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 .
Status of the Application
Claims 1-20 have been examined in this application filed on or after March 16, 2013, and are being examined under the first inventor to file provisions of the AIA . 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 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. This communication is the First Office Action on the Merits.
Key to Interpreting this Office Action
For readability, all claim language has been bolded. Citations from prior art are provided at the end of each limitation in parenthesis. Any further explanations that were deemed necessary the by Examiner are provided at the end of each claim limitation. The Applicant is encouraged to contact the Examiner directly if there are any questions or concerns regarding the current Office Action.
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) 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.
Claims 1-5, 10-15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Stuebler et al. (US 20230304827 A1) herein Stuebler, in view of Zhang et al. (US 20220185316 A1) herein Zhang.
In regards to Claim 1, Stuebler discloses the following:
1. A vehicle control device (see at least [0009] “semi-autonomous robot can be an at least partially automated driving vehicle” and [0064] “controlling an at least semi-autonomous robot”) comprising:
a processor; (see at least FIG. 4 “map validation system 10” and claim 13 “computer processing system”
memory; (see at least [0072] “computer program” and [0073] “machine-readable storage medium”)
and a sensor, (see Fig. 4 and [0101] “sensor system 20”, see also [0007] “sensor data comprises camera data, lidar data, radar data, and/or GPS data”)
wherein the processor is configured to:
determine, based on a comparison between line information included in a road map and line sensing data obtained via the sensor, (see at least [0006] “Receiving sensor data of an at least semi-autonomous robot depicting at least one detected element”, “Receiving map data depicting a map having at least one map element”, [0007] “The term “map element” as used herein particularly includes… road markings”, [0043] “point-like objects—or objects that are sparsely extended relative to sensor space—the approach described can be applied directly, e.g., …dashed lane markings… Solid lane markings, e.g., boundaries” and [0019] “detect a possible discrepancy between the map data and the sensor data”)
a plurality of candidate datasets for identifying a change in a line associated with a road, wherein the plurality of candidate datasets correspond to a time period associated with the line sensing data; (see at least [0032] “The associations of measurements to depict map objects require the localization as input. The association relies on the predicted localization estimate from the previous time step, and the provided association is used to update the upcoming step of the localization module.”, [0039] “it is possible to update a prior map element existence probability… The existence probability specifies whether a map element still exists or not based on the collected, but uncertain, sensor data… Updating the existence probability, and preferably the position, of a map element occurs at each time step in which a set of sensor measurements is available.”, [0047] “updating the existence probability of the at least one map element is repeated at a temporal interval” and [0048] “temporal interval is dependent on a detection rate of at least one environmental sensor” and “For example… a camera that provides 25 frames per second [provides a] prior existence probability of the at least one map element is updated 25 times per second.”)
perform a positioning bias reduction process on each of the plurality of candidate datasets; (see at least [0006] “Determining a data uncertainty, wherein the data uncertainty comprises a sensor data uncertainty, a map data uncertainty”, [0020] “A prerequisite for the proper functioning of the map validation method is not only a precise localization, but also an accurate sensor calibration. The reason for this is that a detected discrepancy between map data and sensor data can be caused not only by an incorrect or outdated map, but also by poor localization and an incorrectly calibrated or faulty sensor.”, [0031] “a predetermined number of best global assignments of sensor data, i.e., sensor measurements, to depict elements in sensor space are computed using Murty's ranked assignment algorithm and, e.g., the Hungarian method as the underlying basis. Ranked-assignments can be based on, e.g., the multi-object measurement model from RFS-theory, which captures not only the spatial uncertainty of sensor measurements, but also the perturbation rate, perturbation intensity, and detection probability.”, [0052] “visibility takes into account the uncertainties of sensor measurements and map elements, as well as the uncertainties of localization and calibration information via error propagation.” and [0059]-[0061] “Verifying a validity of an existence probability”)
identify, among the plurality of candidate datasets:
a first dataset from which a positioning bias has been reduced, and
a second dataset different from the first dataset; (see at least [0033] “evaluating includes one of confirming the map element, disproving the map element, potentially new map element, and no possible statement.” and [0065] “behavior-planning module will know which map elements are confirmed with sensor data, which map elements are unknown, e.g., due to occlusion, and which map elements no longer exist.”)
Stuebler suggests the following:
determine, based on the second dataset, line change information; (see at least [0033] “disproving the map element”, [0074] “the existence of the at least one map element is… disproved”)
Stuebler confirms or disproves the existence of map element “line information” based on the comparison and analysis, cited above. While this implies (suggests) that the stored map element has changed, however Stuebler does not explicitly disclose line change information. However, this is more explicitly taught by Zhang. (see at least [0024] “missing or worn lane-markings”, [0029] “change detection that indicates features of the map 114 are sufficiently different from attributes of the sensor data 112, at corresponding locations in the environment 100, then the commonsense engine 124 causes an update to the map 114” and [0065] “differences between features of the registered object and features of a sensor-based reference map are determined. The features of the sensor-based reference map include a map location that corresponds to a coordinate location of the registered object. For example, portions of the sensor data 112 and portions of the map 114 can overlap the same coordinate locations; differences between features at the same coordinate locations indicate possible change detections that justify updating the map 114.”)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the features of Zhang with the invention of Stuebler, with a reasonable expectation of success, with the motivation of providing safe driving decisions being made by systems that operate vehicles or vehicle fleets that rely on reference maps for autonomous or semi-autonomous driving. (Zhang, [0002])
Stuebler discloses the following:
and control, based on the line change information, a vehicle. (see at least [0064] “Controlling the at least semi-autonomous robot based on the determined robot trajectory.” and [0086] “control mode of the vehicle F changes from “normal driving” to “preventive safety”)
In regards to Claim 2, Stuebler discloses the following:
2. The vehicle control device of claim 1, wherein the processor is configured to determine the plurality of candidate datasets by:
identifying a first line included in the line information; (see at least [0006] “Receiving map data depicting a map having at least one map element”, [0007] “The term “map element” as used herein particularly includes… road markings” and [0043] “point-like objects—or objects that are sparsely extended relative to sensor space—the approach described can be applied directly, e.g., …dashed lane markings… Solid lane markings, e.g., boundaries”)
identifying, based on the line sensing data, a second line corresponding to the first line; (see at least [0006] “Receiving sensor data of an at least semi-autonomous robot depicting at least one detected element”)
determining a statistical quantity representing a deviation of the second line with respect to the first line; (see at least [0014]-[0016] “probabilistic representation”)
and determining, based on the statistical quantity being greater than a threshold value, a candidate dataset, of the plurality of candidate datasets, that corresponds to the second line. (see at least [0058] “existence probability of the at least one map element with a detection probability below a predetermined threshold is not updated.”)
See also Zhang. [0022] “Positions represented by the sensor data 112 and the map 114 may be so accurate that comparing and matching road geometries (e.g., roundabout type, lane width, quantity of lanes) or changes to road-infrastructure (e.g., removal or addition of traffic cones, removal or addition of signs, removal or addition of traffic barriers) can be based on their overlap.”, [0030] “differences between the sensor data 112 and the map 114 are quantified.” And [0067] “responsive to determining that the differences satisfy the change detection criteria, cause the sensor-based reference map to be updated to reduce the differences.”) See claim 1 for motivation to combine the teachings of Zhang with Stuebler.
In regards to Claim 3, Stuebler discloses the following:
3. The vehicle control device of claim 2, wherein the candidate dataset corresponding to the second line comprises at least one of: a sensor bias indicating a misreading of the sensor, the positioning bias, or the line change information representing a change associated with the second line. (see at least [0017] “A perturbation, also called a clutter, comprises a sensor measurement, i.e. sensor data that is not determined by a real object, i.e. map element, for example a ghost measurement.”, [0022] “Errors from sensor measurements”, [0031] “captures not only the spatial uncertainty of sensor measurements, but also the perturbation rate, perturbation intensity, and detection probability.” And [0052] “visibility takes into account the uncertainties of sensor measurements and map elements, as well as the uncertainties of localization and calibration information via error propagation”)
In regards to Claim 4, Stuebler discloses the following:
4. The vehicle control device of claim 1, wherein the processor is further configured to:
identify a first line included in the line information; (see at least [0006] “Receiving map data depicting a map having at least one map element”, [0007] “The term “map element” as used herein particularly includes… road markings” and [0043] “point-like objects—or objects that are sparsely extended relative to sensor space—the approach described can be applied directly, e.g., …dashed lane markings… Solid lane markings, e.g., boundaries”)
identify, based on the line sensing data, a second line corresponding to the first line; (see at least [0006] “Receiving sensor data of an at least semi-autonomous robot depicting at least one detected element”)
and determine, based on a comparison of a location of the first line with a location of the second line, a statistical quantity representing a deviation of the second line with respect to the first line. (see at least [0058] “existence probability of the at least one map element with a detection probability below a predetermined threshold is not updated.”)
See also Zhang. [0022] “Positions represented by the sensor data 112 and the map 114 may be so accurate that comparing and matching road geometries (e.g., roundabout type, lane width, quantity of lanes) or changes to road-infrastructure (e.g., removal or addition of traffic cones, removal or addition of signs, removal or addition of traffic barriers) can be based on their overlap.”, [0030] “differences between the sensor data 112 and the map 114 are quantified.” And [0067] “responsive to determining that the differences satisfy the change detection criteria, cause the sensor-based reference map to be updated to reduce the differences.”) See claim 1 for motivation to combine the teachings of Zhang with Stuebler.
In regards to Claim 5, Stuebler discloses the following:
5. The vehicle control device of claim 1, wherein the processor is further configured to:
remove, through filtering, a sensor bias representing misreading of the sensor. (see at least [0017] “A perturbation, also called a clutter, comprises a sensor measurement, i.e. sensor data that is not determined by a real object, i.e. map element, for example a ghost measurement.”, [0022] “Errors from sensor measurements”, [0031] “captures not only the spatial uncertainty of sensor measurements, but also the perturbation rate, perturbation intensity, and detection probability.” And [0052] “visibility takes into account the uncertainties of sensor measurements and map elements, as well as the uncertainties of localization and calibration information via error propagation”)
In regards to Claim 10, Stuebler discloses the following:
10. The vehicle control device of claim 1, wherein the line sensing data comprises at least one of: a line, a road boundary shape, a road sign, or a road marking. (see at least [0008] “The term “map element” as used herein particularly includes traffic signs, such as traffic signs or traffic lights, as well as road markings.”)
In regards to Claims 11-15 and 20: Claims 11-15 and 20 are the methods performed by the vehicle control devices of claims 1-5 and 10, respectively, and are therefore rejected the same or similar to claims 1-5 and 10, above.
Allowable Subject Matter
Claims 6 and 16 are 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. The prior art of record does not appear to teach or suggest each and every limitation of claims 6 and 16.
Claims 7-9 and 17-19 depend from claims 6 and 16, and are therefore also 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jason Roberson, whose telephone number is (571) 272-7793. The examiner can normally be reached from Monday thru Friday between 8:00 AM and 4:30 PM. The examiner may also be reached through e-mail at Jason.Roberson@USPTO.GOV, or via FAX at (571) 273-7793. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Navid Z Mehdizadeh can be reached on (571)-272-7691.
Another resource that is available to applicants is the Patient Application Information Retrieval (PAIR) system. Information regarding the status of an application can be obtained from the PAIR system. Status information for published applications may be obtained from either Private PAIR or Public PAX. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have any questions on access to the Private PAIR system, please feel free to contact the Electronic Business Center (EBC) at 866-217-9197 (toll free).
Applicants are invited to contact the Office to schedule either an in-person or a telephone interview to discuss and resolve the issues set forth in this Office Action. Although an interview is not required, the Office believes that an interview can be of use to resolve any issues related to a patent application in an efficient and prompt manner.
Sincerely,
/JASON R ROBERSON/
Patent Examiner, Art Unit 3669
April 17, 2026
/NAVID Z. MEHDIZADEH/Supervisory Patent Examiner, Art Unit 3669
Prosecution Insights