DETAILED ACTION
This is a final rejection in response to amendments filed 12/29/2025. Claims 1-9 and 11-19 are pending.
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 Arguments
Applicant’s amendments have overcome the previous 101 claim rejections. Applicant’s arguments with respect to the prior art of record not teaching the amended features have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 5-9, 11-13, and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Han (US 20170316127) in view of Muehlenstaedt (US 20230222268).
Regarding claim 1, Han teaches a system (system 400) comprising:
one or more processors (CPU 401); and
a memory storing instructions that, when executed by the one or more processors (memory 402/403), cause the system to perform:
obtaining or generating annotated data, wherein the annotated data comprises annotations and are associated with locomotion of a vehicle ([0021]-[0025] discuss acquiring image data regarding scenario objects including their attributes (annotations));
inferring mappings between the annotated data and concepts associated with the locomotion of the vehicle, wherein each of the mappings correlates a subset of the annotated data with a concept ([0033] discusses constructing a scenario (concept) based on the scenario objects where the concept is specifically the attributes of the scenario objects);
characterizing a scenario based on the inferred mappings ([0033] discusses constructing a scenario with the scenario objects and the host vehicle based on the attribute information);
implementing a testing simulation based on the characterized scenario and the predicted one or more subsequent scenarios, wherein the testing simulation comprises executing of a test driving operation involving a test vehicle ([0033]-[0037] discuss the system executing a testing simulation based on the traffic scenario);
receiving a query for a particular concept ([0042] and Fig. 2 show one of multiple simulations being simulated through a simulator where it is interpreted that a query for a particular scenario would be received to be introduced to the simulator); and
retrieving, based on the mappings, a particular subset of the annotated data correlated with the particular concept ([0042] and Fig. 2 show the simulator retrieving attributes based on the particular scenario).
Muehlenstaedt teaches predicting one or more subsequent scenarios based on the characterized scenarios ([0012] discusses predicting different outcomes of a base scenario and then assigning priority levels to the these predicted scenarios and [0013] discusses simulating the base and predicted scenarios).
Han teaches a driving simulation with a scenario based on vehicle attributes. Muehlenstaedt teaches predicting different scenarios based on a base scenario. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the simulation of Han with the predicted scenarios of Muehlenstaedt as Muehlenstaedt teaches that this allows the system to prioritize scenarios, preventing waste of computing resources [0003].
Regarding claim 2, Han teaches wherein the annotations are associated with at least one or more static entities, one or more dynamic entities, or one or more environmental conditions ([0028] discusses the object attributes including another vehicle and a pedestrian (dynamic entities)).
Regarding claim 3, Han teaches wherein the one or more dynamic entities comprise the vehicle or one or more other vehicles ([0028] discusses the object attributes including another vehicle).
Regarding claim 5, Han teaches wherein the inferring of the mappings is based on relative positions between the annotations in a media frame ([0028] discusses the object attributes including another vehicle and a pedestrian (dynamic entities) and their relative position to the own vehicle where it is interpreted that the traffic scenario would be mapped according to this relative position).
Regarding claim 6, Han teaches wherein the inferring of the mappings is based on relative orientations between the annotations in a media frame ([0028] discusses the object attributes including another vehicle and a pedestrian (dynamic entities) and their relative orientation to the own vehicle where it is interpreted that the traffic scenario would be mapped according to this relative orientation).
Regarding claim 7, Han teaches wherein the inferring of the mappings is based on a signal of the vehicle or of an other vehicle ([0025] discusses the object attributes including a traffic sign where [0021] discusses this including an indicator).
Regarding claim 9, Han teaches wherein the annotated data comprises media files ([0027] discusses the object data comprising images).
Regarding claim 11, Han teaches a method comprising:
obtaining or generating annotated data, wherein the annotated data comprises annotations and are associated with locomotion of a vehicle ([0021]-[0025] discuss acquiring image data regarding scenario objects including their attributes (annotations));
inferring mappings between the annotated data and concepts associated with the locomotion of the vehicle, wherein each of the mappings correlates a subset of the annotated data with a concept ([0033] discusses constructing a scenario (concept) based on the scenario objects where the concept is specifically the attributes of the scenario objects);
characterizing a scenario based on the inferred mappings ([0033] discusses constructing a scenario with the scenario objects and the host vehicle based on the attribute information);
implementing a testing simulation based on the characterized scenario and the predicted one or more subsequent scenarios, wherein the testing simulation comprises executing of a test driving operation involving a test vehicle ([0033]-[0037] discuss the system executing a testing simulation based on the traffic scenario);
receiving a query for a particular concept ([0042] and Fig. 2 show one of multiple simulations being simulated through a simulator where it is interpreted that a query for a particular scenario would be received to be introduced to the simulator); and
retrieving, based on the mappings, a particular subset of the annotated data correlated with the particular concept ([0042] and Fig. 2 show the simulator retrieving attributes based on the particular scenario).
Muehlenstaedt teaches predicting one or more subsequent scenarios based on the characterized scenarios ([0012] discusses predicting different outcomes of a base scenario and then assigning priority levels to the these predicted scenarios and [0013] discusses simulating the base and predicted scenarios).
Han teaches a driving simulation with a scenario based on vehicle attributes. Muehlenstaedt teaches predicting different scenarios based on a base scenario. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the simulation of Han with the predicted scenarios of Muehlenstaedt as Muehlenstaedt teaches that this allows the system to prioritize scenarios, preventing waste of computing resources [0003].
Regarding claim 12, Han teaches wherein the annotations are associated with at least one or more static entities, one or more dynamic entities, or one or more environmental conditions ([0028] discusses the object attributes including another vehicle and a pedestrian (dynamic entities)).
Regarding claim 13, Han teaches wherein the one or more dynamic entities comprise the vehicle or one or more other vehicles ([0028] discusses the object attributes including another vehicle).
Regarding claim 15, Han teaches wherein the inferring of the mappings is based on relative positions between the annotations in a media frame ([0028] discusses the object attributes including another vehicle and a pedestrian (dynamic entities) and their relative position to the own vehicle where it is interpreted that the traffic scenario would be mapped according to this relative position).
Regarding claim 16, Han teaches wherein the inferring of the mappings is based on relative orientations between the annotations in a media frame ([0028] discusses the object attributes including another vehicle and a pedestrian (dynamic entities) and their relative orientation to the own vehicle where it is interpreted that the traffic scenario would be mapped according to this relative orientation).
Regarding claim 17, Han teaches wherein the inferring of the mappings is based on a signal of the vehicle or of an other vehicle ([0025] discusses the object attributes including a traffic sign where [0021] discusses this including an indicator).
Regarding claim 19, Han teaches wherein the annotated data comprises media files ([0027] discusses the object data comprising images).
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Han in view of Muehlenstaedt and further in view of Ashayer (US 20220283055).
Regarding claim 4, Han teaches wherein the particular subset of the annotated data comprises a first frame and a second frame, ([0027] discusses determining positions of the object attributes within each frame of the traffic image where it is interpreted that this would include a first and second frame).
Ashayer teaches wherein the particular subset of the annotated data comprises a first frame and a second frame, the second frame comprising a static entity or a dynamic entity that is absent from the first frame, wherein the first frame and the second frame comprise media frames ([0023] discusses the system simulating different objects including static objects as the vehicle moves through the simulated environment where it is interpreted that a static object would be present in a first frame and absent in a second frame when the vehicle has passed the static object in the simulated environment).
Han teaches simulating a driving environment. Ashayer teaches the objects changing as the simulation environment changes. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the system of Han with the system of Ashayer as this would allow the system to only focus on objects that affect the vehicle, improving the processing speed.
Regarding claim 14, Han teaches wherein the particular subset of the annotated data comprises a first frame and a second frame,([0027] discusses determining positions of the object attributes within each frame of the traffic image where it is interpreted that this would include a first and second frame).
Ashayer teaches wherein the particular subset of the annotated data comprises a first frame and a second frame, the second frame comprising a static entity or a dynamic entity that is absent from the first frame, wherein the first frame and the second frame comprise media frames ([0023] discusses the system simulating different objects including static objects as the vehicle moves through the simulated environment where it is interpreted that a static object would be present in a first frame and absent in a second frame when the vehicle has passed the static object in the simulated environment).
Han teaches simulating a driving environment. Ashayer teaches the objects changing as the simulation environment changes. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the system of Han with the system of Ashayer as this would allow the system to only focus on objects that affect the vehicle, improving the processing speed.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Han in view of Muehlenstaedt and further in view of Traut (US 20210357692).
Regarding claim 8, Han teaches wherein the inferring of the mappings is performed by a machine learning component ([0028] discusses the object attributes being performed by a deep learning model) but does not explicitly teach the machine learning component being trained over two stages, wherein a first stage is based on a first training dataset that correlates hypothetical annotated data to hypothetical concepts and a second stage is based on a second training dataset that comprises corrected hypothetical annotated data correlated to corrected hypothetical concepts.
Traut teaches the machine learning component being trained over two stages, wherein a first stage is based on a first training dataset that correlates hypothetical annotated data to hypothetical concepts and a second stage is based on a second training dataset that comprises corrected hypothetical annotated data correlated to corrected hypothetical concepts ([0003] discusses a machine learning system trained over multiple stages using hypothetical datasets with different levels of fidelity).
Han teaches performing simulation mapping with machine learning. Traut teaches the machine learning using multiple hypothetical datasets. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the machine learning of Han with the datasets of Traut as Traut teaches that this will improve the computational bandwidth while maintaining fidelity.
Regarding claim 18, Han teaches wherein the inferring of the mappings is performed by a machine learning component ([0028] discusses the object attributes being performed by a deep learning model) but does not explicitly teach the machine learning component being trained over two stages, wherein a first stage is based on a first training dataset that correlates hypothetical annotated data to hypothetical concepts and a second stage is based on a second training dataset that comprises corrected hypothetical annotated data correlated to corrected hypothetical concepts.
Traut teaches the machine learning component being trained over two stages, wherein a first stage is based on a first training dataset that correlates hypothetical annotated data to hypothetical concepts and a second stage is based on a second training dataset that comprises corrected hypothetical annotated data correlated to corrected hypothetical concepts ([0003] discusses a machine learning system trained over multiple stages using hypothetical datasets with different levels of fidelity).
Han teaches performing simulation mapping with machine learning. Traut teaches the machine learning using multiple hypothetical datasets. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the machine learning of Han with the datasets of Traut as Traut teaches that this will improve the computational bandwidth while maintaining fidelity.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIELLE M JACKSON whose telephone number is (303)297-4364. The examiner can normally be reached Monday-Friday 7:00-4:30 MT.
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/D.M.J./ Examiner, Art Unit 3657 /ABBY LIN/Supervisory Patent Examiner, Art Unit 3657