ¶ 007; determine, the risk assessment exceeds an operating threshold for the vehicle; generate a virtual lane that avoids the portion of the existing lane) the computer- executable instructions cause the processor to: receive first sensor data associated with a plurality of roadways; (Beaurepaire: ¶ 029; mapping system 121 may include a database 123 (also referred to as a geographic database 123 or map database) and a server) detect an obstacle in a first roadway of the plurality of roadways based upon the first sensor data; (Beaurepaire: ¶ 064; system 121 may identify multiple different possible hazardous locations for the vehicle based on, for example, the data acquired from the first vehicle at act A110, other vehicles, sensors, and historical data) (Beaurepaire: ¶ 071; puddle of standing water may be identified by a vehicle and stored in the geographic database 123) . . . and (ii) to cause rerouting of the travel path of a corresponding vehicle of the determined plurality of vehicles to avoid the obstacle (Beaurepaire: ¶ 138; server 125 is configured to predict the risk of hydroplaning at specific location(s) and design/generate routes or paths in light the risk.)
Beaurepaire is silent or does not teach: . . . determine, based upon second sensor data of sensors of a plurality of vehicles traveling on the first roadway, that the plurality of vehicles each have a travel path along a route associated with a respective threat level of impact with the obstacle in the first roadway; and transmit information about the obstacle to each of the determined plurality of vehicles including instructions for each of the determined plurality of vehicles to avoid impact with the obstacle, wherein the instructions are configured (i) based upon a respective threat level and respective second sensor data for each of the determined plurality of vehicles . . . however, Shenoy does teach:
. . . determine, based upon second sensor data of sensors (Shenoy: ¶ 014; Col. 3, Lns. 47-49; received set of data may comprise sensor data of the one or more vehicles)of a plurality of vehicles traveling on the first roadway, (Shenoy: [514a – 514c])that the plurality of vehicles each have a travel path along a route associated with a respective threat level of impact with the obstacle in the first roadway; (Shenoy: ¶ 015; Col. 3, Lns. 64-65; sensor that generates the sensor data of the one or more vehicles and/or detects the obstacle information along the path.)and transmit information about the obstacle to each of the determined plurality of vehicles(Shenoy: ¶ 101; Col. 24, Lns. 32-39; CPU of each of the first set of vehicles 514, may be configured to update the corresponding MSDs generated by each of the first set of vehicles 514.) including instructions for each of the determined plurality of vehicles to avoid impact with the obstacle, (Shenoy: ¶ 014; Col. 3, Lns. 44-49; method for generation of a preventive alert. The method may comprise receipt of a set of data from one or more vehicles. The received set of data may comprise sensor data of the one or more vehicles, road conditions of a path, and/or obstacle information along a path of the one or more vehicles) wherein the instructions are configured (i) based upon a respective threat level (Shenoy: ¶ 069; Col. 17, Lns. 54-58; other values may further indicate danger level information of the one or more stationary and/or slow-moving obstacles. Such danger level information may comprise a predicted likelihood for a collision)and respective second sensor data for each of the determined plurality of vehicles (Shenoy: ¶ 014; Col. 3, Lns. 47-49; received set of data may comprise sensor data of the one or more vehicles). . .
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Shenoy with the teachings of Beaurepaire because doing so would result in the predicable benefit of improving road safety because it is “desirable to provide a real-time, preventive alert to the drivers of a set of vehicles approaching towards a hazardous portion of a road.” (Shenoy: ¶ 003; Col 1, Lns. 38-40).
Regarding claim 18, as detailed above, combination Beaurepaire teaches the invention as detailed with respect to claim 17. Beaurepaire further discloses:
wherein the determined plurality of vehicles are configured to activate at least one alert action to notify a driver of the corresponding vehicle of the obstacle in the first roadway. (Beaurepaire: ¶ 023; vehicles may issue warnings for the driver based on the position of the vehicle either on a roadway or within a road network system)
Regarding claim 19, as detailed above, combination Beaurepaire teaches the invention as detailed with respect to claim 17. Beaurepaire further discloses:
wherein the determined plurality of vehicles are configured to activate at least one autonomous and/or semi-autonomous control system in the corresponding vehicle to react to the obstacle in the first roadway. (Beaurepaire: ¶ 032; autonomous vehicle or HAD may be configured to receive routing instructions from a mapping system 121 and automatically perform an action in furtherance of the instructions)
Claims 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over combination Beaurepaire as applied to claims 1 and 9 respectively, and further in view of Zeplin et al. (US 20200098253 A1). As regards the individual claims:
Regarding claim 8, as detailed above, combination Beaurepaire teaches the invention as detailed with respect to claim 1. Beaurepaire does not explicitly teach:
wherein the obstacle is an emergency vehicle, and wherein the at least one processor is further programmed to: determine a route for the emergency vehicle; detect a plurality of vehicles along the route for the emergency vehicle; and
transmit instructions to the determined plurality of vehicles to clear a path for the emergency vehicle; however, Zeplin does teach:
wherein the obstacle is an emergency vehicle, and wherein the at least one processor is further programmed to: determine a route for the emergency vehicle; (Zeplin: ¶¶ 17-18; providing the current position and direction vector data of at least one emergency vehicle; c) predicting a travel route for at least one emergency vehicle from its current position to the position of the destination, taking into account the travel direction selected by the driver of the emergency vehicle). detect a plurality of vehicles along the route for the emergency vehicle; and (Zeplin: ¶ 015; influencing the autonomous vehicle moving in the projected direction of travel on the forecast route of travel in such a way that the respective autonomous vehicle changes or has changed its speed of travel and/or its direction of travel before or at the predicted meeting with at least one emergency vehicle in such a way that the traffic in the direction of the forecast route of travel, in particular the emergency vehicle, is not obstructed by the autonomous vehicle.) transmit instructions to the determined plurality of vehicles to clear a path for the emergency vehicle. (Zeplin: ¶ 060; After the authentication information has been checked by the traffic control computer 3, it influences the relevant driving data of the autonomous vehicles on this route or in the vicinity along the projected driving route for the projected driving direction R of the emergency vehicle 2. For this purpose, the current position data and the travel direction vector of at least one emergency vehicle 2, in addition to being transmitted to the traffic control computer 3 or to a computer functionally connected to the traffic control computer 3, are transmitted directly or indirectly to at least one autonomous vehicle 60 located on the projected travel route of the emergency vehicle)
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Zeplin with the teachings of Beaurepaire because doing so would result in the predicable benefit of “enable[ing] even shorter travel times to the scene of action” by an emergency vehicle. (Zeplin: ¶ 013).
Regarding claim 16, as detailed above, combination Beaurepaire teaches the invention as detailed with respect to claim 9. Beaurepaire does not explicitly teach:
wherein the obstacle is an emergency vehicle, and wherein the method further comprises: determining a route for the emergency vehicle; detecting a plurality of vehicles along the route for the emergency vehicle; and transmitting instructions to the determined plurality of vehicles to clear a path for the emergency vehicle; however, Zeplin does teach:
wherein the obstacle is an emergency vehicle, and wherein the method further comprises: determining a route for the emergency vehicle; (Zeplin: ¶¶ 17-18; providing the current position and direction vector data of at least one emergency vehicle; c) predicting a travel route for at least one emergency vehicle from its current position to the position of the destination, taking into account the travel direction selected by the driver of the emergency vehicle) detecting a plurality of vehicles along the route for the emergency vehicle; and (Zeplin: ¶ 015; influencing the autonomous vehicle moving in the projected direction of travel on the forecast route of travel in such a way that the respective autonomous vehicle changes or has changed its speed of travel and/or its direction of travel before or at the predicted meeting with at least one emergency vehicle in such a way that the traffic in the direction of the forecast route of travel, in particular the emergency vehicle, is not obstructed by the autonomous vehicle.) transmitting instructions to the determined plurality of vehicles to clear a path for the emergency vehicle. (Zeplin: ¶ 060; After the authentication information has been checked by the traffic control computer 3, it influences the relevant driving data of the autonomous vehicles on this route or in the vicinity along the projected driving route for the projected driving direction R of the emergency vehicle 2. For this purpose, the current position data and the travel direction vector of at least one emergency vehicle 2, in addition to being transmitted to the traffic control computer 3 or to a computer functionally connected to the traffic control computer 3, are transmitted directly or indirectly to at least one autonomous vehicle 60 located on the projected travel route of the emergency vehicle)
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Zeplin with the teachings of Beaurepaire because doing so would result in the predicable benefit of “enable[ing] even shorter travel times to the scene of action” by an emergency vehicle. (Zeplin: ¶ 013).
Claims 4, 12, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over combination Beaurepaire as applied to claims 1, 9, and 17 respectively, and further in view of Zeplin et al. (US 20200098253 A1). As regards the individual claims:
Regarding claim 4, as detailed above, combination Beaurepaire teaches the invention as detailed with respect to claim 1. Beaurepaire is silent or does not teach:
wherein the threat levels are determined by a machine learning model associated with the at least one processor and the instructions included in the information about the obstacle a reroute the travel path of the corresponding vehicle to avoid the obstacle based upon the respective determined threat level; however, Zhao does teach:
wherein the threat levels are determined by a machine learning model associated with the at least one processor (Zhao: ¶ 073; machine learning modules 140 use the sensor data 148 (which are not labeled with objects) to increase their accuracy of predictions in detecting objects. For example, supervised and/or unsupervised machine learning algorithms may be used to validate the predictions of the object detection machine learning modules 140 in detecting objects in the sensor data 148) and the instructions included in the information about the obstacle a reroute the travel path of the corresponding vehicle to avoid the obstacle based upon the respective determined threat level. (Zhao: ¶ 201; planning module 962 may include behavioral decision making 966 to determine driving actions (e.g., steering, braking, throttle) in response to determining changing conditions on the road (e.g., traffic light turned yellow, or the autonomous vehicle is in an unsafe driving condition because another vehicle drove in front of the autonomous vehicle and in a region within a pre-determined safe distance of the location of the autonomous vehicle). The planning module 962 performs trajectory generation 968 and selects a trajectory from the set of trajectories determined by the navigation planning operation 964)
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Zhao with the teachings of Beaurepaire because doing so would result in the predicable benefit of better responding to “unexpected traffic . . . on the road ahead.” (Zhao: ¶ 002).
Regarding claim 12, as detailed above, combination Beaurepaire teaches the invention as detailed with respect to claim 9. Beaurepaire is silent or does not teach:
wherein the threat levels are determined by a machine learning model associated with the at least one processor and the instructions included in the information about the obstacle a reroute the travel path of the corresponding vehicle to avoid the obstacle based upon the respective determined threat level; however, Zhao does teach:
wherein the threat levels are determined by a machine learning model associated with the at least one processor (Zhao: ¶ 073; machine learning modules 140 use the sensor data 148 (which are not labeled with objects) to increase their accuracy of predictions in detecting objects. For example, supervised and/or unsupervised machine learning algorithms may be used to validate the predictions of the object detection machine learning modules 140 in detecting objects in the sensor data 148)and the instructions included in the information about the obstacle a reroute the travel path of the corresponding vehicle to avoid the obstacle based upon the respective determined threat level. (Zhao: ¶ 201; planning module 962 may include behavioral decision making 966 to determine driving actions (e.g., steering, braking, throttle) in response to determining changing conditions on the road (e.g., traffic light turned yellow, or the autonomous vehicle is in an unsafe driving condition because another vehicle drove in front of the autonomous vehicle and in a region within a pre-determined safe distance of the location of the autonomous vehicle). The planning module 962 performs trajectory generation 968 and selects a trajectory from the set of trajectories determined by the navigation planning operation 964)
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Zhao with the teachings of Beaurepaire because doing so would result in the predicable benefit of better responding to “unexpected traffic . . . on the road ahead.” (Zhao: ¶ 002).
Regarding claim 20, as detailed above, combination Beaurepaire teaches the invention as detailed with respect to claim 17. Beaurepaire is silent or does not teach:
wherein the threat levels are determined by a machine learning model associated with the processor and the instructions included in the information about the obstacle reroute the travel path of the corresponding vehicle to avoid the obstacle based upon the respective determined threat level.; however, Zhao does teach:
wherein the threat levels are determined by a machine learning model associated with the processor (Zhao: ¶ 073; machine learning modules 140 use the sensor data 148 (which are not labeled with objects) to increase their accuracy of predictions in detecting objects. For example, supervised and/or unsupervised machine learning algorithms may be used to validate the predictions of the object detection machine learning modules 140 in detecting objects in the sensor data 148) and the instructions included in the information about the obstacle reroute the travel path of the corresponding vehicle to avoid the obstacle based upon the respective determined threat level (Zhao: ¶ 201; planning module 962 may include behavioral decision making 966 to determine driving actions (e.g., steering, braking, throttle) in response to determining changing conditions on the road (e.g., traffic light turned yellow, or the autonomous vehicle is in an unsafe driving condition because another vehicle drove in front of the autonomous vehicle and in a region within a pre-determined safe distance of the location of the autonomous vehicle). The planning module 962 performs trajectory generation 968 and selects a trajectory from the set of trajectories determined by the navigation planning operation 964)
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Zhao with the teachings of Beaurepaire because doing so would result in the predicable benefit of better responding to “unexpected traffic . . . on the road ahead.” (Zhao: ¶ 002).
Response to Arguments
Applicant's remarks filed December 31, 2025 have been fully considered.
Applicant’s argument and amendments with respect to the previous applied 35 U.S.C. § 101 rejection is persuasive and the rejection is hereby withdrawn.
Applicant’s arguments with respect to claim 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Applicant argues that
Beaurepaire does not describe or suggest at least "transmit information about the obstacle to each of the determined plurality of vehicles including instructions for each of the determined plurality of vehicles to avoid impact with the obstacle, wherein the instructions are configured (i) based upon a respective threat level and respective second sensor data for each of the determined plurality of vehicles and (ii) to cause rerouting of the travel path of a corresponding vehicle of the determined plurality of vehicles to avoid the obstacle," as recited in amended Claim 1. (Applicant’s Arguments filed December 31, 2025, pg. 13).
Newly applied art Shenoy et al. (US 10625674 B2) teaches a multi-vehicle warning system that transmits sensor data, obstacle location information, and path information to each of the vehicles in an area around the ego vehicle. Shenoy considers a danger level determined by the risk of collision with the obstacle and only warns based on a threshold distance and a determined risk. A person of ordinary skill in the art would be taught or suggested
determine, based upon second sensor data of sensors of a plurality of vehicles traveling on the first roadway, that the plurality of vehicles each have a travel path along a route associated with a respective threat level of impact with the obstacle in the first roadway; and transmit information about the obstacle to each of the determined plurality of vehicles including instructions for each of the determined plurality of vehicles to avoid impact with the obstacle, wherein the instructions are configured (i) based upon a respective threat level and respective second sensor data for each of the determined plurality of vehicles and (ii) to cause rerouting of the travel path of a corresponding vehicle of the determined plurality of vehicles to avoid the obstacle.
by the combination of Beaurepaire and Shenoy because substituting Shenoy’s warning element for Beaurepaire rerouting element would have been obvious because simple substitution of one known element for another to obtain predictable results is obvious. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007). Consequently, Applicant’s arguments and amendments are not persuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure Raichelgauz et al. (US 20220041184 A1) which discloses a method for detecting obstacles by processing visual information related to vehicle maneuvering that is suspected as being obstacle avoidance maneuvers and transmitting the results of the analysis to a server.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES PALL whose telephone number is (571)272-5280. The examiner can normally be reached M-F 9:30 - 18:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Angela Ortiz can be reached at 571-272-1206. 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.
/C.P./Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663