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
This is in response to applicant’s filing date of August 14, 2025. Claims 1-20 are currently pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 8/19/2025 & 1/05/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Applicant’s claim for the benefit of a prior-filed application, 17/576553 filed on 1/14/2022 and now U.S. Patent US-12415538-B2, under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Claim Rejections --35 U.S.C. § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lin et al (US-20210403045-A1)(“Lin”) in view of Akella et al (US-20200139959-A1)(“Akella”).
As per claim 1, Lin discloses a method for improving performance of an autonomous vehicle (AV) (Figure 7), the method comprising:
obtaining data describing an observed driving path in a driving scenario (Lin at Para. [0033]: “the computing system may train the machine-learning model based on a plurality of training data indicative of the observed driving behavior. The plurality of training data indicative of observed driving behavior may comprise captured sensor data comprising images, videos, LiDAR point clouds, radar signals, or any combination thereof.”), wherein the data comprises a first plurality of observed costs associated with a plurality of cost dimensions (Lin at Para. [0043]: “computing system may generate a delta cost volume 408using the initial cost volume 402 and environment data 404 associated with the environment, wherein the delta cost volume 408 is generated by determining adjustments to the initial cost volume 402 that incorporate observed driving behavior.”);
determining a function of a sum of quantities determined for each cost dimension in the plurality of cost dimensions (Lin at Figures 4 & 7 and Para. [0043]: “the computing system may generate a finalized cost volume 410 based on a summation of the initial cost volume 402 and the delta cost volume 408, wherein the finalized cost volume 410 comprises finalized cost measurements.”), wherein the function of the sum of the quantities indicates a margin by which an estimated cost for each cost dimension in the plurality of cost dimensions exceeds an observed cost of the observed driving path (Lin at Figure 7, step 702 “delta cost”, and Para. [0025] which describes a gap between the observed and estimated cost of a trajectory:” computing system may then generate a delta cost volume using the initial cost volume and environment data associated with the environment.”);
controlling a motion of the AV according to the adjustment to the one or more weights applied to the plurality of cost dimensions (Lin at Figure 7, steps 720 & 780, and Para. [0043]: “the delta cost volume 408 is generated by determining adjustments to the initial cost volume 402 that incorporate observed driving behavior.”).
Lin does not disclose but Akella discloses adjusting one or more weights of a plurality of weights applied to the plurality of cost dimensions according to the function of the sum of the quantities (Akella at Par. [0017]; i.e., a cost can be associated with a weight that can increase or decrease a cost associated with a trajectory or a cost associated with a point of the trajectory… a weight associated with a reference cost can be decreased when the vehicle is navigating around a double-parked vehicle or while changing lanes; the system calculates an adjustment to the cost based on the driving behavior); and
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 method of Lin to have further incorporated generating, using a machine learning model, a change in the one or more weights using the initial cost volume and environment data of the environment, as taught by Akella with a reasonable expectation of success because doing so would lead to increased safety and comfort in vehicle operations. The teaching suggestion/motivation to combine is that by changing the weights of the machine-learned model, a smoother transitions leading towards safer and/or more comfortable vehicle operations is realized. Akella at Para. [0017].
As per claim 2, Lin and Akella disclose a method of claim 1, wherein the margin is indicative of an expected dominance gap between the estimated cost and the observed cost (Lin at Figure 7, step 702 “delta cost”, and Para. [0025] which describes a gap between the observed and estimated cost of a trajectory:” computing system may then generate a delta cost volume using the initial cost volume and environment data associated with the environment. In particular embodiments, the delta cost volume may be generated by determining adjustments to the initial cost volume that incorporate observed driving behavior. The computing system may further score a trajectory of the plurality of potential trajectories for the vehicle 100 based on the initial cost volume and the delta cost volume.”).
As per claim 3, Lin and Akella disclose a method of claim 1, wherein the observed driving path comprises an observed human driving path (Lin at Para. [0033]: “the computing system may train the machine-learning model based on a plurality of training data indicative of the observed driving behavior. The plurality of training data indicative of observed driving behavior may comprise captured sensor data comprising images, videos, LiDAR point clouds, radar signals, or any combination thereof.”).
As per claim 4, Lin and Akella disclose a method of claim 1, wherein the function comprises a plurality of learned parameters associated with the plurality of cost dimensions (Lin at Para. [0033]: “computing system may update the parameters of the machine-learning model.”), and wherein the method further comprises updating the plurality of learned parameters to optimize an output of the function (Lin at Para. [0033] discloses: “ the computing system may update the predicted delta cost volume 408 using the updated machine-learning model, re-select a trajectory based on the initial cost volume 402 and the updated predicted delta cost volume 408, calculate the difference between the re-selected trajectory and the predetermined trajectory, output a loss based on the difference, and compare the loss with the loss in the t-th iteration.”).
As per claim 5, Lin and Akella disclose a method of claim 4, wherein adjusting the one or more weights is based on the updated plurality of learned parameters (Akella at Para. [0013] discloses: “adaptively scaling weights associated with one or more costs to enhance safety and/or comfort when determining the contours of a trajectory through an environment. Further, regions establishing buffers around objects in an environment can be increased or decreased in size depending on a classification of an object (e.g., pedestrians, vehicles, etc.) and/or depending on a velocity of the autonomous vehicle in the environment.”).
As per claim 6, Lin and Akella disclose a method of claim 1, wherein the function of the sum of the quantities comprises a respective margin slope for each cost dimension in the plurality of cost dimensions (Lin at Figures 4 & 7 and Para. [0043]: “the computing system may generate a finalized cost volume 410 based on a summation of the initial cost volume 402 and the delta cost volume 408, wherein the finalized cost volume 410 comprises finalized cost measurements.”), and wherein the method further comprises:
setting a value of the respective margin slope for each cost dimension in the plurality of cost dimensions (Lin at Para. [0041] discloses gradually setting a value, delta cost, that tracks a certain change: ”gradually reduced velocity may be both specified by movement restrictions and reflected by observed driving behavior (i.e., human drivers often slow down when approaching a stop sign 140).”), and wherein adjusting the one or more weights in the plurality of weights is based on the respective margin slopes (Akella at Para. [0014] discloses: “weight associated with various costs can be based on triggers such as jumps or discontinuities in a reference trajectory or obstacle costs meeting or exceeding a threshold.”) ,
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 method of Lin to have further incorporated generating, using a machine learning model, a change in the one or more weights using the initial cost volume and environment data of the environment, as taught by Akella with a reasonable expectation of success because doing so would lead to increased safety and comfort in vehicle operations. The teaching suggestion/motivation to combine is that by changing the weights of the machine-learned model, a smoother transitions leading towards safer and/or more comfortable vehicle operations is realized. Akella at Para. [0017].
As per claim 7, Lin and Akella disclose a method of claim 1, wherein the one or more weights of the plurality of weights is adjusted to minimize the function of the sum of quantities (Lin at Para. [0038]: “cost of trajectory 112 being lower than that of trajectory 110 may be due to the adjustments to the initial cost volume 402 encouraging changing lane when following a cargo truck 108. Such adjustments may be learned from observed driving behavior which indicates that human drivers usually avoid following cargo trucks 108 closely and make lane changes to have a better field of view.”).
As per claim 8, Lin and Akella disclose a method of claim 1, wherein the function of the sum of the quantities is optimized when the function of the sum of the quantities achieves a global minimum for all of the plurality of weights applied to the plurality of cost dimensions (Lin at Para. [0023] discloses: “a trajectory that minimizes all these different costs may be determined as a planned trajectory the vehicle 100 should take going forward. Based on the hand-engineered costs, a cost volume may be generated. A cost volume, which may be based on space and time, may provide cost information associated with each location associated with the potential trajectory under different time. Instead of using hand-engineered costs, a cost volume may be directly learned from observed driving behavior by machine-learning models.”).
As per claim 9, Lin and Akella disclose a method of claim 1, wherein the function of the sum of the quantities is a total sum of the quantities determined for each cost dimension in the plurality of cost dimensions (Lin at Figure 4, adder, and Para. [0032] which discloses:” combining the delta cost volume 408 with the initial cost volume 402, the computing system may generate the finalized cost volume 410 that decreases the cost for the trajectory 122 going over the left boundary but increases the cost for the trajectory 120 close to the cyclist 118. Therefore, such trajectory may get as close as possible to a human driving trajectory.”).
As per claim 10, Lin and Akella disclose a method of claim 1, wherein the plurality of cost dimensions includes a control cost, nudge lateral cost, and a lateral jerk cost (Lin at Para. [0022], “the cost of the vehicle 100 driving with a high lateral acceleration or high lateral jerk may be also high because that would make it uncomfortable for passengers in the vehicle 100.”; Para. [0023]: “cost for a potential trajectory may be based on a set of hand-engineered costs determined based on movement restrictions of the vehicle 100” , which is the control cost; and, Para. [0032] which discloses the differences in cost for trajectories 102 & 122 and since the deviation is minor it would be considered a nudge lateral cost: “computing system may generate the finalized cost volume 410 that decreases the cost for the trajectory 122 going over the left boundary but increases the cost for the trajectory 120 close to the cyclist 118.”).
As per claim 11, Lin and Akella disclose a method of claim 1, wherein the data further comprises a second plurality of observed costs associated with the plurality of cost dimensions (Lin at Para. [0063], which discloses a “second contextual representation” of sensor data. ); and the method further comprises: determining the observed cost of the observed driving path by averaging, for each cost dimension in the plurality of cost dimensions, the first plurality of observed costs with the second plurality of observed costs (Lin at Para. [0063] discloses averaging by using feedback to re-perform the cost calculation until there is a convergence: “predicted contextual representation may then be compared to the known second contextual representation (i.e., the ground-truth at time t.sub.1). The comparison may be quantified by a loss value, computed using a loss function. The loss value may be used (e.g., via back-propagation techniques) to update the configuration parameters of the machine-learning model so that the loss would be less if the prediction were to be made again. The machine-learning model may be trained iteratively using a large set of training samples until a convergence or termination condition is met.”).
As per claim 12, Lin and Akella disclose a method of claim 1, the method further comprising: determining a plan for the AV based on the plurality of weights applied to the plurality of cost dimensions (Lin at Para. [0063] discloses averaging by using feedback to re-perform the cost calculation until there is a convergence: “predicted contextual representation may then be compared to the known second contextual representation (i.e., the ground-truth at time t.sub.1). The comparison may be quantified by a loss value, computed using a loss function. The loss value may be used (e.g., via back-propagation techniques) to update the configuration parameters of the machine-learning model so that the loss would be less if the prediction were to be made again. The machine-learning model may be trained iteratively using a large set of training samples until a convergence or termination condition is met.”), wherein the plan for the AV includes a human-behavior prediction portion and an AV-behavior prediction portion (Lin at Para. [0038] discloses:” adjustments may be learned from observed driving behavior which indicates that human drivers usually avoid following cargo trucks 108 closely and make lane changes to have a better field of view.”).
As per claim 12, Lin and Akella disclose a method of claim 12, the method further comprising: generating a first sparse plan distribution based on the human-behavior prediction portion of the plan for the AV (Lin at Para. [0033] discloses: “there may be a predetermined trajectory, e.g., a human driving trajectory associated with the environment that is considered as the best trajectory. The machine-learning model may comprise a delta cost function. Based on the initial cost volume 402 and the training data, the computing system may determine a predicted delta cost volume 408 using the machine-learning model. The computing system may then select a trajectory from the plurality of trajectories based on the initial cost volume 402 and the predicted delta cost volume 408..”); and
generating a second sparce plan distribution based on the AV-behavior prediction portion of the plan for the AV (Lin at Para. [0063] discloses averaging by using feedback to re-perform the cost calculation until there is a convergence: “predicted contextual representation may then be compared to the known second contextual representation (i.e., the ground-truth at time t.sub.1). The comparison may be quantified by a loss value, computed using a loss function. The loss value may be used (e.g., via back-propagation techniques) to update the configuration parameters of the machine-learning model so that the loss would be less if the prediction were to be made again. The machine-learning model may be trained iteratively using a large set of training samples until a convergence or termination condition is met.”).
As per claim 14, Lin discloses an autonomous vehicle control system for an autonomous vehicle (AV), the autonomous vehicle control system (Figs. 9-10) comprising:
one or more processors (Lin at Figure 10, processor 1002.); and
instructions that are executable by the one or more processors to cause the autonomous vehicle control system to perform operations, the operations (Lin at Figures 8-10, memory 1004, and Para. [0070]: “memory 1004 includes main memory for storing instructions for processor 1002 to execute or data for processor 1002 to operate on. As an example and not by way of limitation, computer system 1000 may load instructions from storage 1006 or another source (such as another computer system 1000) to memory 1004.”) comprising:
obtaining data describing an observed driving path in a driving scenario (Lin at Para. [0033]: “the computing system may train the machine-learning model based on a plurality of training data indicative of the observed driving behavior. The plurality of training data indicative of observed driving behavior may comprise captured sensor data comprising images, videos, LiDAR point clouds, radar signals, or any combination thereof.”), wherein the data comprises a first plurality of observed costs associated with a plurality of cost dimensions (Lin at Para. [0043]: “computing system may generate a delta cost volume 408using the initial cost volume 402 and environment data 404 associated with the environment, wherein the delta cost volume 408 is generated by determining adjustments to the initial cost volume 402 that incorporate observed driving behavior.”);
determining a function of a sum of quantities determined for each cost dimension in the plurality of cost dimensions (Lin at Figures 4 & 7 and Para. [0043]: “the computing system may generate a finalized cost volume 410 based on a summation of the initial cost volume 402 and the delta cost volume 408, wherein the finalized cost volume 410 comprises finalized cost measurements.”), wherein the function of the sum of the quantities indicates a margin by which an estimated cost for each cost dimension in the plurality of cost dimensions exceeds an observed cost of the observed driving path (Lin at Figure 7, step 702 “delta cost”, and Para. [0025] which describes a gap between the observed and estimated cost of a trajectory:” computing system may then generate a delta cost volume using the initial cost volume and environment data associated with the environment.”);
controlling a motion of the AV according to the adjustment to the one or more weights applied to the plurality of cost dimensions (Lin at Figure 7, steps 720 & 780, and Para. [0043]: “the delta cost volume 408 is generated by determining adjustments to the initial cost volume 402 that incorporate observed driving behavior.”).
Lin does not disclose but Akella discloses adjusting one or more weights of a plurality of weights applied to the plurality of cost dimensions according to the function of the sum of the quantities (Akella at Par. [0017]; i.e., a cost can be associated with a weight that can increase or decrease a cost associated with a trajectory or a cost associated with a point of the trajectory… a weight associated with a reference cost can be decreased when the vehicle is navigating around a double-parked vehicle or while changing lanes; the system calculates an adjustment to the cost based on the driving behavior); and
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 method of Lin to have further incorporated generating, using a machine learning model, a change in the one or more weights using the initial cost volume and environment data of the environment, as taught by Akella with a reasonable expectation of success because doing so would lead to increased safety and comfort in vehicle operations. The teaching suggestion/motivation to combine is that by changing the weights of the machine-learned model, a smoother transitions leading towards safer and/or more comfortable vehicle operations is realized. Akella at Para. [0017].
As per claim 15, Lin and Akella disclose an autonomous vehicle control system of claim 14, wherein the function comprises a plurality of learned parameters associated with the plurality of cost dimensions (Lin at Para. [0033]: “computing system may update the parameters of the machine-learning model.”), and wherein the operations further comprise updating the plurality of learned parameters to optimize an output of the function (Lin at Para. [0033] discloses: “ the computing system may update the predicted delta cost volume 408 using the updated machine-learning model, re-select a trajectory based on the initial cost volume 402 and the updated predicted delta cost volume 408, calculate the difference between the re-selected trajectory and the predetermined trajectory, output a loss based on the difference, and compare the loss with the loss in the t-th iteration.”).
As per claim 16, Lin and Akella disclose an autonomous vehicle control system of claim 14, wherein the function of the sum of the quantities comprises a respective margin slope for each cost dimension in the plurality of cost dimensions (Lin at Figures 4 & 7 and Para. [0043]: “the computing system may generate a finalized cost volume 410 based on a summation of the initial cost volume 402 and the delta cost volume 408, wherein the finalized cost volume 410 comprises finalized cost measurements.”), and wherein the operations further comprise:
setting a value of the respective margin slope for each cost dimension in the plurality of cost dimensions (Lin at Para. [0041] discloses gradually setting a value, delta cost, that tracks a certain change: ”gradually reduced velocity may be both specified by movement restrictions and reflected by observed driving behavior (i.e., human drivers often slow down when approaching a stop sign 140).”), and wherein adjusting the one or more weights in the plurality of weights is based on the respective margin slopes (Akella at Para. [0014] discloses: “weight associated with various costs can be based on triggers such as jumps or discontinuities in a reference trajectory or obstacle costs meeting or exceeding a threshold.”).
As per claim 17, Lin and Akella disclose an autonomous vehicle control system of claim 14, wherein the function of the sum of the quantities is optimized when the function of the sum of the quantities achieves a global minimum for all of the plurality of weights applied to the plurality of cost dimensions (Lin at Para. [0023] discloses: “a trajectory that minimizes all these different costs may be determined as a planned trajectory the vehicle 100 should take going forward. Based on the hand-engineered costs, a cost volume may be generated. A cost volume, which may be based on space and time, may provide cost information associated with each location associated with the potential trajectory under different time. Instead of using hand-engineered costs, a cost volume may be directly learned from observed driving behavior by machine-learning models.”).
As per claim 18, Lin and Akella disclose an autonomous vehicle control system of claim 14, wherein the function of the sum of the quantities is a total sum of the quantities determined for each cost dimension in the plurality of cost dimensions (Lin at Figure 4, adder, and Para. [0032] which discloses:” combining the delta cost volume 408 with the initial cost volume 402, the computing system may generate the finalized cost volume 410 that decreases the cost for the trajectory 122 going over the left boundary but increases the cost for the trajectory 120 close to the cyclist 118. Therefore, such trajectory may get as close as possible to a human driving trajectory.”).
As per claim 19, Lin and Akella disclose an autonomous vehicle control system of claim 14, wherein the data further comprises a second plurality of observed costs associated with the plurality of cost dimensions (Lin at Para. [0063], which discloses a “second contextual representation” of sensor data. ); and the operations further comprise: determining the observed cost of the observed driving path by averaging, for each cost dimension in the plurality of cost dimensions, the first plurality of observed costs with the second plurality of observed costs (Lin at Para. [0063] discloses averaging by using feedback to re-perform the cost calculation until there is a convergence: “predicted contextual representation may then be compared to the known second contextual representation (i.e., the ground-truth at time t.sub.1). The comparison may be quantified by a loss value, computed using a loss function. The loss value may be used (e.g., via back-propagation techniques) to update the configuration parameters of the machine-learning model so that the loss would be less if the prediction were to be made again. The machine-learning model may be trained iteratively using a large set of training samples until a convergence or termination condition is met.”);
As per claim 21, Lin discloses One or more non-transitory computer-readable media that store (Figure 10, Memory 1004 instructions that are executable by one or more processors to cause a control system for an autonomous vehicle (AV) to perform operations, the operations comprising:
obtaining data describing an observed driving path in a driving scenario (Lin at Para. [0033]: “the computing system may train the machine-learning model based on a plurality of training data indicative of the observed driving behavior. The plurality of training data indicative of observed driving behavior may comprise captured sensor data comprising images, videos, LiDAR point clouds, radar signals, or any combination thereof.”), wherein the data comprises a first plurality of observed costs associated with a plurality of cost dimensions (Lin at Para. [0043]: “computing system may generate a delta cost volume 408using the initial cost volume 402 and environment data 404 associated with the environment, wherein the delta cost volume 408 is generated by determining adjustments to the initial cost volume 402 that incorporate observed driving behavior.”);
determining a function of a sum of quantities determined for each cost dimension in the plurality of cost dimensions (Lin at Figures 4 & 7 and Para. [0043]: “the computing system may generate a finalized cost volume 410 based on a summation of the initial cost volume 402 and the delta cost volume 408, wherein the finalized cost volume 410 comprises finalized cost measurements.”), wherein the function of the sum of the quantities indicates a margin by which an estimated cost for each cost dimension in the plurality of cost dimensions exceeds an observed cost of the observed driving path (Lin at Figure 7, step 702 “delta cost”, and Para. [0025] which describes a gap between the observed and estimated cost of a trajectory:” computing system may then generate a delta cost volume using the initial cost volume and environment data associated with the environment.”);
controlling a motion of the AV according to the adjustment to the one or more weights applied to the plurality of cost dimensions (Lin at Figure 7, steps 720 & 780, and Para. [0043]: “the delta cost volume 408 is generated by determining adjustments to the initial cost volume 402 that incorporate observed driving behavior.”).
Lin does not disclose but Akella discloses adjusting one or more weights of a plurality of weights applied to the plurality of cost dimensions according to the function of the sum of the quantities (Akella at Par. [0017]; i.e., a cost can be associated with a weight that can increase or decrease a cost associated with a trajectory or a cost associated with a point of the trajectory… a weight associated with a reference cost can be decreased when the vehicle is navigating around a double-parked vehicle or while changing lanes; the system calculates an adjustment to the cost based on the driving behavior); and
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 method of Lin to have further incorporated generating, using a machine learning model, a change in the one or more weights using the initial cost volume and environment data of the environment, as taught by Akella with a reasonable expectation of success because doing so would lead to increased safety and comfort in vehicle operations. The teaching suggestion/motivation to combine is that by changing the weights of the machine-learned model, a smoother transitions leading towards safer and/or more comfortable vehicle operations is realized. Akella at Para. [0017].
CONCLUSION
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Pierson et al (US-20190354109-A1) discloses a method for use in a planning agent using a plurality of occupancy costs that are based on a cost function that depends on the location of a first agent and the velocity of the first agent; and changing at least one of speed or direction of travel of the planning agent based on the set of occupancy costs. See Figures 3A-6A and Abstract.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELLIS B. RAMIREZ whose telephone number is (571)272-8920. The examiner can normally be reached 7:30 am to 5:00pm.
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/ELLIS B. RAMIREZ/Primary Examiner, Art Unit 3658
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