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 the Claims
Claims 1, 9, 13, and 17 have been amended, claims 2-3, 7, 8, 14, 15, 18, and 19 have been canceled, and no new claims have been added. Accordingly, claims 1, 4-6, 9-13, 16-17, and 20 are now pending.
Response to Arguments
Applicant's arguments filed on 11/19/2025 regarding the rejection of the claims under 35 U.S.C. 103 over Kentley-Klay (US11067983B2) in view of MULCAHY (US-20200143669-A1) have been fully considered but they are not persuasive.
The applicant argues that Kentley-Klay does not teach transmitting an alert message to nearby vehicles because the cited disclosure is limited to communications between the autonomous vehicle and a remote teleoperator for trajectory selection. According to the applicant, the reference neither discloses vehicle-to-vehicle communication nor an “alert message” warning other vehicles of a road obstruction. Applicant further contends that Kentley-Klay lacks any teaching of transmitting a message to vehicles within a predetermined distance of a road segment.
The examiner respectfully disagrees with these arguments. With respect to the “alert message”, the examiner interprets “alert message” as information transmitted from a vehicle that indicates a roadway condition, such as the presence of an obstacle or other environmental condition affecting vehicle operation. Kentley-Klay expressly discloses a system architecture in which autonomous vehicles communicate with an autonomous vehicle service platform through network communications. For example, Kentley-Klay explains that “various embodiments relate generally to autonomous vehicles and associated mechanical, electrical and electronic hardware, computer software and systems, and wired and wireless network communications to provide an autonomous vehicle fleet as a service.” Kentley-Klay further discloses that a method may include “determining destination locations for autonomous vehicles, calculating, at an autonomous vehicle service platform, delivery locations to which the autonomous vehicles are directed, identifying data to implement a delivery location associated with an autonomous vehicle, and transmitting data representing a command to the autonomous vehicle,” where “the command may be configured to cause navigation of the autonomous vehicle to the delivery location.” (Kentley-Klay, Abstract; See also Col. 17 and 18 of Kentley-Klay).
With respect to the vehicle-to-vehicle argument, Kentley-Klay describes a fleet management architecture in which multiple autonomous vehicles operate within a system managed by an autonomous vehicle service platform and communicate through the network infrastructure supporting the fleet environment. Under the broadest reasonable interpretation, communications transmitted from a vehicle and received by the service platform may be disseminated to other vehicles within the managed fleet through the network infrastructure coordinating vehicle operations. Thus, the communication architecture described in Kentley-Klay supports communication of vehicle-generated information among vehicles through the intermediary fleet network.
Further, Kentley-Klay explains that the autonomous vehicle controller processes environmental and positional data associated with the vehicle and its surrounding environment. For example, Kentley-Klay states that “autonomous vehicle controller 347a is configured to receive camera data 340a, Lidar data 346a, and radar data 348a,” and “is also configured to receive positioning data, such as GPS data 352, IMU data 354, and other position-sensing data.” Kentley-Klay further explains that “reference data 339 includes map data … and route data (e.g., road network data).” (Kentley-Klay, col. 9, lines 1–25). Such environmental and operational information relates directly to roadway conditions and vehicle navigation decisions.
Accordingly, the cited portions of Kentley-Klay demonstrate that the reference teaches transmitting vehicle operational and environmental information through a network associated with an autonomous vehicle fleet management system. Such transmissions reasonably correspond to the claimed transmission of an alert message indicating a roadway condition under the broadest reasonable interpretation. Because the vehicles in the fleet operate within the roadway environment and communicate through the network infrastructure managed by the service platform, information transmitted through the system may be shared among vehicles operating in the relevant roadway area. Thus, the communication architecture described in Kentley-Klay reasonably suggests transmitting operational or roadway-related information to vehicles operating within the relevant roadway environment.
Therefore, the rejection of claims 1, 4-6, 9-13, 16-17, and 20 under 35 U.S.C. 103 over Kentley-Klay (US11067983B2) in view of MULCAHY (US-20200143669-A1) is maintained herein.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claims 1, 4-6, 9-13, 16-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kentley-Klay (US11067983B2) in view of MULCAHY (US-20200143669-A1).
Regarding claims 1, 13, and 17, Kentley-Klay discloses a method comprising:
initiating a collection of probe data associated with a road segment based on detecting a road obstruction on the road segment (Col. 6, Lines 46-55; Lines 10-25; Col. 7, Lines 30-40; Col. 10, Lines 27-30; Col. 11, Lines 25-30; Probe Data is broadly interpreted in view of the definition well-known in the art: real-time traffic information collected from vehicles equipped with GPS or other positioning technologies);
determining an intensity class of the road obstruction (Col. 12, Lines 45-65; Col. 19, Lines 32-65;
determining a diversion confidence score for diverting a route from the road segment based on the intensity (Col. 7, Lines 44-65);
providing one or more instructions to an autonomous vehicle for navigating around the road segment based on the diversion confidence score (Col. 8, Lines 1-15; Col. 9, Lines 1-15; Motion controller; Col. 10, Lines 17-40).
transmitting an alert message indicating the road obstruction to one or more vehicles within a predetermined distance of the road segment (Abstract; Col. 17, Lines 58-70: “Obstacle data 920, planner options data 924, and position data 926 are transmitted to a messaging service bridge 932, which, in accordance with message service configuration data 934, generates telemetry data 940 and query data 942, both of which are transmitted via data-centric messaging bus 972 into teleoperator application 901 as telemetry data 950 and query data 952. Teleoperator API 962 receives telemetry data 950 and inquiry data 952, which, in tum are processed in view of Route data 960 and message service configuration”; Col. 18, Lines 1-5: “data 964. The resultant data is subsequently presented to a teleoperator 908 via teleoperator computing device 904 and/or a collaborative display (e.g., a dashboard display visible to a group of collaborating teleoperators 908).”).
However, Kentley Klay does not explicitly state processing the probe data to generate a time space diagram (TSD) wherein the TSD plots the probe data according to distance from an origin point on the road segment over time, and determining an intensity class of the road obstruction based on one or more features of the TSD, wherein the one or more features of the TSD comprise at least one of: (i) a slope of probe trajectories in the TSD, (ii) a duration of congestion in the TSD, or (iii) an absence of probe trajectories in a downstream portion of the TSD.
On the other hand, MULCAHY teaches processing the probe data to generate a time space diagram (TSD) wherein the TSD plots the probe data according to distance from an origin point on the road segment over time, and determining an intensity class of the road obstruction based on one or more features of the TSD, wherein the one or more features of the TSD comprise at least one of: (i) a slope of probe trajectories in the TSD, (ii) a duration of congestion in the TSD, or (iii) an absence of probe trajectories in a downstream portion of the TSD ([0002]; [0007]; [0024]; [0025]).
It would have been obvious for someone with ordinary skill in the art before the effective filing date of the current application to modify the teachings of the Kentley-Klay reference and include features from the MULCAHY reference with a reasonable expectation of success. Having a TSD provides more accurate analysis and visualization of traffic flow.
Regarding claims 4, 16, and 20, Kentley-Klay discloses determining an intensity weight value based on the intensity class of the road obstruction, wherein the diversion confidence score is further based on the intensity weight (Col. 19, Lines 44-56: “Confidence level generator 1123 may be configured to analyze perception data 45 1132 to determine a state for the autonomous vehicle. For example, confidence level generator 1123 may use semantic information associated with static and dynamic objects, as well as associated probabilistic estimations, to enhance a degree of certainty that planner 1164 is determining safe course of action. For example, planner 1164 may use perception engine data 1132 that specifies a probability that an object is either a person or not a person to determine whether planner 1164 is operating safely (e.g., planner 1164 may receive a degree of certainty that an object has a 98% probability of being a person, and a probability of 2% that the object is not a person).”; Note: Note: the probability that an object is either a person or not a person is broadly interpreted as the intensity weight herein)
Regarding claim 5, Kentley-Klay discloses determining a detection weight value of the road obstruction based on the sensor data used for the detecting of the road obstruction, wherein the diversion confidence is further based on the detection weight (Col. 19, Lines 44-56: “Confidence level generator 1123 may be configured to analyze perception data 45 1132 to determine a state for the autonomous vehicle. For example, confidence level generator 1123 may use semantic information associated with static and dynamic objects, as well as associated probabilistic estimations, to enhance a degree of certainty that planner 1164 is determining safe course of action. For example, planner 1164 may use perception engine data 1132 that specifies a probability that an object is either a person or not a person to determine whether planner 1164 is operating safely (e.g., planner 1164 may receive a degree of certainty that an object has a 98% probability of being a person, and a probability of 2% that the object is not a person).”; Note: See 112(b) rejection above; Note: the probability that an object is either a person or not a person is broadly interpreted as the detection weight herein).
Regarding claim 6, Kentley-Klay discloses the detection weight value is a fixed value (Col. 19, Lines 44-56: “Confidence level generator 1123 may be configured to analyze perception data 45 1132 to determine a state for the autonomous vehicle. For example, confidence level generator 1123 may use semantic information associated with static and dynamic objects, as well as associated probabilistic estimations, to enhance a degree of certainty that planner 1164 is determining safe course of action. For example, planner 1164 may use perception engine data 1132 that specifies a probability that an object is either a person or not a person to determine whether planner 1164 is operating safely (e.g., planner 1164 may receive a degree of certainty that an object has a 98% probability of being a person, and a probability of 2% that the object is not a person).”; Note: the probability that an object is either a person or not a person is broadly interpreted as the detection weight herein).
Regarding claim 9, Kentley-Klay discloses the alert message is transmitted based on determining that the diversion confidence score is above a threshold confidence (Col. 6, Lines 30-40: “If autonomous vehicle controller 147 cannot ascertain a path or trajectory over which vehicle 109d may safely transit with a relatively high degree of certainty, then autonomous vehicle controller 147 may transmit request message 105 for teleoperation services. In response, a teleoperator computing device 104 may receive instructions from a teleoperator 108 to perform a course of action to successfully (and safely) negotiate obstacles 126”; Col. 17, Col. 18).
Regarding claim 10, Kentley-Klay discloses initiating a presentation of an alert message indicating the road obstruction to a passenger of a vehicle traveling within a predetermined distance of the road segment without diverting the vehicle around the road segment based on determining that the diversion confidence is below a threshold confidence (Col. 19, Lines 55-65: “Upon determining a confidence level (e.g., based on statistics and probabilistic determinations) is below a threshold required for predicted safe operation, a relatively low confidence level (e.g., single probability score) may trigger planner 1164 to transmit a request 1135 for teleoperation support to autonomous vehicle service platform 1101. In some cases, telemetry data and a set of candidate trajectories may accompany the request.”).
Regarding claim 11, Kently-Klay discloses the intensity class is classified according to one or more classes, and wherein the one or more classes are associated with a respective intensity weight value for determining the diversion confidence score (Col. 9, Lines 40-67; Col 10, Lines 1-25; Col. 12, Lines 46-65; Col. 19).
Regarding claim 12, Kentley-Klay discloses determining the intensity, the diversion confidence, or a combination thereof (Col. 19), and processing images using a trained machine learning model (Col. 22, Lines 14-25).
However, Kentley-Klay does not explicitly state converting the TSD to an image; and processing the image using a trained machine learning model.
On the other hand, MULCAHY teaches converting the TSD to an image ([0006]; [0045]; Graphs are considered 2D images projection of time and space).
It would have been obvious for someone with ordinary skill in the art before the effective filing date of the current application to modify the teachings of the Kentley-Klay reference and include features from the MULCAHY reference with a reasonable expectation of success, to convert the time diagram reference into an image in order to determine the intensity. Doing so allows a more concise intensity detection process via the converted image.
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.
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/S.B./Examiner, Art Unit 3666
/SCOTT A BROWNE/Supervisory Patent Examiner, Art Unit 3666