DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in response to the application filed on ----6/28/2024 for application 18/759,305. Claim 1 – 20 are pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 6/28/2024, 9/16/2025, 11/24/2025, 1/5/2026, 2/11/2026 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
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.
Claim(s) 1 – 2, 7 – 8, 13 – 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kracun, WO2021126170 in view of Chen GB2640413 with evidential reference of Matheson, “System trains driverless cars in simulation before they hit the road”.
Claim 1. Kracun discloses: A system comprising: one or more processors; and one or more non-transitory memory storing processor-executable instructions that (0026, “a system including processing hardware and non-transitory computer-readable medium storing instructions”), when executed by the one or more processors, cause the system to perform operations comprising:
receiving a set of clusters associated with an embedding space (00101, “the machine learning module 134 (or another suitable module of the navigation system) may cluster similar locations for each maneuver using a clustering algorithm based on similarities in success statistics for maneuver execution. For example, locations 322-326 may be clustered to the same cluster for the corresponding illustrated right turns. A machine learning model for generating a metric of probability may be trained separately for each cluster (i.e., location class generated by the clustering algorithm). Within each class, the different locations may be interrelated by correlation matrices that may be specific to each maneuver, to certain conditions, etc.”; i.e., the system of Kracun use clustering model to find the cluster that the current scenario belongs to and control the vehicle based on the cluster), … embeddings generated by a machine-learned model using a set of scenario data determined based at least in part on sensor data received from a first vehicle (0086, “The machine learning module 134 in these scenarios generates metrics of difficulty for a maneuver by training a model to recognize visual similarities (e.g., using satellite imagery or street-level imagery), similarities in road geometry (e.g., using schematic map data, satellite imagery, data from vehicle sensors)”; i.e., using/receiving sensor data of vehicle);
receiving a set of difficulty metrics associated with the set of clusters, wherein a first difficulty metric is associated with the first cluster and indicates an average predicted likelihood that an adverse event will occur (0041, “The metric of difficulty for a maneuver in some cases indicates the probability that an operator will execute the maneuver successfully”; i.e., the difficulty metric is relating to the probability/likelihood of failure/adverse events) during simulation of operation of a simulated vehicle in a subset of simulated scenarios associated with the first cluster (Examiner notes that one of ordinary skilled in the art would recognize that training a machine learning model using sensor data to make decision and control a vehicle is to simulate the operation of the vehicle. Such understanding can be easily found online for example: Matheson, “System trains driverless cars in simulation before they hit the road”);
receiving sensor data at a second vehicle; determining, by the machine-learned model based at least in part on the sensor data, an embedding; determining that the embedding is associated with the region identified by the first cluster (0018, “Receiving the dataset may include receiving at least one of (i) satellite imagery, (ii) map data, or (iii) vehicle sensor data for the plurality of locations and the location indicated in the query; training the machine-learning model includes applying, by the one or more processors, a feature (embeddings) extraction function to the data set to determine road geometry at the corresponding locations”; machine learning model is used to extract embeddings from sensor data); and
controlling the second vehicle based at least in part on the first difficulty metric (0007, “implemented in an autonomous ( or "self-driving") vehicle to adjust the manner in which the autonomous vehicle executes a maneuver in view of the determined difficulty of the maneuver”; i.e., based on the difficulty metric, the system control vehicle in different ways).
Kracun does not explicitly teach:
wherein a first cluster of the set of clusters identifies a region in the embedding space associated with embeddings
Chen, in the same field of endeavor, explicitly teach:
wherein a first cluster of the set of clusters identifies a region in the embedding space associated with embeddings (Chen, 0026, “the similarity condition is satisfied comprises determining whether a distance in feature embedding space between the feature embedding of the driving scene data and the feature embedding of the reference driving scene data is less than a predetermined maximum similarity distance”; i.e., for each given reference, the cluster is a region in the embedding space that satisfy similarity condition)
Kracun and Chen both teach driving scene grouping and clustering and are analogous. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable likelihood of success to further include the details of the clustering technique taught by Chen in the system of Kracun to achieve the claimed teaching. One of the ordinary skill in the art would have motivated to make this modification so that “fast determination of similarity may thus be achieved” (Chen 0026).
Chen 2. Kracun and Chen combination renders obviousness of all the limitation of Claim 1. The combination further teach: the machine-learned model determined embeddings for the subset of simulated scenarios; the first cluster was determined based at least in part on the embeddings; and the embeddings are located within the region indicated by the first cluster (refer to the mapping in Claim 1 & Chen, 0010, “data collected by autonomous vehicles may be used to train machine learning models such as machine learning motion planners or driving policies. It may therefore be advantageous to be able to selectively collect data that relates to a driving scene that may be particularly useful for training purposes”; i.e., the system collect training data (scenario for training/simulation) based on the similarity of the embedded features).
Regarding Claim 7 – 8, these are the corresponding non-transitory computer readable media claim of Claim 1 – 2 and thus are rejected with same reason.
Claim 13. Kracun and Chen combination renders obviousness of all the limitation of Claim 7. The combination further teach: wherein determining the embedding is associated with the region comprises determining: the embedding is within the region, the embedding is within a threshold distance of a portion of the region, or the first cluster is a nearest cluster to the embedding from among the set of clusters (refer to the mapping in Claim 1 & Chen 0026, “predetermined maximum similarity distance (threshold distance)”).
Claim 14. Kracun and Chen combination renders obviousness of all the limitation of Claim 7. The combination further teach: wherein the first difficulty metric comprises at least one of: a first likelihood that simulating operation of the second vehicle in a first scenario of the set of scenario data will result in the second vehicle contacting an object; a second likelihood that simulating operation of the second vehicle in the first scenario will result in an acceleration or jerk of the second vehicle that meets or exceeds a threshold acceleration or threshold jerk; or a third likelihood that simulating operation of the second vehicle in the first scenario will result in the second vehicle idling, altering or ending a mission, or violating an operating constraint (refer to the mapping in Claim 1 & Kracun, 0041, “the probability that an operator will execute the maneuver successfully”, i.e., the probability/likelihood that the mission of the vehicle will not finish without altering or abort).
Regarding Claim 15 – 16, these are the corresponding method claim of Claim 1 – 2 and thus are rejected with same reason.
Claim(s) 3 – 4, 9 – 10, 17 – 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kracun, WO2021126170 in view of Chen GB2640413 with evidential reference of Matheson, “System trains driverless cars in simulation before they hit the road” as applied to Claim 1 and further in view of Montanari, “Improving AI via optimal selection of training samples”.
Claim 3. Kracun and Chen combination renders obviousness of all the limitation of Claim 1. The combination does not explicitly teach: the operations further comprise determining that the first difficulty metric violates a constraint, wherein violating the constraint comprises at least one of the first difficulty metric meeting or exceeding a threshold difficulty metric or an average difficulty metric across difficulty metrics determined for other clusters; and controlling the second vehicle based at least in part on the first difficulty metric comprises at least one of: removing a location from a set of locations the second vehicle is permitted to use for trajectory planning; increasing processing or memory allocation for operation planning by the second vehicle; increasing a cost associated with a candidate trajectory or removing the candidate trajectory from a set of candidate trajectories; increasing a number of the set of candidate trajectories; removing a maneuver from a set of maneuvers available for controlling the second vehicle; decreasing at least one of a maximum speed or a maximum acceleration for controlling the second vehicle; transmitting log data comprising at least part of the sensor data to a remote computing device; or transmitting a request for input from the remote computing device.
Montanari, in the same field of endeavor, explicitly teach:
the operations further comprise determining that the first difficulty metric violates a constraint, wherein violating the constraint comprises at least one of the first difficulty metric meeting or exceeding a threshold difficulty metric or an average difficulty metric across difficulty metrics determined for other clusters; and controlling the second vehicle based at least in part on the first difficulty metric comprises at least one of: removing a location from a set of locations the second vehicle is permitted to use for trajectory planning; increasing processing or memory allocation for operation planning by the second vehicle; increasing a cost associated with a candidate trajectory or removing the candidate trajectory from a set of candidate trajectories; increasing a number of the set of candidate trajectories; removing a maneuver from a set of maneuvers available for controlling the second vehicle; decreasing at least one of a maximum speed or a maximum acceleration for controlling the second vehicle; transmitting log data comprising at least part of the sensor data to a remote computing device; or transmitting a request for input from the remote computing device (Montanari, page 6, “Uncertainty-based subsampling is effective”, “The simplest rule of thumb emerging from our work broadly confirms earlier research: subsampling schemes based on the sample ‘hardness’, i.e. on how uncertain surrogate predictions are, perform well in a broad array of settings.”; Kracun and Chen combination teaches a difficulty metric estimation method (See mapping in Claim 1 & Kracun reference) and training data sampling scheme that “selectively collect data that relates to a driving scene that may be particularly useful for training purposes” (Chen 0010), Montanari teaches using hardness measure to sample training data as illustrated in the figure of page 3. The combination renders obviousness that for the driving data that has difficulty metric higher than a threshold, transmitting log data comprising at least part of the sensor data to a remote computing device for store as training sample).
Kracun (in view of Chen) and Montanari both teach training data sampling using hardness/difficulty measurement and are analogous. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable likelihood of success to further include the details of the selection scheme taught by Montanari in the system of Kracun (in view of Chen) to achieve the claimed teaching. One of the ordinary skill in the art would have motivated to make this modification to “elevate model performance while simultaneously slashing the substantial costs tied to infrastructure computation and storage needed to manage vast dataset” (Montanari, page 7).
Claim 4. Kracun and Chen combination renders obviousness of all the limitation of Claim 1. The combination does not explicitly teach: the operations further comprise determining that the first difficulty metric satisfies a constraint, wherein satisfying the constraint comprises at least one of the first difficulty metric being at or below a threshold difficulty metric or an average difficulty metric across difficulty metrics determined for other clusters; and controlling the second vehicle based at least in part on the first difficulty metric comprises at least one of: decreasing a cost associated with a candidate trajectory; adding a location to a set of locations the second vehicle is permitted to use for trajectory planning; adding a maneuver to a set of maneuvers available for controlling the second vehicle; decreasing a number of a set of candidate trajectories; or suppressing submission of log data to a remote computing device.
Montanari, in the same field of endeavor, explicitly teach:
the operations further comprise determining that the first difficulty metric satisfies a constraint, wherein satisfying the constraint comprises at least one of the first difficulty metric being at or below a threshold difficulty metric or an average difficulty metric across difficulty metrics determined for other clusters; and controlling the second vehicle based at least in part on the first difficulty metric comprises at least one of: decreasing a cost associated with a candidate trajectory; adding a location to a set of locations the second vehicle is permitted to use for trajectory planning; adding a maneuver to a set of maneuvers available for controlling the second vehicle; decreasing a number of a set of candidate trajectories; or suppressing submission of log data to a remote computing device (refer to the mapping in Claim 3, the combination renders obviousness to suppressing submission of driving/log data to a remote computing device for those driving/log data that the difficulty metric is less than a threshold).
The reason for combination is same as Claim 3.
Regarding Claim 9 – 10, these are the corresponding non-transitory computer readable media claim of Claim 3 – 4 and thus are rejected with same reason.
Regarding Claim 17 – 18, these are the corresponding method claim of Claim 3 – 4 and thus are rejected with same reason.
Claim(s) 5, 11, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kracun, WO2021126170 in view of Chen GB2640413 with evidential reference of Matheson, “System trains driverless cars in simulation before they hit the road” as applied to Claim 1 and further in view of Van Heukelom et al., (hereinafter Van Heukelom), US11433922.
Claim 5. . Kracun and Chen combination renders obviousness of all the limitation of Claim 1. The combination further teach: the machine-learned model is a first machine-learned model and the operations further comprise: receiving a candidate trajectory for controlling the second vehicle (refer to the mapping in Claim 1 & Chen 0067, “driving policy may be a trained machine learning driving policy which takes as input sensor data (for example relating to the vehicle environment and vehicle state) and outputs data indicative of a trajectory for the autonomous vehicle to follow during a subsequent time period”); … determining, by the first machine-learned model and based at least in part on the predicted state, a second embedding; and determining that the second embedding is associated with the region identified by the first cluster (refer to the mapping in Claim 1 & Chen 0067, “driving policy may be a trained machine learning driving policy which takes as input sensor data (for example relating to the vehicle environment and vehicle state) and outputs data indicative of a trajectory for the autonomous vehicle to follow during a subsequent time period.” )
The combination does not explicitly teach:
determining, by a second machine-learned model, a predicted state of a set of objects;
wherein controlling the second vehicle based at least in part on the first difficulty metric comprises: discarding or increasing a cost associated with the candidate trajectory based at least in part on determining that the first difficulty metric meets or exceeds a threshold difficulty metric, or decreasing a cost associated with the candidate trajectory based at least in part on determining that the first difficulty metric is less than the threshold difficulty metric.
Van Heukelom, in the same field of endeavor, explicitly teach:
determining, by a second machine-learned model, a predicted state of a set of objects (Van Heukelom col. 9, ln 47 – 60, “machine-learned model can output the uncertainty metric associated with the object 108”);
wherein controlling the second vehicle based at least in part on the first difficulty metric comprises: discarding or increasing a cost associated with the candidate trajectory based at least in part on determining that the first difficulty metric meets or exceeds a threshold difficulty metric, or decreasing a cost associated with the candidate trajectory based at least in part on determining that the first difficulty metric is less than the threshold difficulty metric (col. 9, ln 61 – col. 10, ln 9, “In some instances, the computing device can use an uncertainty metric threshold to determine if the uncertainty metric meets or exceeds the uncertainty metric threshold. If the uncertainty metric meets or exceeds the uncertainty metric threshold, the computing device can control the vehicle to perform an action such as determine a trajectory that avoids the object 108”; examiner notes that Kracun teaches the difficult metric that relates to the uncertainty of success/failure maneuver of vehicle, Van Heukelom teaches discarding current trajectory if the uncertainty of the environment exceeds a threshold. The combination renders obviousness of the claimed limitation).
Kracun (in view of Chen) and Van Heukelom both teach autonomous driving considering the risk/uncertainty of the environment and are analogous. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable likelihood of success to further include the control policy of the Van Heukelom in the system of Kracun (in view of Chen) to achieve the claimed teaching. One of the ordinary skill in the art would have motivated to make this modification “to determine a safe and efficient trajectory for controlling a vehicle” (Van Heukelom, col 4, ln 35 - 48).
Regarding Claim 11 and 19, these are the corresponding non-transitory computer readable media claim and method claim of Claim 5 and thus are rejected with same reason.
Claim(s) 6, 12, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kracun, WO2021126170 in view of Chen GB2640413, Van Heukelom et al., (hereinafter Van Heukelom), US11433922 as applied to Claim 5 and further in view of Narayanan et al., (hereinafter Narayanan), US20240174256.
Claim 6. . Kracun and Chen combination renders obviousness of all the limitation of Claim 5. The combination does not explicitly teach: wherein determining the second embedding is further based at least in part on one or more of: a preliminary cost associated with the candidate trajectory being below a threshold cost; a layer of a tree search associated with the candidate trajectory comprises a multiple of n, where n is a positive integer; or the candidate trajectory comprises a default candidate trajectory from among a set of default trajectories.
Narayanan, in the same field of endeavor, explicitly teach:
wherein determining the second embedding is further based at least in part on one or more of: a preliminary cost associated with the candidate trajectory being below a threshold cost; a layer of a tree search associated with the candidate trajectory comprises a multiple of n, where n is a positive integer; or the candidate trajectory comprises a default candidate trajectory from among a set of default trajectories (Narayanan, 0004, “determining candidate trajectories and using a decision search tree to control a vehicle”; i.e., the system of Narayanan use decision tree to determine selected trajectory. As Narayanan fig. 5 illustrated, there is at least 1 (positive integer) of candidate trajectories).
Kracun (in view of Chen and Van Heukelom) and Narayanan both teach autonomous driving considering the environment and are analogous. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable likelihood of success to further include the tree trajectory planning technique of Narayanan in the system of Kracun (in view of Chen and Van Heukelom) to achieve the claimed teaching. One of the ordinary skill in the art would have motivated to make this modification to “optimize” the traveling of the vehicle (Narayanan, 0013).
Regarding Claim 12 and 20, these are the corresponding non-transitory computer readable media claim and method claim of Claim 6 and thus are rejected with same reason.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure: Hardik, “Active Learning Sampling Strategies” which teaches training batch sampling method that selectively include hard/difficulty samples.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHIEN MING CHOU whose telephone number is (571)272-9354. The examiner can normally be reached Monday- Friday 9 am - 5 pm.
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/SHIEN MING CHOU/Examiner, Art Unit 3667
/Hitesh Patel/Supervisory Patent Examiner, Art Unit 3667
4/28/26
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