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 .
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4, 7-12, 15,16 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Zarringhalam (US 2021/0284199)
As to claim 1 Zarringhalam discloses a computer-implemented method comprising:
receiving metrics for points along a planned path of a vehicle (Paragraph 47 “A quantity of the “N” future predicted positions is calculated as a function 71 described in greater detail in reference to FIG. 7, and can vary depending on multiple criteria including a vehicle velocity, a vehicle forward and lateral acceleration, a yaw rate, a lane proximity, a curvature tracking error, a yaw rate error, a steering angle, a steering angle rate, a torque commanded, the vehicle performance constraints generated from the data of the vehicle dynamics model 42, safety constraints which are described below, actuation constraints which are described below and event constraints such as the geometry of a roadway 60 as the automobile vehicle 12 is projected to travel along the roadway 60 with respect to an optimal path defined by a lane centerline 62.”) ;
determining whether the metrics satisfy constraints for the metrics(Paragraph 47 “A quantity of the “N” future predicted positions is calculated as a function 71 described in greater detail in reference to FIG. 7, and can vary depending on multiple criteria including a vehicle velocity, a vehicle forward and lateral acceleration, a yaw rate, a lane proximity, a curvature tracking error, a yaw rate error, a steering angle, a steering angle rate, a torque commanded, the vehicle performance constraints generated from the data of the vehicle dynamics model 42, safety constraints which are described below, actuation constraints which are described below and event constraints such as the geometry of a roadway 60 as the automobile vehicle 12 is projected to travel along the roadway 60 with respect to an optimal path defined by a lane centerline 62.”);
responsive to determining that the metrics do not satisfy the constraints for the metrics, performing a weight optimization (Paragraph 53 “A third category of weighted penalties defines a group of Performance and Comfort Penalties 78. The Performance and Comfort Penalties 78 have weighing factors lower than the weighting factors of the actuation penalty factors and are thereby the lowest weighting factors of the three penalty groups. The Performance and Comfort Penalty elements can include, but are not limited to a lane proximity, a position tracking error, a heading tracking error, a curvature tracking error, an adjusted lateral acceleration, an adjusted yaw rate, a lateral jerk and a side slip, individually assigned a predetermined threshold lower than the thresholds of the Actuation Penalties elements. Initiation of the automated driving control function 16 is permitted if the thresholds of the Safety Event Penalties 74, the thresholds of the Actuation Penalties 76 and if the predetermined thresholds of the Performance and Comfort Penalty elements are not exceeded.”); and
responsive to the metrics satisfying the constraints for the metrics, engaging a model predictive controller to control the vehicle (Paragraph 60 “If the first determination 116 identifies the absolute value of the change of the quality index custom-character.sub.ΔT 110 is less than the first threshold β.sub.1 and the second determination 118 identifies the absolute value of the rate of change of the quality index Qdot.sub.ΔT 112 is less than the second threshold β.sub.2 AND the third determination 120 identifies the rate of change of the quality index Qdot.sub.ΔT 112 is also negative-definite an allow controls signal 122 is generated which permits actuation of the automated driving control function 16.”).
As to claim 2 Zarringhalam discloses a computer-implemented method wherein the model predictive controller is disabled while the metrics do not satisfy the constraints for the metrics (Paragraph 53).
As to claim 3 Zarringhalam discloses a computer-implemented method wherein the metrics are selected from a group consisting of a lateral error, a heading error, a lateral velocity, a yaw rate, a lateral acceleration, a steering angle, and a steering rate (Paragraph 53).
As to claim 4 Zarringhalam discloses a computer-implemented method wherein the metrics comprise a lateral error, a heading error, a lateral velocity, a yaw rate, a lateral acceleration, a steering angle, and a steering rate(Paragraph 53).
As to claim 7 Zarringhalam discloses a computer-implemented method wherein model predictive controller controls the vehicle by engaging an active safety feature(Paragraph 53).
As to claim 8 Zarringhalam discloses a computer-implemented method wherein the active safety feature is selected from a group consisting of active cruise control, automated lane change, front collision alert, collision imminent breaking, and automated evasive steering (Paragraph 65).
As to claim 9 the claim is interpreted and rejected as in claim 1.
As to claim 10 the claim is interpreted and rejected as in claim 2.
As to claim 11 the claim is interpreted and rejected as in claim 3.
As to claim 12 the claim is interpreted and rejected as in claim 4.
As to claim 15 the claim is interpreted and rejected as in claim 7.
As to claim 16 the claim is interpreted and rejected as in claim 8.
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 5-6, 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Zarringhalam (US 2021/0284199) in view of Maleki (US 2022/0219691)
As to claim 5 Maleki teaches a computer-implemented method wherein the weight optimization is performed iteratively until the metrics satisfy the constraints for the metrics (Paragraph 240). It would have been obvious to one of ordinary skill to modify Zarringhalam to include the teachings of optimizing the weights iteratively for the purpose of improving efficiency.
As to claim 6 Maleki teaches a computer-implemented method further comprising displaying an indicium to a driver of the vehicle to prompt the driver to control the vehicle to cause an active safety feature to engage (Paragraph 361-362).
As to claim 13 the claim is interpreted and rejected as in claim 5.
As to claim 14 the claim is interpreted and rejected as in claim 6.
Claims 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Zarringhalam (US 2021/0284199) in view of Thompson (US 2022/0242401)
As to claim 17 discloses a computer program product comprising:
a set of one or more computer-readable storage media;
program instructions, collectively stored in the set of one or more storage media, for causing a processor set to perform computer operations comprising:
receiving metrics for points along a planned path of a vehicle, wherein the metrics(Paragraph 47 “A quantity of the “N” future predicted positions is calculated as a function 71 described in greater detail in reference to FIG. 7, and can vary depending on multiple criteria including a vehicle velocity, a vehicle forward and lateral acceleration, a yaw rate, a lane proximity, a curvature tracking error, a yaw rate error, a steering angle, a steering angle rate, a torque commanded, the vehicle performance constraints generated from the data of the vehicle dynamics model 42, safety constraints which are described below, actuation constraints which are described below and event constraints such as the geometry of a roadway 60 as the automobile vehicle 12 is projected to travel along the roadway 60 with respect to an optimal path defined by a lane centerline 62.”) ;
Responsive to the metrics satisfying the constraints for the metrics, engaging a model predictive controller to control the vehicle(Paragraph 53 “A third category of weighted penalties defines a group of Performance and Comfort Penalties 78. The Performance and Comfort Penalties 78 have weighing factors lower than the weighting factors of the actuation penalty factors and are thereby the lowest weighting factors of the three penalty groups. The Performance and Comfort Penalty elements can include, but are not limited to a lane proximity, a position tracking error, a heading tracking error, a curvature tracking error, an adjusted lateral acceleration, an adjusted yaw rate, a lateral jerk and a side slip, individually assigned a predetermined threshold lower than the thresholds of the Actuation Penalties elements. Initiation of the automated driving control function 16 is permitted if the thresholds of the Safety Event Penalties 74, the thresholds of the Actuation Penalties 76 and if the predetermined thresholds of the Performance and Comfort Penalty elements are not exceeded.”);.
Zarringhalam does not explicitly disclose optimizing model predictive control weights using a weight optimizer that receives vehicle states including a lateral position, a lateral velocity, a yaw angle, a steering rate, a steering angle, a lateral acceleration, and a road geometry, wherein if a predicted motion does not satisfy the constraints, the weight optimizer adjusts the model predictive control weights to improve performance,
Thompson teaches does not explicitly disclose By optimizing model predictive control weights using a weight optimizer that receives vehicle states including a lateral position, a lateral velocity, a yaw angle, a steering rate, a steering angle, a lateral acceleration, and a road geometry (Paragraph 16 “The model predictive controller can further operate more effectively by utilizing learned external parameters to consider changes external to the vehicle (such as changes in friction between the vehicle and the road, which could be caused by adverse or favorable weather). The MPC can operate more effectively, for example by dynamically updating one or more controls parameters (such as weights of the MPC).”, Paragraph 150 “On a real vehicle, information may be received from external sensors such as sensors at infrastructure elements sensing the vehicle as it nears or passes such elements. Vehicle information can include, for example, information from vehicle sensors indicating vehicle operating parameters such as acceleration, speed, lateral acceleration, wheel traction, vehicle roll/pitch/yaw, and so on. Vehicle information can also include information from vehicle hardware (e.g., from vehicle actuators 137 and vehicle hardware interfaces 180).”), wherein if a predicted motion does not satisfy the constraints, the weight optimizer adjusts the model predictive control weights to improve performance, wherein the weight optimizer utilizes planner objectives and model predictive constraints, wherein the weight optimizer utilizes planner objects and model predictive constraints, where in the planner objectives include lateral offset form a target based on a planned path and a heading error form the target based on the planned path, wand wherein the model predictive constraints include maximum lateral velocity, maximum steering rate, maximum lateral acceleration, maximum lateral offset, maximum heading error, maximum lateral jerk, and maximum steering angle (Paragraphs 82-83 “A breach of the predicted boundaries may include exceeding one or more values of the operational parameters or calculated values based on values of the operational parameters. Nearing, arriving to, or exceeding the operational constraints of the vehicle may cause the vehicle (or a driver of the vehicle) to lose vehicle stability, lose vehicle control, cause a collision, and cause other vehicle catastrophes. One or more operational parameters (such as weights or gains of the MPC) can vary how the MPC penalizes for breaching, nearing, arriving to, or exceeding one or more operational constraints of the vehicle. One or more operational parameters (such as weights or gains) can determine how an envelope of nominal or safe vehicle states are enforced… The trajectory threshold determination component may be configured to determine predicted threshold values of a trajectory metric. Determination may be based on the contextual information and the operational information. The trajectory metric may characterize a trajectory of the vehicle such that the vehicle is traveling on a roadway. The predicted threshold values of the trajectory metric may represent desirable circumventions in order to avoid causing vehicle catastrophes. Vehicle catastrophes may include a collision, losing control of the vehicle, losing vehicle stability, veering into other lanes, or other vehicle catastrophes. By way of non-limiting example, the predicted thresholds of the trajectory metric account for obstacles in the roadway, lane boundaries, roadway boundaries, vehicle width, or other predicted thresholds of the trajectory metric”).
It would have been obvious to one of ordinary skill to modify Zarringhalam to include the teachings of using a weight optimizer for the various vehicle states for the purpose of guiding the vehicle safely along the road.
As to claim 18 the claim is interpreted and rejected as in claim 2.
Claim 19-20 is rejected under 35 U.S.C. 103 as being unpatentable over Zarringhalam (US 2021/0284199) in view of Thompson (US 2022/0242401) as applied to claim 17 above, and in further view of Maleki (US 2022/0219691)
As to claim 19 the claim is interpreted and rejected as in claim 5.
As to claim 20 the claim is interpreted and rejected as in claims 7, 8.
Response to Arguments
Applicant's arguments filed 6/2/2026 have been fully considered but they are not persuasive.
On page 7 of the applicants arguments applicants argue that Zarringhalam does not disclose “responsive to determining that the metrics do not satisfy the constraints for the metrics, performing a weight optimization.” And does not disclose the corresponding limitation in claim 9 “performing a weight optimization to optimize model predictive control weights for the vehicle” and then determining whether the metrics satisfy the constraints subsequent to that optimization.
The examiner respectfully disagrees with the applicants arguments. The applicant is reminded that the examiner interprets the claim with the broadest reasonable interpretation. Zarringhalam teaches receiving metrics for points along a planned path of a vehicle. First Zarringhalam teaches of determining future predictions of the vehicle along the route and the various metrics for points along a route which include information regarding the vehicle velocity, yaw rate, lateral acceleration, etc.( (Paragraph 47 “A quantity of the “N” future predicted positions is calculated as a function 71 described in greater detail in reference to FIG. 7, and can vary depending on multiple criteria including a vehicle velocity, a vehicle forward and lateral acceleration, a yaw rate, a lane proximity, a curvature tracking error, a yaw rate error, a steering angle, a steering angle rate, a torque commanded, the vehicle performance constraints generated from the data of the vehicle dynamics model 42, safety constraints which are described below, actuation constraints which are described below and event constraints such as the geometry of a roadway 60 as the automobile vehicle 12 is projected to travel along the roadway 60 with respect to an optimal path defined by a lane centerline 62.”). Zarringhalam teaches responsive to determining that the metrics do not satisfy the constraints for the metrics, performing a weight optimization (Paragraph 53). The system includes a quality index which assigns weighted penalties assigned to variables when calculating the quality index. There are three different categories as shown in Figures 7 and 8 which include: performance and comfort penalties, actuation penalties, and safety event penalties. Each of the different categories contains a weighted penalty and the system calculates the quality index based on propagated vehicle states and determining whether or not to activate an automated driving function based on the quality index. Here the system is optimized to assess over a period whether or not automated driving is safe based on such variables. The quality index equation (Paragraph 54 ”Q=.Math.k=N0(PCTRPC+PUTUPU+PSTSPS)”) incorporates safety event penalties with S which denotes the weighting factor assigned to the safety penalties which are determined from table 82. For the safety event penalties, the weights have either zero or infinity such that if any of the safety penalties is present or violated than the system then prevents the system from automated driving. Here the weights are already optimized such that an event such a safety violation would prevent an automated driving function.
On page 10 of the applicants arguments applicants argue that Paragraph 65 of the specification requires that performing weight optimization required an acaudal optimization or adjustment of weights a responsive action to failed constraints. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., changing or modifying the weights) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). The applicant does not claim that the weights need to be adjusted or modified. The claims do not explicitly claim that the weights are changed or adjusted but rather just claims that the weights are optimized. Here the weights are chosen in advance and are optimized for the particular system.
In regards to applicants arguments on page 11 “Response to Rejections under 35 U.S.C. § 103: Claims 5-6, 13-14, and 19”. See examiners arguments and office action above.
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 IMRAN K MUSTAFA whose telephone number is (571)270-1471. The examiner can normally be reached Mon-Fri 9-5.
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, James J Lee can be reached at 571-270-5965. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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IMRAN K. MUSTAFA
Primary Examiner
Art Unit 3668
/IMRAN K MUSTAFA/ Primary Examiner, Art Unit 3668
8/25/2026
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