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 and Arguments
The amendment filed 6/1/2026 has been entered. Claims 1-4, 6-12remain pending in the application. Applicant’s amendments to the claims have overcome the §112(b) and 101 rejections previously set forth in the Non-Final Office Action.
Applicant’s arguments with respect to the rejection(s) under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of WO2022247994A1 (“Materne”).
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1, 2, 8, 10-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over US20220163966A1 Fonseca et al ("Fonseca") in view of WO2022247994A1 (“Materne”).
As per claims 1, 10-12 Fonseca teaches the limitations of the method and products:
A method for planning a trajectory for an at least partially automated vehicle, comprising the following steps: a) providing a machine learning model, trained in advance, for determining occupancy of occupancy grids, wherein the machine learning model is trained to predict the occupancy of an occupancy grid of the occupancy grids at a subsequent time from predict a total occupancy of the occupancy grid at a current time; d) comparing the predicted occupancy of the occupancy grid at the current time to the occupancy determined using the measurement data, and detecting deviations between occupancy determined using the measurement data and the predicted occupancy, and determining a location-dependent measure of a reliability of occupancy information depending on the comparison; e) planning a trajectory for the at least partially automated vehicle based on the reliability including based on the detected deviations and/or a degree of uncertainty. (Fonseca at least the abstract, [0010]: “trajectory or route”, [0076]: “a machine learned model (e.g., neural network) which has been trained”, [0018]: “region indicating a potential region occupied by an object over a period of time (such as 3 seconds, 5 seconds, 10 seconds, and the like)”, FIG. 5, FIG. 6, [0077]: “alternatively, the sensor system(s) 706 can send sensor data, via the one or more networks 734, to the one or more computing device(s) at a particular frequency, after a lapse of a predetermined period of time, in near real-time”, [0136]: “Another approach is to preprocess the observed OGMs, using motion information, outside of the OGM prediction system … Preprocessing to account for ego motion may be more effective. Also, the preprocessing approach (rather than determining the state transformation inside the RNN) means that the predictions are generated based on the current ego vehicle state. Such prediction may provide more flexibility for a planning algorithm to plan a proper trajectory for the vehicle.”).
Fonseca does not explicitly disclose a history of a specific length of occupancies, but does teach a BRI equivalent ([0018], [0077]). One of ordinary skill in the art could, based on the teachings of Fonseca, interpret an embodiment of the invention to act on such aforementioned limitations.
Fonseca does not disclose:
wherein the occupancy grids are transformed prior to processing by the machine learning model and/or prior to the comparison such that an ego movement of the vehicle is compensated.
Materne teaches the aforementioned limitation(s) (Materne at least: “the merging of the projected raw data and the projected preprocessed object data includes combining the current merged grid cell dimension with the merged grid cell dimension of the previous time step and compensating for the vehicle's own movements and/or the projected preprocessed object data and/or grid cells with dynamic information. Each time step can be viewed as its own occupancy grid. Since the procedure is carried out regularly or quasi-permanently, there is always an occupancy grid from the previous time step, except for the start of the system. In order not to have to laboriously redefine the information contained there for each time step, the current time step is merged with the previous one. This requires an assignment of the grid cells to the grid cell data from the previous step. This is achieved by calculating out, i.e. compensating for, the movements of the vehicle and the movements of the objects in the environment, represented by the projected data.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Fonseca with the aforementioned limitations taught by Materne with a reasonable expectation of success. One of ordinary skill would have been motivated to combine these references in order to increase the safety of the vehicle systems.
As per claim 2, Fonseca teaches the invention as described above. Fonseca additionally teaches:
wherein a measure of the reliability is determined for a specific spatial area of a current occupancy depending on the deviation between the occupancy determined using the measurement data and the predicted occupancy, depending on the deviation and/or a measurement uncertainty, wherein the specific spatial area includes at least one cell and/or multiple contiguous cells of the occupancy grid. (Fonseca at least [0091]: “receiving sensor data representative of a physical environment from the sensor; generating, based at least in part on the sensor data, a dilated prediction probability associated with a location of an object and an additional area exceeding the location of the object; comparing a point representing the autonomous vehicle with the dilated prediction probability; and causing, based at least in part on the comparing the point representing the vehicle with the dilated prediction probability, the autonomous vehicle to perform an action.”)
As per claim 8, Fonseca teaches the invention as described above. Fonseca additionally teaches:
wherein the machine learning model has an architecture in which spatial and temporal dimensions of input data are processed separately. (Fonseca at least [0136])
Claim(s) 3-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fonseca and Materne in view of EP3534297A2 Hansen et al ("Hansen", machine translation provided).
As per claim 3, Fonseca teaches the invention as described above. Fonseca does not disclose:
in step d), a subjective logic opinion is determined for determining the reliability for each cell of the occupancy grid, wherein a tuple bij, dij, uij, aij is determined for each cell ij, wherein bij represents a match between the measurement and the prediction of the occupancy of the cell ij, dij represents a deviation between the measurement and the prediction of the occupancy of the cell ij, uij represents an uncertainty of the occupancy of the cell ij, and aij describes a basic probability of the occupancy of the cell ij.
Hansen teaches the aforementioned limitation (Hansen at least: “Preferably, the represented environment is logically subdivided into a plurality of (in particular equal) tiles, wherein the map data and the reliability data are tile-resolved or resolvable or interpolatable, where a tile may be associated or assignable to a data tuple that has at least one presence probability for that tile has a reliability value and optionally also an immutability tag.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Fonseca with the aforementioned limitations taught by Hansen with a reasonable expectation of success. One of ordinary skill would have been motivated to combine these references in order to provide an improved position determination.
As per claim 4, Fonseca in combination with the other reference teaches the invention as described above. Fonseca additionally teaches:
in step e), a trajectory planning for the vehicle takes place such that cells and/or areas of cells of the occupancy grid: (a) with a high deviation dij and/or a high uncertainty uij are avoided and/or (b) areas with a high match bij are preferred. (Fonseca at least [0022]: “reference trajectory at the planned velocity (or series of velocities). The vehicle may then, determine a boundary of the drivable area based on the uncertainty model (e.g., the heat map) and the planned trajectory. For example, the system may perform a level set or determine a boundary of the drivable area based on a cell within the heatmap adjacent to the planned trajectory having a likelihood or probability of occupancy meets or exceeds a threshold”)
Claim(s) 6-7, 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fonseca and Materne in view of US20210276598A1 Abolfathi ("Abolfathi").
As per claim 6, Fonseca teaches the invention as described above. Fonseca does not disclose:
input data for the machine learning model includes 3D tensors, wherein the 3D tensors each include occupancy grids of a specific environment at different times.
Abolfathi teaches the aforementioned limitation (Abolfathi at least [0111]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Fonseca with the aforementioned limitations taught by Abolfathi with a reasonable expectation of success. One of ordinary skill would have been motivated to combine these references in order to improve the accuracy of predicted OGMs (Abolfathi [0166]).
As per claim 7, Fonseca teaches the invention as described above. Fonseca does not disclose:
the machine learning model includes an encoder-decoder architecture, wherein output data for the machine learning model includes an occupancy grid of the same dimensions as the occupancy grids of the input data, but only for a specific time.
Abolfathi teaches the aforementioned limitation (Abolfathi at least [0009], [0106]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Fonseca with the aforementioned limitations taught by Abolfathi with a reasonable expectation of success. The motivation to combine these references is the same as above in claim 6.
As per claim 9, Fonseca teaches the invention as described above. Fonseca does not disclose:
the machine learning model includes a recurrent network.
Abolfathi teaches the aforementioned limitation (Abolfathi at least [0009]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Fonseca with the aforementioned limitations taught by Abolfathi with a reasonable expectation of success. The motivation to combine these references is the same as above in claim 6.
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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/O.T./Examiner, Art Unit 3669
/NAVID Z. MEHDIZADEH/Supervisory Patent Examiner, Art Unit 3669
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