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 .
Claim Rejections - 35 USC § 112
Claims 6-7 and 14-15 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 6 and 14, the phrase "such as" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Claims 7 and 15 depend upon claims 6 and 14 respectively.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 6-11 and 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Gardner (20230158409) in view of Zafeiropoulos (20240169112, referred to as “Zafe” herein) and in further view of Bonk (20190135283).
Regarding claim 1, Gardner teaches receiving sensor data for a race performed on a physical race track, wherein the sensor data captures a first plurality of parameters of a vehicle moving along a driving path (pars. 12-14, 22, and 24, telemetry captures vehicle parameters);
performing a racing simulation in which a virtual vehicle moves along a simulated driving path on a virtual race track, wherein the virtual race track corresponds to the physical race track and the simulated driving path corresponds to the driving path (pars. 12-13, 19-20, 60 and 91, matching virtual race track vehicle path);
Zafe teaches generating virtual sensor data capturing a second plurality of physical parameters of the virtual vehicle moving along the simulated driving path (pars. 20-22 and 39-49, simulated sensor);
detecting a difference between the first plurality of parameters and the second plurality of parameters at a first set of points in the driving path (pars. 23, 59-65 and 89-90, difference along path);
executing a path imperfections machine learning (ML) model that receives the difference as an input (pars. 25, 70-71 and 89-90, ML model receives component differences)
It would have been obvious prior to the effective filing date of the invention to one of ordinary skill in the art to include in Garder the ability to have simulation and real data analysis as taught by Zafe. The reason is to allow the system to identify errors based on the ML output of the data.
Zafe and Gardner do not teach outputting track imperfections.
Bonk teaches outputs track imperfections at the first set of points that cause the difference (see pras. 11-15, 26, 32-36 and 68-39, local track imperfections from sensor patterns).
It would have been obvious prior to the effective filing date of the invention to one of ordinary skill in the art to include in Zafe and Garnder to ability to output track imperfections in a map as taught by Bonk. The reason is to improve the accuracy of the simulation.
Regarding claim 2, Gardner teaches wherein the virtual race track is a model comprising a plurality of points each with track information, and the driving path comprises a subset of the points where the vehicle has driven (pars. 22-25, 33-35, 60 and 91 which teaches sampled positions to populate the track model)
Bonk teaches wherein the path imperfections ML model modifies track information for the first set of points in the subset of the points (pars. 15, 26, 32-38 and 68-69, update map based on imperfections)
Regarding claim 3, see Bonk, pars. 10, 12-15, 36-38 and 68-69 which teaches anomaly locations.
Regarding claim 6, see Zafe, pars. 30, 39, 42, 45 and 65 which teaches real and simulated lidar data.
Regarding claim 7, see Zafe pars. 32-33, 61 and 94, GPS, acceleration, speed and pressure sensors.
Regarding claim 8, see Bonk pars. 13, 26, 29 and 32-36 and 64-68, signatures map differences leads to anomalies.
Regarding claim 9, see the rejection of claim 1 above and pars. 113-116 of Zafe which teaches processor.
Regarding claim 10-11 and 14, see the rejection of claims 2-3 and 6.
Regarding claims 15-17, see the rejection of claims 8-9.
Allowable Subject Matter
Claims 4-5 and 12-13 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims 4 and 12 recite executing a generative ML model that receives, as an input, the track imperfections indicated in the subset of the points and the virtual race track, and outputs an updated virtual race track with possible imperfections across other points of the plurality of points that are not in the subset. This is not found in the prior art.
Claims 5 and 13 are dependent on 4 and 12 respectively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lee teaches verifying accuracy of a virtual sensor model for simulation based on reality information data. According to an embodiment, a virtual sensor verification method may acquire information on positions and states of real vehicles which are running on a real road, may acquire real sensor data generated in real sensors of a reality information acquisition vehicle from among the real vehicles, may reproduce the real vehicles on a virtual road as virtual vehicles, based on the acquired information on the positions and states, may acquire virtual sensor data outputted from virtual sensors mounted in a virtual information acquisition vehicle from among the virtual vehicles.
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/HADI AKHAVANNIK/Primary Examiner, Art Unit 2676
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