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
Claims 1, 8, and 15 have been amended.
Claim 20 previously cancelled.
Claims 1-19 and 21 are currently pending
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) 1-7, 15-19, 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).
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)), wherein the sensor data corresponds with an environment around an autonomous vehicle (AV) (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)); provide the sensor data to a perception module (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)); receive, from the perception module, a first perception output based on the sensor data (Crego: The localization component may output at least part of this data to the perception component 114, which may output at least some of the localization data and/or use the localization data as a reference for determining at least some of the perception data (Col. 7, Ln. 23-27)); provide the sensor data to a plurality of validation modules (Crego: see at least (Col. 27, Ln. 57 - Col. 28, Ln. 11) and (Col. 28, Ln. 66 - Col. 29, Ln. 15) for first and second perception error model), receive perception outputs from the plurality of validation modules (Crego: the prediction output and the perception output are generated by the perception component at least one of according to a second configuration or responsive to a second scenario (Col. 28, Ln. 62-65)); determine a second perception output (Crego: The error estimation component 512 may comprise a perception error model 520 and/or a prediction model 522. The perception error model 520 may output a mixture model and/or one or more probability distributions associated with the object detection (Col. 24, Ln. 14-19); The system of any one of paragraphs G-I, wherein the operations further comprise training a second perception error model based at least in part on a difference between a prediction output and a perception output of a perception component of the autonomous vehicle, wherein the prediction output and the perception output are generated by the perception component at least one of according to a second configuration or responsive to a second scenario (Col. 28, Ln. 57-65)) based on the perception outputs received from each of the plurality of validation modules (Crego: The non-transitory computer-readable medium of any one of paragraphs N-P, wherein the operations further comprise training a second perception error model based at least in part on a difference between a prediction output and a perception output of a perception component of the autonomous vehicle, wherein the prediction output and the perception output are generated by the perception component at least one of according to a second configuration or responsive to a second scenario (Col. 28, Ln. 57-65); (Col. 28, Ln. 66 - Col. 29, Ln. 15)), wherein the second perception output is a ground-truth perception output (Crego: training the perception error model 242 may comprise receiving ground truth data associated with prediction data 240 extracted from log data (e.g., the log data may be generated from simulated or real-world operation of the autonomous vehicle) and determining a difference between the prediction data 240 and the ground truth data. For example, the prediction data 240 may be a prediction associated with time n+1 and the ground truth data may include the perception system output at time n+1 and/or label data generated by manual, semi-automatic, or automatic ground truth labelling (Col. 13, Ln. 61 - Col. 14, Ln. 4)) and determine if the first perception output corresponds with the second perception output (Crego: the second error distribution indicates a reduction in the likelihood of error compared to the first error distribution (Col. 31, Ln. 3-5)).
Crego does not explicitly recite a first and second module. However, Crego does disclose training a second model and a second configuration without the need for additional hardware. Which, the examiner respectfully submits, is parallel in service and result as a process. In considering the disclosure of a reference, it is proper to take into account not only specific teachings of the reference but also the inferences which one skilled in the art would reasonably be expected to draw therefrom. For example, for one of ordinary skill, it would be obvious, and likely simple, that a single module performing two tasks could be split into two modules for performing the same two tasks.
However, should it be found that Crego fails to disclose the above limitations, in the same field of endeavor, Elli discloses, a first validation module among the plurality of validation modules has a first network architecture (Elli: [FIG. 4 (402a)(402b)(409a)(409b)]), a second validation module among the plurality of validation modules has a second network architecture (Elli: [FIG. 4 (402a)(402b)(409a)(409b)]), the first network architecture is distinct from the second network architecture (Elli: [FIG. 4 (402a)(402b)(409a)(409b)]), each of the plurality of perception modules generates a perception output (Elli: [FIG. 4 (402a)(402b)(409a)(409b)]), 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, receive perception outputs from the plurality of validation modules and a determining based on a consensus mechanism applied to the perception outputs from the plurality of validation modules.
However, in the same field of endeavor, Hyde discloses, receive perception outputs from the plurality of validation modules (Hyde: [0023] based on output validations of outputs from first functional circuitry and second functional circuitry; [0048], [0142-0146], [0194]); determined based on a consensus mechanism applied to the perception outputs from the plurality of validation modules (Hyde: [0106] the monitoring circuitry 210 of the autonomous vehicle computing system 200 can be used to determine a difference between outputs of the functional circuits (e.g., 206, 208, etc.) ... monitoring circuitry 210 can generate comparative data associated with one or more differences between the outputs of the functional circuits (e.g., 202, 204, etc.) ... first output data from functional circuitry 202 may indicate a first output describing a first trajectory of an object external to the autonomous vehicle while second output data from functional circuitry 204 may indicate a second trajectory of the object. If the first trajectory and the second trajectory are within a certain degree of similarity, the comparative data can indicate that the functionality of both outputs is assured. [0107] ... generating the comparative data can include detecting a fault within functional circuitry of the autonomous vehicle computing system 200 ... by generating the comparative data, the monitoring circuitry 210 can detect a fault within one or more of the associated functional circuits being compared. A fault can be detected based on a certain degree of difference between outputs and/or an inherent aspect of an output (e.g., an impossible prediction, incompatible output, etc.) ... a first output may include a detection of an object external to the autonomous vehicle while a second output may not include a detection of the object in question. By generating the comparative data, the monitoring circuitry 210 can detect a fault within the second functional circuit associated with the failure to recognize the object external to the autonomous vehicle ... a fault can be detected based on a difference between outputs that satisfies a difference threshold), 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 disclosed 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.
REGARDING CLAIM 2, Crego, as modified, remain as applied above to claim 1, and further, Crego also discloses, compare the first perception output with the second perception output (Crego: (Col. 31, Ln. 3-5)).
REGARDING CLAIM 3, Crego, as modified, remain as applied above to claim 1, and further, Crego also discloses, determine if the first perception output is within a predetermined threshold of the second perception output (Crego: (Col. 28, Ln. 2-11); (Col. 31, Ln. 3-5)).
REGARDING CLAIM 4, Crego, as modified, remain as applied above to claim 1, and further, Crego also discloses, in response to determining that the first perception output does not correspond with the second perception output, flag the first perception output for further review (Crego: (Col. 3, Ln. 25-31)).
REGARDING CLAIM 5, Crego, as modified, remain as applied above to claim 1, and further, Crego also discloses, a deep-learning neural network (Crego: include one or more machine-learned (ML) models (Col. 7, Ln. 37-38)).
REGARDING CLAIM 6, Crego, as modified, remain as applied above to claim 1, and further, Crego also discloses, camera data, Light Detection and Ranging (LiDAR) (Crego: a depth position sensor (e.g., a lidar sensor … ) (Col. 6, Ln. 59-60)).
REGARDING CLAIM 7, Crego, as modified, remain as applied above to claim 1, and further, Crego also discloses, the sensor data is received from one or more autonomous vehicle (AV) sensors (Crego: (Col. 2, Ln. 51-56)).
REGARDING CLAIM 15, Crego discloses, receive sensor data (Crego: (Col. 2, Ln. 51-56)), wherein the sensor data corresponds with an environment around an autonomous vehicle (AV) (Crego: (Col. 2, Ln. 51-56)); providing the sensor data to a perception module (Crego: (Col. 2, Ln. 51-56)); receiving a first perception output based on the sensor data (Crego: (Col. 7, Ln. 23-27)); providing the sensor data to a plurality of validation modules (Crego: see at least (Col. 27, Ln. 57 - Col. 28, Ln. 11) and (Col. 28, Ln. 66 - Col. 29, Ln. 15) for first and second perception error model), wherein a first validation module among the plurality of validation modules has a first network architecture (Crego: (Col. 27, Ln. 50-56)), wherein a second validation module among the plurality of validation modules has a second network architecture (Crego: (Col. 27, Ln. 50-56)), wherein the first network architecture is distinct from the second network architecture (Crego: (Col. 27, Ln. 50-56)), and wherein each of the plurality of perception modules generates a perception output (Crego: (Col. 27, Ln. 50-56)); receive, from the plurality of validation modules, a second perception output based on the sensor data (Crego: (Col. 28, Ln. 62-65)); and determine if the first perception output corresponds with the second perception output (Crego: (Col. 31, Ln. 3-5)).
Crego does not explicitly recite a first and second module. However, Crego does disclose training a second model and a second configuration without the need for additional hardware. Which, the examiner respectfully submits, is parallel in service and result as a process. In considering the disclosure of a reference, it is proper to take into account not only specific teachings of the reference but also the inferences which one skilled in the art would reasonably be expected to draw therefrom. For example, for one of ordinary skill, it would be obvious, and likely simple, that a single module performing two tasks could be split into two modules performing the same two tasks.
However, should it be found that Crego fails to disclose, wherein a first validation module among the plurality of validation modules has a first network architecture, wherein a second validation module among the plurality of validation modules has a second network architecture, wherein the first network architecture is distinct from the second network architecture, and wherein each of the plurality of perception modules generates a perception output, in the same field of endeavor, Elli discloses, wherein a first validation module among the plurality of validation modules has a first network architecture (Elli: [FIG. 4 (402a)(402b)]), wherein a second validation module among the plurality of validation modules has a second network architecture (Elli: [FIG. 4 (402a)(402b)]), wherein the first network architecture is distinct from the second network architecture (Elli: [FIG. 4 (402a)(402b)]), and wherein each of the plurality of perception modules generates a perception output (Elli: [FIG. 4 (402a)(402b)(409a)(409b)]), 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.
REGARDING CLAIM 16, Crego, as modified, remain as applied above to claim 15, and further, Crego also discloses, compare the first perception output with the second perception output (Crego: (Col. 31, Ln. 3-5)).
REGARDING CLAIM 17, Crego, as modified, remain as applied above to claim 15, and further, Crego also discloses, determine if the first perception output is within a predetermined threshold of the second perception output (Crego: (Col. 28, Ln. 2-11); (Col. 31, Ln. 3-5)).
REGARDING CLAIM 18, Crego, as modified, remain as applied above to claim 15, and further, Crego also discloses, flag the first perception output for further review, if the first perception output does not correspond with the second perception output (Crego: (Col. 3, Ln. 25-31)).
REGARDING CLAIM 19, Crego, as modified, remain as applied above to claim 15, and further, Crego also discloses, a deep-learning neural network (Crego: include one or more machine-learned (ML) models (Col. 7, Ln. 37-38)).
REGARDING CLAIM 21, Crego, as modified, remain as applied above to claim 1, and further, Elli 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 (Crego: [0077]; [0054-0057]).
Claim(s) 8-14 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 8, Crego discloses, receiving sensor data (Crego: (Col. 2, Ln. 51-56)), wherein the sensor data corresponds with an environment around an autonomous vehicle (AV) (Crego: (Col. 2, Ln. 51-56)); providing the sensor data to a perception module (Crego: (Col. 2, Ln. 51-56)); receiving a first perception output based on the sensor data (Crego: (Col. 7, Ln. 23-27)); providing the sensor data to a plurality of validation modules (Crego: see at least (Col. 27, Ln. 57 - Col. 28, Ln. 11) and (Col. 28, Ln. 66 - Col. 29, Ln. 15) for first and second perception error model), wherein a first validation module among the plurality of validation modules has a first network architecture (Crego: (Col. 27, Ln. 50-56)), wherein a second validation module among the plurality of validation modules has a second network architecture (Crego: (Col. 27, Ln. 50-56)), wherein the first network architecture is distinct from the second network architecture (Crego: (Col. 27, Ln. 50-56)), and wherein each of the plurality of perception modules generates a perception output (Crego: (Col. 27, Ln. 50-56)); receive, from the plurality of validation modules, a second perception output based on the sensor data (Crego: (Col. 28, Ln. 62-65)); receiving perception outputs from the plurality of validation modules (Crego: the prediction output and the perception output are generated by the perception component at least one of according to a second configuration or responsive to a second scenario (Col. 28, Ln. 62-65)); determining a second perception output (Crego: The error estimation component 512 may comprise a perception error model 520 and/or a prediction model 522. The perception error model 520 may output a mixture model and/or one or more probability distributions associated with the object detection (Col. 24, Ln. 14-19); The system of any one of paragraphs G-I, wherein the operations further comprise training a second perception error model based at least in part on a difference between a prediction output and a perception output of a perception component of the autonomous vehicle, wherein the prediction output and the perception output are generated by the perception component at least one of according to a second configuration or responsive to a second scenario (Col. 28, Ln. 57-65)) based on the perception outputs received from each of the plurality of validation modules (Crego: The non-transitory computer-readable medium of any one of paragraphs N-P, wherein the operations further comprise training a second perception error model based at least in part on a difference between a prediction output and a perception output of a perception component of the autonomous vehicle, wherein the prediction output and the perception output are generated by the perception component at least one of according to a second configuration or responsive to a second scenario (Col. 28, Ln. 57-65); (Col. 28, Ln. 66 - Col. 29, Ln. 15)), wherein the second perception output is a ground- truth perception output (Crego: training the perception error model 242 may comprise receiving ground truth data associated with prediction data 240 extracted from log data (e.g., the log data may be generated from simulated or real-world operation of the autonomous vehicle) and determining a difference between the prediction data 240 and the ground truth data. For example, the prediction data 240 may be a prediction associated with time n+1 and the ground truth data may include the perception system output at time n+1 and/or label data generated by manual, semi-automatic, or automatic ground truth labelling (Col. 13, Ln. 61 - Col. 14, Ln. 4)) and determine if the first perception output corresponds with the second perception output (Crego: (Col. 31, Ln. 3-5)).
Crego does not explicitly recite a first and second module. However, Crego does disclose training a second model and a second configuration without the need for additional hardware. Which, the examiner respectfully submits, is parallel in service and result as a process. In considering the disclosure of a reference, it is proper to take into account not only specific teachings of the reference but also the inferences which one skilled in the art would reasonably be expected to draw therefrom. For example, for one of ordinary skill, it would be obvious, and likely simple, that a single module performing two tasks could be split into two modules performing the same two tasks.
However, should it be found that Crego fails to disclose, wherein a first validation module among the plurality of validation modules has a first network architecture, wherein a second validation module among the plurality of validation modules has a second network architecture, wherein the first network architecture is distinct from the second network architecture, and wherein each of the plurality of perception modules generates a perception output, in the same field of endeavor, Elli discloses, wherein a first validation module among the plurality of validation modules has a first network architecture (Elli: [FIG. 4 (402a)(402b)]), wherein a second validation module among the plurality of validation modules has a second network architecture (Elli: [FIG. 4 (402a)(402b)]), wherein the first network architecture is distinct from the second network architecture (Elli: [FIG. 4 (402a)(402b)]), and wherein each of the plurality of perception modules generates a perception output (Elli: [FIG. 4 (402a)(402b)(409a)(409b)]), 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, determined based on a consensus mechanism applied to the perception outputs from the plurality of validation modules, wherein the consensus mechanism 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, determined based on a consensus mechanism applied to the perception outputs from the plurality of validation modules (Hyde: [0106] the monitoring circuitry 210 of the autonomous vehicle computing system 200 can be used to determine a difference between outputs of the functional circuits (e.g., 206, 208, etc.) ... monitoring circuitry 210 can generate comparative data associated with one or more differences between the outputs of the functional circuits (e.g., 202, 204, etc.) ... first output data from functional circuitry 202 may indicate a first output describing a first trajectory of an object external to the autonomous vehicle while second output data from functional circuitry 204 may indicate a second trajectory of the object. If the first trajectory and the second trajectory are within a certain degree of similarity, the comparative data can indicate that the functionality of both outputs is assured. [0107] ... generating the comparative data can include detecting a fault within functional circuitry of the autonomous vehicle computing system 200 ... by generating the comparative data, the monitoring circuitry 210 can detect a fault within one or more of the associated functional circuits being compared. A fault can be detected based on a certain degree of difference between outputs and/or an inherent aspect of an output (e.g., an impossible prediction, incompatible output, etc.) ... a first output may include a detection of an object external to the autonomous vehicle while a second output may not include a detection of the object in question. By generating the comparative data, the monitoring circuitry 210 can detect a fault within the second functional circuit associated with the failure to recognize the object external to the autonomous vehicle ... a fault can be detected based on a difference between outputs that satisfies a difference threshold), wherein the consensus mechanism 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 disclosed 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 field 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 apparatus disclosed by a modified Crego to include weighting dependent validation based upon history, length of backpropagation 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.
REGARDING CLAIM 9, Crego, as modified, remain as applied above to claim 8, and further, Crego also discloses, comparing the first perception output with the second perception output (Crego: (Col. 31, Ln. 3-5)).
REGARDING CLAIM 10, Crego, as modified, remain as applied above to claim 8, and further, Crego also discloses, determining if the first perception output is within a predetermined threshold of the second perception output (Crego: (Col. 31, Ln. 3-5)).
REGARDING CLAIM 11, Crego, as modified, remain as applied above to claim 8, and further, Crego also discloses, flagging the first perception output for further review, if the first perception output does not correspond with the second perception output (Crego: (Col. 3, Ln. 25-31)).
REGARDING CLAIM 12, Crego, as modified, remain as applied above to claim 8, and further, Crego also discloses, the validation module comprises a deep-learning neural network (Crego: include one or more machine-learned (ML) models (Col. 7, Ln. 37-38)).
REGARDING CLAIM 13, Crego, as modified, remain as applied above to claim 8, and further, Crego also discloses, the sensor data comprises camera data, Light Detection and Ranging (LiDAR) (Crego: a depth position sensor (e.g., a lidar sensor … ) (Col. 6, Ln. 59-60)).
REGARDING CLAIM 14, Crego, as modified, remain as applied above to claim 8, and further, Crego also discloses, the sensor data is received from one or more autonomous vehicle (AV) sensors (Crego: (Col. 2, Ln. 51-56)).
Response to Arguments
Applicant’s arguments with respect to the rejection of independent claim(s) 1, 8, and 15 under 35 USC §103, obviousness, have been considered but are moot because the new ground of rejection does not rely on the same combined prior art references applied in the prior 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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