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 .
Response to Amendment
Independent claims 1, 8, and 15 have been amended.
Claims 2, 9, 16, and 20 has been cancelled.
There are no new claims.
Claims 1, 3-8, 10-15, 17-19, and 21 are currently pending.
The official correspondence below is an after non-final.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 3-8, 10-15, 17-19, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Crego (US 11741274 B1) in view of Elli (US 20220114458 A1), in further view of Hyde (US 20210300425 A1) and Capell (US 12204823 B1).
REGARDING CLAIM 1, Crego discloses, at least one memory (Crego: [FIG. 2(214)(224)]); and at least one processor coupled to the at least one memory (Crego: [FIG. 2(214)(224)]), the at least one processor configured to: receive sensor data (Crego: the perception component may comprise hardware and/or software for receiving sensor data from one or more sensors of an autonomous vehicle and detecting one or more objects in an environment associated with the autonomous vehicle and/or characteristics associated with the one or more objects (Col. 2, Ln. 51-56)); determine a ground-truth perception output (Crego: Ground truth data may comprise manually labeled, automatically-labeled (e.g., via a machine-learned model pipeline trained for the task of ground truth generation) (Col. 8, Ln. 34-36)); and control, based on the ground-truth perception output, operation of a vehicle propulsion system (Crego: (Col. 9, Ln. 26-34) FIG. 1 depicts an example of such a trajectory 132, represented as an arrow indicating a heading, velocity, and/or acceleration, although the trajectory itself may comprise instructions for a controller, which may, in turn, actuate a drive system of the vehicle 102. For example, the trajectory 132 may comprise instructions for controller(s) 134 of the autonomous vehicle 102 to actuate drive components of the vehicle 102 to effectuate a steering angle and/or steering rate; (Col. 18, Ln. 46-47) generate instructions for controlling steering, propulsion, braking), a vehicle braking system (Crego: (Col. 9, Ln. 26-34) FIG. 1 depicts an example of such a trajectory 132, represented as an arrow indicating a heading, velocity, and/or acceleration, although the trajectory itself may comprise instructions for a controller, which may, in turn, actuate a drive system of the vehicle 102. For example, the trajectory 132 may comprise instructions for controller(s) 134 of the autonomous vehicle 102 to actuate drive components of the vehicle 102 to effectuate a steering angle and/or steering rate; (Col. 18, Ln. 46-47) generate instructions for controlling steering, propulsion, braking), and a vehicle steering system during navigation of a vehicle (Crego: (Col. 9, Ln. 26-34) FIG. 1 depicts an example of such a trajectory 132, represented as an arrow indicating a heading, velocity, and/or acceleration, although the trajectory itself may comprise instructions for a controller, which may, in turn, actuate a drive system of the vehicle 102. For example, the trajectory 132 may comprise instructions for controller(s) 134 of the autonomous vehicle 102 to actuate drive components of the vehicle 102 to effectuate a steering angle and/or steering rate; (Col. 18, Ln. 46-47) generate instructions for controlling steering, propulsion, braking).
Crego does not explicitly disclose, a dual system.
However, in the same field of endeavor, Elli discloses, provide the sensor data to a plurality of validation modules (Elli: [0063] A first EE module 406a of the first perception task module 402a may generate a first estimation 409a. The first estimation 409a may include a first error distribution for the first task data. The first EE module 406a may estimate the first error distribution for the first perception task and the first sensor data; [0065] A second EE module 406b of the second perception task module 402b may generate a second estimation 409b. The second estimation 409b may include a second error distribution for the second task data. The second EE module 406b may estimate the second error distribution for the second perception task and the second sensor data; [0079] The NELR module 604 may be trained by comparing the pre-identified latent representations 601 to the ground truth labels; [FIG. 4 (406a)(406b)]) and a plurality of perception modules (Crego: [FIG. 4(402a)(402b)]), wherein a first validation module among the plurality of validation modules has a first network architecture (Elli: [FIG. 4]), wherein a second validation module among the plurality of validation modules has a second network architecture (Elli: [FIG. 4]), wherein the first network architecture is distinct from the second network architecture (Elli: [FIG. 4]), and wherein each of the plurality of perception modules generates a perception output (Elli: [FIG. 4(409a)(409b)]); receive, from the plurality of validation modules, a first set of perception outputs (Elli: [FIG. 4(409a)(409b)]); receive, from the plurality of perception modules, a second set of perception outputs (Elli: [FIG. 4(409a)(409b)]); and employ a consensus algorithm (Elli: [0097] The NELR module may include a ML algorithm or an AI algorithm. The ML algorithm or the AI algorithm may be selected based on the type of sensor data and the ground truth labels in the DSD. For example, if the sensor data includes RGB data, the ML algorithm of the NELR module may include a convolutional neural network (CNN). The CNN may be trained to include a general loss function for regression (e.g., a mean squared error), an optimizer (e.g. a stochastic gradient descent or ADAM)), or some combination thereof; [0019] include closed solutions in probability density function) evaluating the first and second set of perception outputs (Elli: [0066] A safety monitor 416 may receive the first environmental model 412a, the second environmental model 412b, the fused environment model 414, the first estimation 409a, the second estimation 409b, or some combination thereof. The safety monitor 416 may identify safety issues for operation of the vehicle within the environment based on the first environmental model 412a, the second environmental model 412b, the fused environment model 414, the first estimation 409a, the second estimation 409b, or some combination thereof) from the plurality of validation modules (Elli: [FIG. 4(402a)(402b)]) and the plurality of perception modules, respectively (Elli: [FIG. 4 (406a)(406b)]), for the benefit of multimodal automatic mapping of sensing defects to task-specific (examiner: first (camera), second (radar/lidar)) error measurements.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus disclosed by Crego to include two modules taught by Elli. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to multimodal automatic mapping of sensing defects to task-specific (examiner: first (camera), second (examiner: radar/lidar)) error measurements.
Crego, as modified, does not explicitly disclose, the consensus algorithm comprises a weighted voting consensus approach an historic accuracy of the corresponding validation module of the plurality of validation modules, and versioning information associated with the corresponding validation module of the plurality of validation modules.
However, in the same field of endeavor, Hyde discloses, the consensus algorithm comprises a weighted voting consensus approach (Hyde: [0055] monitoring circuitry can assign a certain weight to the first output … and a fourth functional circuitry generated a fourth output using a deterministic algorithm, the monitoring circuitry can weigh the consistency of the fourth output more heavily) an historic accuracy of the corresponding validation module of the plurality of validation modules (Hyde: [0053] if a monitoring circuit receives five outputs where the first three outputs do not recognize an object in an environment and the last two outputs do recognize an object in the environment, the monitoring circuit can still find a sufficient level of consistency between the results, as the consistency of the last two outputs can be weighed more heavily as they are more temporally relevant than the first three outputs. As such, the temporal recency of the outputs can be considered and utilized in the weighting of consistency between outputs by the monitoring circuit; [0152] the consistency of the last two outputs (e.g., output data 810D) can be weighed more heavily as they are more temporally relevant than the first two outputs (e.g., output data 810A-810B). As such, the temporal recency of the outputs can be considered and utilized in the weighting of consistency between outputs by the monitoring circuitry ... [0154] the monitoring circuitry 812 can weigh the consistency of various outputs based on the algorithm), and versioning information associated with the corresponding validation module of the plurality of validation modules (Hyde: [0048] The world state can describe a perception of the environment external to the autonomous vehicle. The second functional circuitry generate a first output validation for the first output in the same manner; [0088] the perception system 124 can update the state data 130 for each object at each iteration), for the benefit of determining a threshold level of difference (consensus) to compute a proper vehicle response.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus disclosed by a modified Crego to include consensus validation taught by Hyde. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to determine a threshold level of difference (consensus) to compute a proper vehicle response.
Crego, as modified, does not explicitly disclose, a weight of a given perception output is based on an amount of time that a corresponding validation module of the plurality of validation modules has been deployed.
However, in the same fields of endeavor, Capell discloses, a weight of a given perception output is based on an amount of time that a corresponding validation module of the plurality of validation modules has been deployed (Capell: The simulation log may be stored in the database of simulation data 212 storing a historical log of simulation runs indexed by corresponding run ID and/or batch ID ... the simulation result and/or a simulation log may be used as training data for machine learning engine (Col. 11, Ln. 62-66); the machine learning engine 166 may compare the predicted machine learning model output with a machine learning model known output (e.g., simulated output in the simulation scenario) from the training instance and, using the comparison, update one or more weights in the machine learning model 224 ... one or more weights may be updated by backpropagating the difference over the entire machine learning model (Col. 13, Ln. 20-28)), for the benefit of creating a perception validation scenario to create or refine a perception model used for controlling the operation of autonomous vehicles.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus disclosed by a modified Crego to include determining a threshold level of difference (consensus) to compute a proper vehicle response taught by Capell. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to create a perception validation scenario to create or refine a perception model used for controlling the operation of autonomous vehicles.
Crego, as modified, does not explicitly disclose, at least one validation module of the plurality of validation modules is trained or updated off-line using training data collected from multiple fleet autonomous vehicles.
However, in the same field of endeavor, Armstrong-Crews discloses, at least one validation module of the plurality of validation modules is trained or updated off-line using training data collected from multiple fleet autonomous vehicles (Armstrong-Crews: [0089] The evaluated information may also be shared with the other nearby self-driving vehicles and/or communicated with a back-end system for fleet-wide management, where it can be logged and utilized, e.g., as part of an offline training process), for the benefit of including how nearby vehicles and other road users react to the self-driving vehicle, can provide useful metrics; each self-driving vehicle can use that information to modify current driving operations or use it as part of a reinforcement learning approach for future driving situations.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus disclosed by a modified Crego to include offline updates taught by Armstrong-Crews. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to including how nearby vehicles and other road users react to the self-driving vehicle, can provide useful metrics; each self-driving vehicle can use that information to modify current driving operations or use it as part of a reinforcement learning approach for future driving situations.
REGARDING CLAIM 3, Crego, as modified, remains as applied above to claim 1, and further, Crego, as modified, also discloses each of the plurality of perception modules comprises a deep-learning neural network (Crego: Col. 18, Ln. 18-20).
REGARDING CLAIM 4, Crego, as modified, remains as applied above to claim 1, and further, Crego, as modified, also discloses the sensor data is collected using one or more autonomous vehicle (AV) mounted sensors (Crego: Col. 26, Ln. 35-39).
REGARDING CLAIM 5, Crego, as modified, remains as applied above to claim 1, and further, Crego, as modified, also discloses, each of the perception modules comprises a machine-learning model that has been trained on different training data (Crego: Col. 8, Ln. 34-36).
REGARDING CLAIM 6, Crego, as modified, remains as applied above to claim 1, and further, Crego, as modified, also discloses each of the perception modules comprises a machine-learning model that has been trained using a different training paradigm (Elli: [0080]; [0166]).
REGARDING CLAIM 7, Crego, as modified, remains as applied above to claim 1, and further, Crego, as modified, also discloses the sensor data comprises: camera data, Light Detection and Ranging (LiDAR) data, radar data, or a combination thereof (Crego: Col. 11, Ln. 21-25).
REGARDING CLAIM 8, Crego discloses, receive sensor data (Crego: (Col. 2, Ln. 51-56)); to determine a ground-truth perception output (Crego: (Col. 8, Ln. 34-36)); and control, based on the ground-truth perception output, operation of a vehicle propulsion system (Crego: (Col. 9, Ln. 26-34); (Col. 18, Ln. 46-47)), a vehicle braking system (Crego: (Col. 9, Ln. 26-34); (Col. 18, Ln. 46-47)), and a vehicle steering system during navigation of a vehicle (Crego: (Col. 9, Ln. 26-34); (Col. 18, Ln. 46-47)).
Crego does not explicitly disclose, a dual system.
However, in the same field of endeavor, Elli discloses, provide the sensor data to a plurality of validation modules (Elli: [0063]; [0065]; [0079]; [FIG. 4 (406a)(406b)]) and a plurality of perception modules (Crego: [FIG. 4(402a)(402b)]), wherein a first validation module among the plurality of validation modules has a first network architecture (Elli: [FIG. 4]), wherein a second validation module among the plurality of validation modules has a second network architecture (Elli: [FIG. 4]), wherein the first network architecture is distinct from the second network architecture (Elli: [FIG. 4]), and wherein each of the plurality of perception modules generates a perception output (Elli: [FIG. 4(409a)(409b)]); receive, from the plurality of validation modules, a first set of perception outputs (Elli: [FIG. 4(409a)(409b)]); receive, from the plurality of perception modules, a second set of perception outputs (Elli: [FIG. 4(409a)(409b)]); and employ a consensus algorithm (Elli: [0097]; [0019]) evaluating the first and second set of perception outputs (Elli: [0066]) from the plurality of validation modules (Elli: [FIG. 4(402a)(402b)]) and the plurality of perception modules, respectively (Elli: [FIG. 4 (406a)(406b)]), for the benefit of multimodal automatic mapping of sensing defects to task-specific (examiner: first (camera), second (radar/lidar)) error measurements.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus disclosed by Crego to include two modules taught by Elli. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to multimodal automatic mapping of sensing defects to task-specific (examiner: first (camera), second (examiner: radar/lidar)) error measurements.
Crego, as modified, does not explicitly disclose, the consensus algorithm comprises a weighted voting consensus approach an historic accuracy of the corresponding validation module of the plurality of validation modules, and versioning information associated with the corresponding validation module of the plurality of validation modules.
However, in the same field of endeavor, Hyde discloses, the consensus algorithm comprises a weighted voting consensus approach (Hyde: [0055]) an historic accuracy of the corresponding validation module of the plurality of validation modules (Hyde: [0053]; [0152-0154]), and versioning information associated with the corresponding validation module of the plurality of validation modules (Hyde: [0048]; [0088]), for the benefit of determining a threshold level of difference (consensus) to compute a proper vehicle response.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus disclosed by a modified Crego to include consensus validation taught by Hyde. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to determine a threshold level of difference (consensus) to compute a proper vehicle response.
Crego, as modified, does not explicitly disclose, a weight of a given perception output is based on an amount of time that a corresponding validation module of the plurality of validation modules has been deployed.
However, in the same fields of endeavor, Capell discloses, a weight of a given perception output is based on an amount of time that a corresponding validation module of the plurality of validation modules has been deployed (Capell: (Col. 11, Ln. 62-66); (Col. 13, Ln. 20-28)), for the benefit of creating a perception validation scenario to create or refine a perception model used for controlling the operation of autonomous vehicles.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus disclosed by a modified Crego to include determining a threshold level of difference (consensus) to compute a proper vehicle response taught by Capell. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to create a perception validation scenario to create or refine a perception model used for controlling the operation of autonomous vehicles.
Crego, as modified, does not explicitly disclose, at least one validation module of the plurality of validation modules is trained or updated off-line using training data collected from multiple fleet autonomous vehicles.
However, in the same field of endeavor, Armstrong-Crews discloses, at least one validation module of the plurality of validation modules is trained or updated off-line using training data collected from multiple fleet autonomous vehicles (Armstrong-Crews: [0089]), for the benefit of including how nearby vehicles and other road users react to the self-driving vehicle, can provide useful metrics; each self-driving vehicle can use that information to modify current driving operations or use it as part of a reinforcement learning approach for future driving situations.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus disclosed by a modified Crego to include offline updates taught by Armstrong-Crews. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to including how nearby vehicles and other road users react to the self-driving vehicle, can provide useful metrics; each self-driving vehicle can use that information to modify current driving operations or use it as part of a reinforcement learning approach for future driving situations.
REGARDING CLAIM 10, Crego, as modified, remains as applied above to claim 8, and further, Crego, as modified, also discloses each of the plurality of perception modules comprises a deep-learning neural network (Crego: Col. 18, Ln. 18-20).
REGARDING CLAIM 11, Crego, as modified, remains as applied above to claim 8, and further, Crego, as modified, also discloses the sensor data is collected using one or more autonomous vehicle (AV) mounted sensors (Crego: Col. 26, Ln. 35-39).
REGARDING CLAIM 12, Crego, as modified, remains as applied above to claim 8, and further, Crego, as modified, also discloses, each of the perception modules comprises a machine-learning model that has been trained on different training data (Crego: Col. 8, Ln. 34-36).
REGARDING CLAIM 13, Crego, as modified, remains as applied above to claim 8, and further, Crego, as modified, also discloses each of the perception modules comprises a machine-learning model that has been trained using a different training paradigm (Elli: [0080]; [0166]).
REGARDING CLAIM 14, Crego, as modified, remains as applied above to claim 8, and further, Crego, as modified, also discloses the sensor data comprises: camera data, Light Detection and Ranging (LiDAR) data, radar data, or a combination thereof (Crego: Col. 11, Ln. 21-25).
REGARDING CLAIM 15, Crego discloses, receive sensor data (Crego: (Col. 2, Ln. 51-56)); to determine a ground-truth perception output (Crego: (Col. 8, Ln. 34-36)); and control, based on the ground-truth perception output, operation of a vehicle propulsion system (Crego: (Col. 9, Ln. 26-34); (Col. 18, Ln. 46-47)), a vehicle braking system (Crego: (Col. 9, Ln. 26-34); (Col. 18, Ln. 46-47)), and a vehicle steering system during navigation of a vehicle (Crego: (Col. 9, Ln. 26-34); (Col. 18, Ln. 46-47)).
Crego does not explicitly disclose, a dual system.
However, in the same field of endeavor, Elli discloses, provide the sensor data to a plurality of validation modules (Elli: [0063]; [0065]; [0079]; [FIG. 4 (406a)(406b)]) and a plurality of perception modules (Crego: [FIG. 4(402a)(402b)]), wherein a first validation module among the plurality of validation modules has a first network architecture (Elli: [FIG. 4]), wherein a second validation module among the plurality of validation modules has a second network architecture (Elli: [FIG. 4]), wherein the first network architecture is distinct from the second network architecture (Elli: [FIG. 4]), and wherein each of the plurality of perception modules generates a perception output (Elli: [FIG. 4(409a)(409b)]); receive, from the plurality of validation modules, a first set of perception outputs (Elli: [FIG. 4(409a)(409b)]); receive, from the plurality of perception modules, a second set of perception outputs (Elli: [FIG. 4(409a)(409b)]); and employ a consensus algorithm (Elli: [0097]; [0019]) evaluating the first and second set of perception outputs (Elli: [0066]) from the plurality of validation modules (Elli: [FIG. 4(402a)(402b)]) and the plurality of perception modules, respectively (Elli: [FIG. 4 (406a)(406b)]), for the benefit of multimodal automatic mapping of sensing defects to task-specific (examiner: first (camera), second (radar/lidar)) error measurements.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus disclosed by Crego to include two modules taught by Elli. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to multimodal automatic mapping of sensing defects to task-specific (examiner: first (camera), second (examiner: radar/lidar)) error measurements.
Crego, as modified, does not explicitly disclose, the consensus algorithm comprises a weighted voting consensus approach an historic accuracy of the corresponding validation module of the plurality of validation modules, and versioning information associated with the corresponding validation module of the plurality of validation modules.
However, in the same field of endeavor, Hyde discloses, the consensus algorithm comprises a weighted voting consensus approach (Hyde: [0055]) an historic accuracy of the corresponding validation module of the plurality of validation modules (Hyde: [0053]; [0152-0154]), and versioning information associated with the corresponding validation module of the plurality of validation modules (Hyde: [0048]; [0088]), for the benefit of determining a threshold level of difference (consensus) to compute a proper vehicle response.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus disclosed by a modified Crego to include consensus validation taught by Hyde. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to determine a threshold level of difference (consensus) to compute a proper vehicle response.
Crego, as modified, does not explicitly disclose, a weight of a given perception output is based on an amount of time that a corresponding validation module of the plurality of validation modules has been deployed.
However, in the same fields of endeavor, Capell discloses, a weight of a given perception output is based on an amount of time that a corresponding validation module of the plurality of validation modules has been deployed (Capell: (Col. 11, Ln. 62-66); (Col. 13, Ln. 20-28)), for the benefit of creating a perception validation scenario to create or refine a perception model used for controlling the operation of autonomous vehicles.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus disclosed by a modified Crego to include determining a threshold level of difference (consensus) to compute a proper vehicle response taught by Capell. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to create a perception validation scenario to create or refine a perception model used for controlling the operation of autonomous vehicles.
Crego, as modified, does not explicitly disclose, at least one validation module of the plurality of validation modules is trained or updated off-line using training data collected from multiple fleet autonomous vehicles.
However, in the same field of endeavor, Armstrong-Crews discloses, at least one validation module of the plurality of validation modules is trained or updated off-line using training data collected from multiple fleet autonomous vehicles (Armstrong-Crews: [0089]), for the benefit of including how nearby vehicles and other road users react to the self-driving vehicle, can provide useful metrics; each self-driving vehicle can use that information to modify current driving operations or use it as part of a reinforcement learning approach for future driving situations.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus disclosed by a modified Crego to include offline updates taught by Armstrong-Crews. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to including how nearby vehicles and other road users react to the self-driving vehicle, can provide useful metrics; each self-driving vehicle can use that information to modify current driving operations or use it as part of a reinforcement learning approach for future driving situations.
REGARDING CLAIM 17, Crego, as modified, remains as applied above to claim 15, and further, Crego, as modified, also discloses each of the plurality of perception modules comprises a deep-learning neural network (Crego: Col. 18, Ln. 18-20).
REGARDING CLAIM 18, Crego, as modified, remains as applied above to claim 15, and further, Crego, as modified, also discloses the sensor data is collected using one or more autonomous vehicle (AV) mounted sensors (Crego: Col. 26, Ln. 35-39).
REGARDING CLAIM 19, Crego, as modified, remains as applied above to claim 15, and further, Crego, as modified, also discloses, each of the perception modules comprises a machine-learning model that has been trained on different training data (Crego: Col. 8, Ln. 34-36).
REGARDING CLAIM 21, Crego, as modified, remains as applied above to claim 1, and further, Crego, as modified, also discloses, the first validation module was trained using first training data (Elli: [0057]; [0063-0064]), and wherein the second validation module was trained using second training data (Elli: [0057]; [0065]) that differs from the first training data (Elli: [0077]; [0054-0057]).
Response to Arguments
Applicant’s arguments, beginning on page 7, submitted 06-08-2026, with respect to the rejection of the independent claims under 35 USC §101, noneligible subject matter, have been fully considered and are persuasive. While the independent claims do not explicitly state that the vehicles being controlled are self-driving (versus human operator responding to information on a screen), in light of the specification, the application is directed toward operating/controlling autonomous vehicles. Thus, the rejection of the independent claims under 35 USC §101, noneligible subject matter, has been withdrawn.
Applicant’s arguments with respect to the rejection of the independent claims under 35 USC §103, obviousness, have been considered but are moot because the new ground of rejection does not rely on the reference combination applied in the prior §103 rejection of record for matter specifically challenged in the argument.
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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/A.S./Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663
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