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 .
Examiner Notes that the fundamentals of the rejections are based on the broadest reasonable interpretation of the claim language. Applicant is kindly invited to consider the reference as a whole. References are to be interpreted as by one of ordinary skill in the art rather than as by a novice. See MPEP 2141. Therefore, the relevant inquiry when interpreting a reference is not what the reference expressly discloses on its face but what the reference would teach or suggest to one of ordinary skill in the art.
Status of the Claims
This is a Final Office Action in response to Applicant’s amendment of 29 September 2025. Claims 1-20 are pending and have been considered as follows.
Response to Amendment and/or Argument
Applicant’s amendments and/or arguments with respect to the Claim Rejections of Claims 1-20 under 35 U.S.C. 112(b) as set forth in the office action 28 May 2025 have been considered and are persuasive. Therefore, the Claim Rejections of Claims 1-20 under 35 U.S.C. 112(b) as set forth in the office action 28 May 2025 have been withdrawn.
Applicant’s amendments and/or arguments with respect to the Claim Rejections of Claims 1-20 under 35 U.S.C. 101 as set forth in the office action 28 May 2025 have been considered and are persuasive. Therefore, the Claim Rejections of Claims 1-20 under 35 U.S.C.101 as set forth in the office action 28 May 2025 have been withdrawn.
Applicant’s arguments with respect to claim(s) 1, 13 and 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.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3-12 and 15-19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 3 (Similarly claim 15) recites limitation in lines 2-3 “third difference data describing a difference between the first predicted route and the first predicted route” is indefinite because it is unclear and confusing to the Examiner why would there be a difference data if a difference is compared between two IDENTICAL routes (namely, first predicted route and first predicted route)? Accordingly, this limitation renders the claim to be indefinite.
The dependent claims 4-12 and 16-19 are also rejected under 112 second paragraph by the fact that they are dependent upon the rejected independent claims 3 and 15.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-11 and 13- 20 are rejected under 35 U.S.C. 103 as being unpatentable over Rao (WO 2020/142548 A1) in view of Iagnemma (US 2017/0356751 A1).
Regarding Claim 1, Rao teaches A system for providing transportation services (see at least Abstract), comprising:
a service assignment system comprising at least one processor (see at least Fig. 1-4, 10 [0043]: routing and mapping system provided by a server or autonomous vehicles to prioritize various routes between a pick up and drop off locations), the service assignment system being programmed to perform operations comprising:
receiving, at the service assignment system, a transportation service request from a user, the transportation service request describing a transportation service having a start location and an end location; (see at least Fig. 2 [0016, 0044-0053]: user initiate a routing a request for transport from a first location to a second location)
selecting, by the service assignment system, a first set of candidate autonomous vehicles (AVs) of a first AV type, the first set of candidate AVs capable of executing the transportation service, the first set of candidate AVs comprising a first AV; (See at least Fig. 1-9 [0035-0074]: the coordination and selection of the type of vehicle to be matched to a consumer can be based on a plurality of factors including the customer profile, the route between origination and destination, and the capabilities of the autonomous vehicle. An optimization function is enabled to capture the data from the available vehicle characteristics to determine a list of matching vehicles (e.g. autonomous trucks, buses, vehicles)various types of AV may be part of a ridesharing services, car sharing service, a fleet, a distributed network of a variety of vehicles).)
selecting, by the service assignment system, the first AV based on the first predicted route for executing the transportation service; (see at least Fig. 1-8 [0040-0092]:determining an autonomous vehicle based on one or more capabilities of the AV and selecting a preferred navigation route of the plurality of navigation routes and communicating instructions to the autonomous vehicle to proceed on the selected preferred navigational route.) and
transmitting instruction data from the service assignment system to the first AV, the instruction data causing the first AV to begin executing the transportation service based on the first predicted route. (see at least Fig. 1-8 [0040-0092]:: determining an autonomous vehicle based on one or more capabilities of the AV and selecting a preferred navigation route of the plurality of navigation routes and communicating instructions to the autonomous vehicle to proceed on the selected preferred navigational route)
it may be alleged that Rao does not explicitly teach determining, by the service assignment system, a first predicted route for the first AV using vehicle capability data describing autonomous vehicle operational capabilities of the first AV type and first difference data, the first difference data indicating at least one roadway element difference between a previous predicted route generated by the service assignment system for a previous AV of the first AV type and a previous planned route generated by and received from the previous AV of the first AV type, the previous planned route describing a transportation service route generated by and planned for the previous AV;
Iagnemma is directed to system and method for evaluating and selecting routes for autonomous vehicles by filtering out road segments that vehicle cannot safely traverse based on vehicle’s specific capabilities, sensor performances and roadway/environmental conditions, Iagnemma teaches determining, by the service assignment system, a first predicted route for the first AV using vehicle capability data describing autonomous vehicle operational capabilities of the first AV type and first difference data, the first difference data indicating at least one roadway element difference between a previous predicted route generated by the service assignment system for a previous AV of the first AV type and a previous planned route generated by and received from the previous AV of the first AV type, the previous planned route describing a transportation service route generated by and planned for the previous AV; (see at least Fig. 1-10 [0017-0087]: (corresponds to planned route by the AV) Vehicles capable of highly automated driving (e.g., autonomous vehicles) rely on a motion planning process, i.e., an algorithmic process to automatically generate and execute a trajectory through the environment toward a designated short-term goal. To distinguish the trajectory that is generated by the motion planning process from the route that is generated by a route planning process (corresponds to predicted route), we note that trajectories are paths through the vehicle's immediate surroundings (e.g. with distance scales typically on the order of several meters to several hundred meters) that are specifically designed to be free of collisions with obstacles and often have desirable characteristics related to path length, ride quality, required travel time, lack of violation of rules of the road, adherence to driving practices, or other factors. The validation of road segments and routes (or determination of inability to travel safely or robustly) could be based on successful experimental travel (or simulated travel) by an autonomous vehicle at a level of road features such as streets or at a lane level within a given road feature. A routing algorithm could make use of such information by considering only validated autonomous driving routes when determining an optimal route between the ego vehicle's current position and a goal position. Such an optimal route might attempt to include only road segments that have been deemed “validated autonomous driving routes,” or it might attempt to include a combination of validated and unvalidated driving routes, with the combination determined by an optimization process that considers a variety of factors such as travel distance, expected travel time, and whether or not the road segments are validated or unvalidated, among other factors. In general the route algorithm could explore only candidate routes that are known to have a viability status that exceeds a viability threshold, for example, to allow for sufficiently robust or sufficiently safe travel or both. That is, the system distinguishes between (a) route planning process by the server which produces a high-level, coarse routing (road elements, start-end position, path through road network) and (b) motion planning process by the autonomous vehicle that produces vehicle-level trajectory planning to execute the route (detailed path, lane level and obstacle avoidance) where some trajectories the AV computes may not exactly match the predicted route due to vehicle capabilities, sensor constraints and road way features; (c) road segments and route may be validated through prior successful travel by autonomous vehicle at street/lane level; and (d) routing algorithm can generate optimal routes based on validated segments or combinations of validated and unvalidated segments. Such that, differences between the predicted route and the vehicle planned trajectory, deviations due to AV motion planning limitations or unvalidated segments , produces the “first difference data” used to update subsequent route prediction.)
selecting, by the service assignment system, the first AV based on the first predicted route for executing the transportation service; (see at least Fig. 1-10 [0017-0087]: The route planning process aims to exclude candidate routes that include road features that can be determined to be not safely navigable by an autonomous vehicle. For this purpose the route planning process can usefully consider sources of information that are specifically relevant to autonomous vehicles, including information about characteristics of road features such as spatial characteristics, orientation, surface characteristics, and others. Generally, such information would be used to avoid routing the autonomous vehicle through areas of the road network that would be difficult or impossible for the vehicle to navigate at a required level of performance or safety. That is, route planning explicitly excludes candidate routes (and/or segments) that include road features not safely navigable by an autonomous vehicle, difficult or impossible to traverse at a required level of performance or safety, wherein the route feasibility is evaluated using AV-specific data and the purpose of roue planning is to ensure the AV can safely execute the route; meaning that route feasibility is vehicle dependent and vehicle assignment is inseparable from route planning.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Rao’s autonomous vehicle routing system to incorporate the technique of incorporating vehicle specific performance and contextual road data into route planning as taught by Iagnemma with reasonable expectation of success to improve safety and reliability for autonomous vehicle operation.
Regarding claims 2 and 14, the combination of Rao in view of Iagnemma teaches The system and the method of claims 1 and 13, the operations further comprising:
Rao further teaches selecting a second set of candidate AVs, the second set of candidate AVs capable of executing the transportation service request, the second set of candidate AVs comprising the first AV and a second AV of a second AV type; (see at least Fig. 1-9 [0035-0074]: the coordination and selection of the type of vehicle to be matched to a consumer can be based on a plurality of factors including the customer profile, the route between origination and destination, and the capabilities of the autonomous vehicle. An optimization function is enabled to capture the data from the available vehicle characteristics to determine a list of matching vehicles (e.g. autonomous trucks, buses, vehicles)various types of AV may be part of a ridesharing services, car sharing service, a fleet, a distributed network of a variety of vehicles). As shown in Fig. 7, each route may be mapped to a set of characteristics of qualifying fleet vehicles including several types of vehicles including dedicated autonomous vehicles, autonomous passenger buses and larger autonomous self-driving trucks.)
selecting, by the service assignment system, the first AV from the second set of candidate AVs to execute the transportation service, the selecting being based in part on the first predicted route and the second predicted route.(see at least Fig. 1-8 [0040-0092]:determining an autonomous vehicle based on one or more capabilities of the AV and selecting a preferred navigation route of the plurality of navigation routes and communicating instructions to the autonomous vehicle to proceed on the selected preferred navigational route.)
it may be alleged that Rao does not explicitly teach determining, by the service assignment system, a second predicted route for the second AV using vehicle capability data describing the second AV type and second difference data indicating at least one roadway element included in a previous predicted route for a previous AV of the second AV type and not included in a previous planned route received from the previous AV of the second AV type, the second AV type vehicle capability data different from the first AV type vehicle capability data;
Iagnemma is directed to system and method for evaluating and selecting routes for autonomous vehicles by filtering out road segments that vehicle cannot safely traverse based on vehicle’s specific capabilities, sensor performances and roadway/environmental conditions, Iagnemma teaches determining, by the service assignment system, a second predicted route for the second AV using vehicle capability data describing the second AV type and second difference data indicating at least one roadway element included in a previous predicted route for a previous AV of the second AV type and not included in a previous planned route received from the previous AV of the second AV type, the second AV type vehicle capability data different from the first AV type vehicle capability data; (see at least Fig. 1-10 [0017-0087]: (corresponds to planned route by the AV) Vehicles capable of highly automated driving (e.g., autonomous vehicles) rely on a motion planning process, i.e., an algorithmic process to automatically generate and execute a trajectory through the environment toward a designated short-term goal. To distinguish the trajectory that is generated by the motion planning process from the route that is generated by a route planning process (corresponds to predicted route), we note that trajectories are paths through the vehicle's immediate surroundings (e.g. with distance scales typically on the order of several meters to several hundred meters) that are specifically designed to be free of collisions with obstacles and often have desirable characteristics related to path length, ride quality, required travel time, lack of violation of rules of the road, adherence to driving practices, or other factors. The validation of road segments and routes (or determination of inability to travel safely or robustly) could be based on successful experimental travel (or simulated travel) by an autonomous vehicle at a level of road features such as streets or at a lane level within a given road feature. A routing algorithm could make use of such information by considering only validated autonomous driving routes when determining an optimal route between the ego vehicle's current position and a goal position. Such an optimal route might attempt to include only road segments that have been deemed “validated autonomous driving routes,” or it might attempt to include a combination of validated and unvalidated driving routes, with the combination determined by an optimization process that considers a variety of factors such as travel distance, expected travel time, and whether or not the road segments are validated or unvalidated, among other factors. In general the route algorithm could explore only candidate routes that are known to have a viability status that exceeds a viability threshold, for example, to allow for sufficiently robust or sufficiently safe travel or both. That is, the system distinguishes between (a) route planning process by the server which produces a high-level, coarse routing (road elements, start-end position, path through road network) and (b) motion planning process by the autonomous vehicle that produces vehicle-level trajectory planning to execute the route (detailed path, lane level and obstacle avoidance) where some trajectories the AV computes may not exactly match the predicted route due to vehicle capabilities, sensor constraints and road way features; (c) road segments and route may be validated through prior successful travel by autonomous vehicle at street/lane level; and (d) routing algorithm can generate optimal routes based on validated segments or combinations of validated and unvalidated segments. Such that, differences between the predicted route and the vehicle planned trajectory, deviations due to AV motion planning limitations or unvalidated segments , produces the “first difference data” used to update subsequent route prediction.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Rao’s autonomous vehicle routing system to incorporate the technique of incorporating vehicle specific performance and contextual road data into route planning as taught by Iagnemma with reasonable expectation of success to improve safety and reliability for autonomous vehicle operation.
Regarding claims 3 and 15, the combination of Rao in view of Iagnemma teaches The system and the method of claims 2 and 14, the operations further comprising:
It may be alleged that Rao does not explicitly teach determining, by the service assignment system, third difference data describing a difference between the first predicted route and the first predicted route; and
determining, by the service assignment system, a predicted route for a third AV of the first AV type based at least in part on the third difference data.
Iagnemma is directed to system and method for evaluating and selecting routes for autonomous vehicles by filtering out road segments that vehicle cannot safely traverse based on vehicle’s specific capabilities, sensor performances and roadway/environmental conditions, Iagnemma teaches determining, by the service assignment system, third difference data describing a difference between the first predicted route and the first predicted route; and determining, by the service assignment system, a predicted route for a third AV of the first AV type based at least in part on the third difference data. (see at least Fig. 1-10 [0017-0087]: To distinguish the trajectory that is generated by the motion planning process from the route that is generated by a route planning process (corresponds to predicted route), we note that trajectories are paths through the vehicle's immediate surroundings (e.g. with distance scales typically on the order of several meters to several hundred meters) that are specifically designed to be free of collisions with obstacles and often have desirable characteristics related to path length, ride quality, required travel time, lack of violation of rules of the road, adherence to driving practices, or other factors. The validation of road segments and routes (or determination of inability to travel safely or robustly) could be based on successful experimental travel (or simulated travel) by an autonomous vehicle at a level of road features such as streets or at a lane level within a given road feature. A routing algorithm could make use of such information by considering only validated autonomous driving routes when determining an optimal route between the ego vehicle's current position and a goal position. Such an optimal route might attempt to include only road segments that have been deemed “validated autonomous driving routes,” or it might attempt to include a combination of validated and unvalidated driving routes, with the combination determined by an optimization process that considers a variety of factors such as travel distance, expected travel time, and whether or not the road segments are validated or unvalidated, among other factors. In general the route algorithm could explore only candidate routes that are known to have a viability status that exceeds a viability threshold, for example, to allow for sufficiently robust or sufficiently safe travel or both. That is, the system distinguishes between (a) route planning process by the server which produces a high-level, coarse routing (road elements, start-end position, path through road network) and (b) motion planning process by the autonomous vehicle that produces vehicle-level trajectory planning to execute the route (detailed path, lane level and obstacle avoidance) where some trajectories the AV computes may not exactly match the predicted route due to vehicle capabilities, sensor constraints and road way features; (c) road segments and route may be validated through prior successful travel by autonomous vehicle at street/lane level; and (d) routing algorithm can generate optimal routes based on validated segments or combinations of validated and unvalidated segments. Such that, differences between the predicted route and the vehicle planned trajectory, deviations due to AV motion planning limitations or unvalidated segments, produces the “difference data” used to update subsequent route prediction.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Rao’s autonomous vehicle routing system to incorporate the technique of incorporating vehicle specific performance and contextual road data into route planning as taught by Iagnemma with reasonable expectation of success to improve safety and reliability for autonomous vehicle operation.
Regarding claims 4 and 16, the combination of Rao in view of Iagnemma teaches The system and the method of claims 2 and 14, the operations further comprising:
It may be alleged that Rao does not explicitly teach determining, by the service assignment, that a portion of roadway elements described by the third difference data is described by a common roadway element property, the common roadway element property comprising at least one of a cost parameter, a traversal characteristics, or a connectivity feature associated with the roadway elements, the portion of roadway elements included in a previous predicted route for a previous AV of the first AV type and not included in a previous planned route received for the third AV of the first AV type; and
modifying a routing rule associated with the first AV type to disfavor roadway elements having the common roadway element property.
Iagnemma is directed to system and method for evaluating and selecting routes for autonomous vehicles by filtering out road segments that vehicle cannot safely traverse based on vehicle’s specific capabilities, sensor performances and roadway/environmental conditions, Iagnemma teaches determining, by the service assignment, that a portion of roadway elements described by the third difference data is described by a common roadway element property, the common roadway element property comprising at least one of a cost parameter, a traversal characteristics, or a connectivity feature associated with the roadway elements, the portion of roadway elements included in a previous predicted route for a previous AV of the first AV type and not included in a previous planned route received for the third AV of the first AV type; and modifying a routing rule associated with the first AV type to disfavor roadway elements having the common roadway element property. (see at least Fig. 1-10 [0017-0087]: To distinguish the trajectory that is generated by the motion planning process from the route that is generated by a route planning process (corresponds to predicted route), we note that trajectories are paths through the vehicle's immediate surroundings (e.g. with distance scales typically on the order of several meters to several hundred meters) that are specifically designed to be free of collisions with obstacles and often have desirable characteristics related to path length, ride quality, required travel time, lack of violation of rules of the road, adherence to driving practices, or other factors. The validation of road segments and routes (or determination of inability to travel safely or robustly) could be based on successful experimental travel (or simulated travel) by an autonomous vehicle at a level of road features such as streets or at a lane level within a given road feature. A routing algorithm could make use of such information by considering only validated autonomous driving routes when determining an optimal route between the ego vehicle's current position and a goal position. Such an optimal route might attempt to include only road segments that have been deemed “validated autonomous driving routes,” or it might attempt to include a combination of validated and unvalidated driving routes, with the combination determined by an optimization process that considers a variety of factors such as travel distance, expected travel time, and whether or not the road segments are validated or unvalidated, among other factors. In general the route algorithm could explore only candidate routes that are known to have a viability status that exceeds a viability threshold, for example, to allow for sufficiently robust or sufficiently safe travel or both. Spatial and connectivity characteristics of intersections, roundabouts, junctions or other road features may allow the autonomous vehicle to avoid certain intersections that make it impossible for the autonomous vehicle capabilities to traverse safely. That is, the system distinguishes between (a) route planning process by the server which produces a high-level, coarse routing (road elements, start-end position, path through road network) and (b) motion planning process by the autonomous vehicle that produces vehicle-level trajectory planning to execute the route (detailed path, lane level and obstacle avoidance) where some trajectories the AV computes may not exactly match the predicted route due to vehicle capabilities, sensor constraints and road way features; (c) road segments and route may be validated through prior successful travel by autonomous vehicle at street/lane level; and (d) routing algorithm can generate optimal routes based on validated segments or combinations of validated and unvalidated segments. Such that, differences between the predicted route and the vehicle planned trajectory, deviations due to AV motion planning limitations or unvalidated segments, produces the “difference data” used to update subsequent route prediction to include or exclude a route segment planned for autonomous vehicle traversal.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Rao’s autonomous vehicle routing system to incorporate the technique of incorporating vehicle specific performance and contextual road data into route planning as taught by Iagnemma with reasonable expectation of success to improve safety and reliability for autonomous vehicle operation.
Regarding claims 5 and 17, the combination of Rao in view of Iagnemma teaches The system and the method of claims 4 and 16,
Rao further teaches wherein the common roadway elements property describes a connectivity between roadway elements. (see at least Fig. 1-9 [0032-0092]: roundabouts, unprotected left turn, stop signs and traffic lights described connectivity between roadway elements.)
Regarding claims 6 and 18, the combination of Rao in view of Iagnemma teaches The system and the method of claims 4 and 16,
Rao further teaches wherein the common roadway element property describe an actor density above a threshold value. (see at least [0058-0060]: The routing and mapping system may further include specific details of intersections, images obtained by one or more AVs of the intersections, reported accident data, reported traffic data, averages of the time needed to navigate the intersection, and other factors to describe the intersection.)
Regarding claims 7 and 19, the combination of Rao in view of Iagnemma teaches The system and the method of claims 4 and 16,
Rao further teaches wherein the common roadway element property describe a traffic condition. (see at least [0058-0060]: The routing and mapping system may further include specific details of intersections, images obtained by one or more AVs of the intersections, reported accident data, reported traffic data, averages of the time needed to navigate the intersection, and other factors to describe the intersection.)
Regarding claim 8, the combination of Rao in view of Iagnemma teaches The system and the method of claims 4,
Rao further teaches wherein modifying the routing rule associated with the first AV type comprises raising a cost for routing AV of the first AV type to roadway elements having the common roadway element property. (see at least Fig. 1-9 [0035-0092]: the various paths and routes may be rank ordered based on a variety of factors including the AV friendly score, time score, and cost score wherein the AV friendly score may be created for the roundabout intersection. The AV score may be configurable to include a plurality of factors. In this instance, a score of the intersection may be created which includes the following factors: number of vehicles, number of turns, unprotected left turn, and traffic light mediated versus stop sign or no sign.)
Regarding claim 9, the combination of Rao in view of Iagnemma teaches The system and the method of claims 4,
Rao further teaches wherein modifying the routing rule associated with the first AV type comprises modifying a routing graph associated with the first AV type for generating the predicted routes. (see at least Fig. 1-9 [0035-0092]: the routing of autonomous vehicles, vehicles, and ridesharing vehicles can optimized based on one or more processors that accounts for factors including the capabilities of the autonomous vehicles, the specific mapping paths available, and predicted characteristics of intersections. That is each intersection can be associated with an AV friendly score and necessary requirement such as both LIDAR, IMU and/or cameras to navigate the intersection effectively.)
Regarding claim 9, the combination of Rao in view of Iagnemma teaches The system and the method of claims 4,
Rao does not explicitly teach wherein the difference data is based on at least one of the previous AV traversing a previously predicted route, the previous AV traversing a previously planned route, or a simulated AV of the first AV traversing a simulated route.
Iagnemma is directed to system and method for evaluating and selecting routes for autonomous vehicles by filtering out road segments that vehicle cannot safely traverse based on vehicle’s specific capabilities, sensor performances and roadway/environmental conditions, Iagnemma teaches wherein the difference data is based on at least one of the previous AV traversing a previously predicted route, the previous AV traversing a previously planned route, or a simulated AV of the first AV traversing a simulated route. (see at least Fig. 1-10 [0017-0087]: To distinguish the trajectory that is generated by the motion planning process from the route that is generated by a route planning process (corresponds to predicted route), we note that trajectories are paths through the vehicle's immediate surroundings (e.g. with distance scales typically on the order of several meters to several hundred meters) that are specifically designed to be free of collisions with obstacles and often have desirable characteristics related to path length, ride quality, required travel time, lack of violation of rules of the road, adherence to driving practices, or other factors. The validation of road segments and routes (or determination of inability to travel safely or robustly) could be based on successful experimental travel (or simulated travel) by an autonomous vehicle at a level of road features such as streets or at a lane level within a given road feature. A routing algorithm could make use of such information by considering only validated autonomous driving routes when determining an optimal route between the ego vehicle's current position and a goal position. Such an optimal route might attempt to include only road segments that have been deemed “validated autonomous driving routes,” or it might attempt to include a combination of validated and unvalidated driving routes, with the combination determined by an optimization process that considers a variety of factors such as travel distance, expected travel time, and whether or not the road segments are validated or unvalidated, among other factors. In general the route algorithm could explore only candidate routes that are known to have a viability status that exceeds a viability threshold, for example, to allow for sufficiently robust or sufficiently safe travel or both.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Rao’s autonomous vehicle routing system to incorporate the technique of deriving the difference data based on at least one of the previous AV traversing a previously predicted route, the previous AV traversing a previously planned route, or a simulated AV of the first AV traversing a simulated route as taught by Iagnemma with reasonable expectation of success to improve safety and reliability for autonomous vehicle operation.
Regarding Claim 11, the combination of Rao in view of Iagnemma teaches The system of claim 3, the operations further comprising:
Rao further teaches selecting a first roadway element status using the first difference data (see at least Fig. 3, 8[0017-0018, 0053-0092]: a training and learning model may be enabled such that AVs may capture objects, and these objects may further be classified as being AV friendly or AV unfriendly. Accordingly, during route planning an optimization function may be created that preferences AV friendly routes versus AV unfriendly routes. As an example, an AV friendly object that may be captured are clearly defined lane markings whereas an AV unfriendly route may not have any lane markings. An additional example of an AV friendly route may include the presence of a only right turns, projected left turns, clearly defined bike lanes, or the historical presence of few vehicles. The AV friendly index as stored by a server may be adjusted over time. These AV friendly scores can then be linked to future predictions as to which route is most autonomous vehicle friendly.); and
providing first roadway element status data describing the first roadway element status to the third AV. (see at least Fig. 3 [0017-0018, 0053-0064]: a training and learning model may be enabled such that AVs may capture objects, and these objects may further be classified as being AV friendly or AV unfriendly. Accordingly, during route planning an optimization function may be created that preferences AV friendly routes versus AV unfriendly routes. As an example, an AV friendly object that may be captured are clearly defined lane markings whereas an AV unfriendly route may not have any lane markings. An additional example of an AV friendly route may include the presence of a only right turns, projected left turns, clearly defined bike lanes, or the historical presence of few vehicles. The AV friendly index as stored by a server may be adjusted over time. These AV friendly scores can then be linked to future predictions as to which route is most autonomous vehicle friendly.)
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable Rao in view of Iagnemma and Siegel et al. (U.S. Patent No. 10,248,120 B1 hereinafter Siegel).
Regarding Claim 12, the combination of Rao in view of Iagnemma teaches The system of claim 3, the operations further comprising:
The combination of Rao in view of Iagnemma does not explicitly teach determining, by the service assignment system, third difference data describing a difference between the first predicted route and the first planned route;
determining a risk level associated with at least one roadway element described by the third difference data; and
determining not to select a third AV of the first AV type to execute a second transportation service based at least in part on the risk level.
Siegel is directed to navigable path networks for autonomous vehicles, Siegel teaches determining, by the service assignment system, third difference data describing a difference between the first predicted route and the first planned route (see at least Fig. 9A-C, 10, Col. 35 Line 0- Col.37- Line 63: as shown in Fig. 9A, the autonomous vehicle 950-1 may receive instructions to travel on an optimal route 935-1 (i.e. predicted route) from the fulfillment center 930. Base on the reports from other autonomous vehicles in Fig. 9B, the autonomous vehicle 950-1 may change its route to a new optimal route 935-2 (i.e. planned route) that also travels through or around the urban park avoiding areas with reported congestions, blocked path, and slow speeds (i.e. third difference data)) ; and
determining a risk level associated with at least one roadway element described by the third difference data (see at least Fig. 5B, Col. 28 Lines 28-Col. 29 Line 13: As shown in Fig. 5B, the autonomous vehicles reported at least one roadway elements to have water hazard, muddy track with maximum 0.5 MPH, icy path in winter conditions and sidewalks that close dusk/dawn which all potential risks to autonomous vehicle causing autonomous vehicles to be delayed or stranded.); and
determining not to select a third AV of the first AV type to execute a second transportation service based at least in part on the risk level. (see at least Fig. 5B, 5C, Col. 28 Lines 28-Col. 29 Line 13: As shown in Fig. 5B, the autonomous vehicles reported at least one roadway elements to have water hazard, muddy track with maximum 0.5 MPH, icy path in winter conditions and sidewalks that close dusk/dawn which all potential risks to autonomous vehicle. As shown in Fig. 5C, optimal routes are determined to be out of the areas associated with risks and autonomous vehicles are not to executed transportation service near roadway areas with potential risks).
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Rao and Iagnemma to incorporate the technique of determining third difference data describing a difference between the first predicted route and the first planned route; determining a risk level associated with at least one roadway element described by the third difference data; and determining not to select a third AV of the first AV type to execute a second transportation service based at least in part on the risk level as taught by Siegel with reasonable expectation of success to ensure environmental characteristics including weather conditions, surface conditions, traffic conditions or any other information or data that may potentially impact the performance of any given autonomous vehicle can be used to update a routing system to improve autonomous vehicles’ operation efficiency (Siegel, Col. 36 Lines 15-42).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANA F ARTIMEZ whose telephone number is (571)272-3410. The examiner can normally be reached M-F: 9:00 am-3:30 pm EST.
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, Faris S. Almatrahi can be reached at (313) 446-4821. 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.
/DANA F ARTIMEZ/Examiner, Art Unit 3667
/FARIS S ALMATRAHI/Supervisory Patent Examiner, Art Unit 3667