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 .
Claim Objections
Claim 11 is objected to because of the following informalities: for consistency with the other claims, “the different calibration parameters” should be replaced with “the set of different calibration parameters”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3 and 4 are 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.
Claim 3 refers to “the calibration parameter used for determining the occupancy map associated with the extremal value”. Neither “the calibration parameter” nor “the occupancy map” have antecedent basis. For examination purposes, it will be assumed that “the calibration parameter” and “the occupancy map” should be replaced with “a calibration parameter” and “an occupancy map”.
Claim 4 recites “…wherein the sharpness value is determined…” Since claim 2 recites that “a sharpness value is determined for each of the determined occupancy maps”, it is unclear which sharpness value is being referred to. The examiner assumes that Applicant intends to recite that all sharpness values are determined according to the metric given, therefore for examination purposes the above will be replaced with “…wherein the sharpness values are determined…”.
Claim 4 also recites that “
S
(
f
)
is the sharpness value of the occupancy map determined on the basis of the calibration factor
f
”. It is unclear which occupancy map “the occupancy map” refers to (multiple have been recited), and there is no antecedent basis for “the calibration factor”. For examination purposes, it will be assumed that the above should be replaced with: “
S
(
f
)
is the sharpness value of an occupancy map determined on the basis of a calibration factor
f
”.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
At Step 1 of the 101 analysis, all of the claims are directed to one of the statutory categories of invention.
Claim 1 is rejected in response to the following analysis:
At Step 2A Prong One, the judicial exceptions are bolded in the copy of claim 1 below:
A computer implemented method for calibrating a motion sensor arranged at a vehicle and configured to output a raw motion measurement value indicative of a current motion condition of the vehicle, the method comprising:
computing processed motion measurement values by modifying the outputted raw motion measurement value based on a set of different calibration parameters,
monitoring the vicinity of the vehicle with at least one environment perception sensor,
determining a set of occupancy maps based on mapping data acquired by the environment perception sensor in accordance with a motion model for the vehicle, wherein each of the processed motion measurement values is used to establish the motion model for one of the occupancy maps, and
selecting, based on a comparison of the determined occupancy maps, one of the different calibration parameters for modifying subsequently outputted raw motion measurement values.
Computing processed measurement values by modifying raw values based on calibration parameters encompasses a mental or mathematical operation depending on complexity. Determining occupancy maps in accordance with a motion model for the vehicle, the processed values being used to establish the motion model for an occupancy map, encompasses mathematical processes. Comparing occupancy maps encompasses a mental or mathematical process depending on complexity. Finally, selecting a calibration parameter to modify subsequent raw values also encompasses a mental or mathematical process.
At Step 2A Prong Two, the additional elements include a motion sensor at a vehicle which outputs raw values indicating a current motion of the vehicle, monitoring the vicinity of the vehicle with an environment perception sensor, and acquiring mapping data by the environment perception sensor. Thus use of the claim requires accessing data outputted by a motion sensor at a vehicle which outputs data indicating the vehicle’s current motion condition, and further requires monitoring the vehicle’s vicinity and acquiring mapping data with the environment perception sensor. This establishes the field as that of vehicles which have sensors to detect motion and map the vehicle’s vicinity. However, the data acquisition is primarily in the service of the mathematical/mental processes applied to them. The calibration parameters are not detailed, nor is the processing performed on the raw motion values, nor is the motion model. Finally, the result of the claim is that one selects a (generic) calibration parameter to modify subsequent raw motion values. The claim does not explicitly recite applying the calibration parameter to actively perform the modification, although such a step is implied. More importantly, the result of the modification is general. The result of claim 1 is that the way the raw motion values are modified is changed; however, what exactly the raw motion values represent (a “current motion condition” could be position, velocity, acceleration, sliding or skidding, shaking, stalling or at a standstill, braking, etc.) and what the modifications entail are not further defined. Furthermore, the result is not a particular real-world transformation, and it is unclear if the result is meant to provide some particular real-world improvement to, for example, the vehicle. For these reasons, the additional elements do not integrate the judicial exceptions into a practical application.
At Step 2B, when considered as a whole, claim 1 does not amount to significantly more than the judicial exceptions for the reasons given above.
Claims 2-4 recite further judicial exceptions describing how a sharpness value is used to determine which calibration parameter to select. However, it is still unclear what the calibration parameters represent, or what the raw data to be processed represents, therefore the issues raised in the analysis of claim 1 remain unchanged. Therefore, these claims are also rejected.
Claim 5 recites that the vicinity of the vehicle is monitored while the vehicle moves. This does not change the analysis given in the rejection of claim 1, therefore claim 5 is also rejected.
Claim 6 states that the motion sensor is an odometry sensor. This clarifies that the motion values describe changes in distance travelled, however the way the raw data is modified is still not described in any detail, therefore for at least this reason claim 6 is also rejected.
Claim 7 recites that the raw motion values represent vehicle velocity, however claim 7 is rejected for the same reasons as claim 6.
Claim 8 recites that the environment perception sensor is radar, lidar, or a camera. This somewhat limits the field of use by excluding other environment perception sensors, but the issues outlined in the rejection of claim 1 remain, therefore claim 8 is also rejected.
Claims 9-10 describe that the processing involves scaling or correcting for bias, however what the data values represent is not clarified and the other issues outlined in the rejection of claim 1 remain, therefore these claims are rejected.
Claim 11 describes that the set of calibration parameters occupy a range around an expected value, but this does not address the issues outlined in the rejection of claim 1, therefore claim 11 is also rejected.
Claim 12 recites that the data acquired by the environment perception sensor are collected for a predetermined number of time intervals, and that the occupancy maps are determined by overlaying the collected data in a spatial representation of the vehicle’s vicinity. These limitations do not address the issues outlined in the rejection of claim 1, therefore claim 12 is also rejected.
Claims 13-15 recite a computer system, non-transitory computer readable medium, and vehicle comprising a computer system, all of which implement the method of claim 1, and are therefore rejected for the same reasons as claim 1.
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.
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.
Claims 1-3 and 5-15 are rejected under 35 U.S.C. 103 as being unpatentable over Sun (CN 114625121A) in view of Fan (“An incremental LiDAR/POS online calibration method”).
Regarding claim 1, Sun discloses a computer implemented method (¶n17: “The autonomous inspection and exploration vehicle system based on multi-sensor fusion provided by the present invention includes a terminal display device, a processor, a gas source sensing module, and a chassis drive module; the processor is equipped with a SLAM module, an autonomous exploration and navigation module, and a terminal display interface control module”) for calibrating (¶n118: "the camera and LiDAR can be matched to calibrate the error and drift of the attitude sensor") a motion sensor (¶n28: attitude sensor) arranged at a vehicle (the system is for a vehicle navigating its environment; see above and ¶n1) and configured to output a raw motion measurement value indicative of a current motion condition of the vehicle (¶n41: "the attitude sensor employs a self-calibrated nine-axis data fusion IMU inertial sensor"; ¶n45: the attitude sensors collect a vehicle's pose i.e. position and orientation; ¶n138: the nine-axis fusion IMU inertial sensor measures attitude angles, accelerations, and angular velocities.), the method comprising:
computing a processed motion measurement value by modifying the outputted raw motion measurement value (See ¶n118, the calibration would involve processing the raw values to obtain calibrated values; also from ¶n118, the errors to be calibrated can include noise, bias, scale errors, or axis misalignments) based on a set of different calibration parameters (the parameters used to perform calibration would be drawn from some set),
monitoring the vicinity of the vehicle with at least one environment perception sensor (¶n30: a "laser inertial navigation system…collects point cloud data of [a] scene through the lidar"),
determining a set of occupancy maps (LIDAR scans integrated into the map of the environment, a part of simultaneous localization and mapping (SLAM); see also ¶n54: “the conversion between 3D point cloud and 2D grid navigation map”) based on mapping data (the point cloud scans) acquired by the environment perception sensor (the LIDAR) in accordance with a motion model for the vehicle (¶n27: “the multi-sensor fusion module can build an incremental environmental map in real time based on the pose information”), wherein the processed motion measurement value is used to establish the motion model for one of the occupancy maps (the calibrated values would update the pose information), and
selecting one of the different calibration parameters for modifying outputted raw motion measurement values (one or more of the parameters from the set would be used to calibrate the attitude sensor).
While Sun does not explicitly recite that the selected one of the different calibration parameters would modify subsequently outputted raw motion measurement values, it would have been obvious to do so in order to correct future measurements from persistent errors such as bias.
In light of the above, Sun does not explicitly disclose computing processed motion measurement values by modifying the outputted raw motion measurement value (i.e., computing plural processed values from a single raw value), wherein each of the processed motion measurement values is used to establish the motion model for one of the occupancy maps, and selecting, based on a comparison of the determined occupancy maps, one of the different calibration parameters for modifying subsequently outputted raw motion measurement values.
Fan teaches calibration between LIDAR and a position and orientation (POS) system (Abstract; Pg. 4, column 1, above Section 3.1: “The purpose of this paper is to find out the rotation and translation parameters
R
l
b
between [the LIDAR coordinate frame] {l} and [the inertial measurement unit (IMU) coordinate frame] {b} respectively”). As part of the invention, Fan teaches an optimization method seeking to minimize the coordinate difference between a LIDAR scan and the global map, whereby an iterative method acting on the rotation and translation parameters
R
b
l
is used to minimize the deviation of coordinates between a LIDAR and POS to obtain optimal calibration parameters (Pg. 6, column 1, under Section 3.3 and around Eqs. 6-8; note that, above Eq. 6, the LIDAR scan is transformed from the LIDAR frame to the POS frame, then from Eq. 6 to the global frame, so minimizing between the LIDAR scan and the global map requires calibrating the LIDAR and POS frames). Thus Fan describes an optimization method as an iterative method wherein multiple calibrations are attempted as part of a process of converging onto an "optimal" calibration. Each set of rotation and translation parameters attempted may be applied to the POS frame to obtain processed values, and choosing the best parameters to use is based on a comparison between each of the resultant transformed scans i.e. which transformed scans are most aligned.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Fan with the invention of Sun by computing processed motion measurement values by modifying the outputted raw motion measurement value (i.e., computing plural processed values from a single raw value), wherein each of the processed motion measurement values is used to establish the motion model for one of the occupancy maps, and selecting, based on a comparison of the determined occupancy maps, one of the different calibration parameters for modifying subsequently outputted raw motion measurement values. Computing multiple processed values for a given raw value and using each of the processed values to establish a motion model, then selecting one of the parameters to modify subsequent raw values based on comparing the determined occupancy maps, would enable one to use an iterative method to minimize an error function by changing various calibration parameters to find an optimal calibration of the attitude sensor data.
Regarding claim 13, the limitations of claim 13 are rejected for the reasons in the rejection of claim 1.
Regarding claim 14, Sun in view of Fan teaches the limitations of claim 1, and moreover it would have been obvious to include a non-transitory computer readable medium comprising instructions for carrying out the method of claim 1 in order to enable a computer to perform the method autonomously.
Regarding claim 15, Sun in view of Fan teaches the limitations of claim 13, and furthermore it would have been obvious to include a vehicle comprising the computer system of claim 13, the motion sensor, and the environment perception sensor in order to apply the method of claim 1 for an autonomous or semi-autonomous vehicle.
Regarding claim 2, Sun in view of Fan teaches the limitations of claim 1 and further teaches that a sharpness value (the error) is determined for each of the determined occupancy maps and the calibration parameter is selected based on a comparison of the determined sharpness values (see rejection of claim 1).
Regarding claim 3, Sun in view of Fan teaches the limitations of claim 2 and further teaches that an extremal value of the sharpness values is determined (the error is minimized) and a calibration parameter used for determining an occupancy map associated with the extremal value (the transformation resulting in the LIDAR scan being most aligned) is selected (see rejection of claim 1).
Regarding claim 5, Sun in view of Fan teaches the limitations of claim 1. While Sun does not explicitly state that the vicinity of the vehicle is monitored while the vehicle is moving, it would have been obvious to do so because this is common for autonomous or semi-autonomous vehicles.
Regarding claim 6, Sun in view of Fan teaches the limitations of claim 1 and further teaches that the motion sensor is an odometry sensor (Sun’s attitude sensor measures changes in position over time, therefore it is an odometry sensor).
Regarding claim 7, Sun in view of Fan teaches the limitations of claim 1 and further teaches that the raw motion measurement value represents a vehicle velocity (see ¶n138 of Sun in the rejection of claim 1; the measurements may be velocity measurements).
Regarding claim 8, Sun in view of Fan teaches the limitations of claim 1 and further teaches that the environment perception sensor is a lidar sensor or a camera (Sun, ¶n30: a lidar is used; ¶n54: the system is also equipped with a camera to inspect the environment.).
Regarding claim 9, Sun in view of Fan teaches the limitations of claim 1 and further teaches that the set of different calibration parameters comprises scaling factors (¶n118: the attitude sensor errors can include scale errors, and as already referred to in the rejection of claim 1 "the camera and LiDAR can be matched to calibrate the error and drift of the attitude sensor") but does not explicitly disclose the remaining limitations. However, considering the iteration optimization process in claim 1, it would have been reasonable for multiple scaling factors to be attempted as transformation values are adjusted as part of the iteration process, in order to find an optimal scaling factor to apply. Defining the scaling factors used as the plurality of different scaling factors, then, it would have been obvious to compute the processed motion measurement values by multiplying the raw measurement value with the different scaling factors for the reasons given above.
Regarding claim 10, Sun in view of Fan teaches the limitations of claim 1 and further teaches that the set of different calibration parameters comprises bias values (¶n118: the attitude sensor errors can include bias, and as already referred to in the rejection of claim 1 "the camera and LiDAR can be matched to calibrate the error and drift of the attitude sensor"), but does not explicitly disclose the remaining limitations. However, considering the iteration optimization process in claim 1, it would have been reasonable for multiple bias values to be attempted as transformation values are adjusted as part of the iteration process, in order to find an optimal bias value to apply. Defining the bias values used as the plurality of different bias values, then, it would have been obvious to compute the processed motion measurement values by adding the different bias values to the raw measurement value for the reasons given above.
Regarding claim 11, Sun in view of Fan teaches the limitations of claim 1 but does not explicitly teach the limitations of claim 11. However, it would have been obvious to cause the set of different calibration parameters to be spread over a range of values extending in positive direction and in negative direction from an expected value. Any subset of the real numbers with two or more unequal elements can be made to satisfy the above condition by defining the expected value to be the average of the elements of the set in question. It would have been obvious to draw from a set of multiple differently-valued parameters in order to optimize over a range of possible calibration parameters (and it would have been obvious for the parameters to be a subset of the real numbers since the attitude sensor measures real-numbered values), and doing so would satisfy the above limitations.
Regarding claim 12, Sun in view of Fan teaches the limitations of claim 1 and further teaches that data acquired by the environment perception sensor are collected for a predetermined number of time intervals (the time it takes to make a single scan).
While Sun does not teach the remaining limitations, Fan teaches that each of the occupancy maps is determined by overlaying the collected data in a spatial representation of the vicinity of the vehicle (Pg. 6, column 1, under Section 3.3: define the occupancy maps to be the LIDAR scan as it is embedded in the global map to find “the coordinate difference of associated points between global map and LiDAR scan”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Sun with the invention of Fan by determining each of the occupancy maps by overlaying the collected data in a spatial representation of the vicinity of the vehicle. Doing so would enable one to see how well each LIDAR scan aligns with the previously established map of the vehicle’s environment, which would aid in the process of finding the best calibration to apply.
Examiner’s Note
Claim 4 is distinguishable over the prior art of record.
Regarding claim 4, Sun in view of Fan teaches the limitations of claim 2. However, the prior art does not fairly suggest defining the sharpness values as in claim 4. Therefore, claim 4 is distinguishable over the prior art of record.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Bariant discloses a method of correcting a vehicle’s position as determined by odometry information via SLAM (Abstract).
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ETHAN WESLEY EDWARDS
Examiner
Art Unit 2857
/E.W.E./ Examiner, Art Unit 2857
/LINA CORDERO/ Primary Examiner, Art Unit 2857
