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 .
Continued Examination
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/20/2026 has been entered.
Response to Arguments
Applicant’s arguments, see pages 7-10, filed 02/20/2026, with respect to the rejection(s) of claim(s) 1-3, 6-10, 13, and 14 under 35 USC 103 have been fully considered and are persuasive in light of the amendments to the claims. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Chowdhury et al. ("Extended Rigid Multi-Target Tracking").
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.
Claim(s) 1, 2, 6, 7, 9, 10, and 13 is/are rejected under 35 U.S.C. 102(a)(1) as unpatentable over Watanabe et al. (JP 2020038498, previously cited) in view of Nagata et al. (WO 2018180096, previously cited) in view of Chowdhury et al.
Claim 1.
Watanabe et al. teaches:
An information processing device
(Watanabe – [0001]) “The present invention relates to a self-location estimation device.”
a first candidate position acquisition unit configured to acquire a first candidate position of a moving body
(Watanabe – [0006]) “a self-position estimation device comprising a measurement unit that measures the self-position of a moving body, and an estimation unit that corrects an estimated value of the self-position estimated by dead reckoning by reflecting a measurement value of the self-position measured by the measurement unit, wherein the estimation unit adjusts the degree to which the measurement value is reflected depending on the quality of the measurement value.”
a second candidate position acquisition unit configured to acquire a second candidate position of the moving body, the second candidate position being determined based on matching between data based on an output from an external sensor provided in the moving body and map data
(Watanabe – [0009]) “the measurement unit measures a second measurement value of the self-location by performing scan matching of an ICP algorithm using a point cloud of measurement points measured by a distance sensor scanning around the moving body”
(Watanabe – [0012]) “the scan matching unit calculates the mean square value of the point cloud of measurement points measured by the distance sensor at the map data used for the scan matching and the current position of the moving body”
a reliability value calculation unit configured to calculate a reliability value representing a reliability of the second candidate position determined based on the matching
(Watanabe – [0029]) “the scan matching unit 4 generates an index (hereinafter referred to as a ‘second index’) indicating the measurement state (e.g., measurement accuracy) of the self-position measured by the scan matching unit 4”
an estimated position determination unit configured to determine an estimated position of the moving body based on the first candidate position, the second candidate position, and the reliability value
(Watanabe – [0006]) “a self-position estimation device comprising a measurement unit that measures the self-position of a moving body, and an estimation unit that corrects an estimated value of the self-position estimated by dead reckoning by reflecting a measurement value of the self-position measured by the measurement unit, wherein the estimation unit adjusts the degree to which the measurement value is reflected depending on the quality of the measurement value.”
wherein the data is point cloud data
(Watanabe – [0009]) “the measurement unit measures a second measurement value of the self-location by performing scan matching of an ICP algorithm using a point cloud of measurement points measured by a distance sensor scanning around the moving body”
wherein the map data is voxel data representing positions of an object with respect to each voxel that is a unit area
(Watanabe – [0026]) “the map data is a voxel map.”
associate measurement points of the point cloud data with the each voxel
(Watanabe – [0051]) “scan matching using the ICP algorithm with the voxel map and the input point cloud to measure the self-position”
determine the second candidate position based on the matching between voxel data of voxels subjected to the association and the measurement points associated with the voxels
(Watanabe – [0051]) “scan matching using the ICP algorithm with the voxel map and the input point cloud to measure the self-position”
wherein the information processing device performs autonomous driving control of the moving body to travel based on an estimation result of the estimated position of the moving body
(Watanabe – [0044]) “the control device 100 controls the amount of accelerator and brake operation and the steering angle of the transport vehicle M based on the ex-post estimated values obtained from the self-position estimation unit 6”
While Watanabe et al. teaches various units; Watanabe et al. does not explicitly teach that such units are performed by a processor coupled to a memory, or determining reliability based on an association ratio. However, Nagata et al. teaches:
a processor coupled to a memory storing instructions for the processor
(Nagata – [0044]) “The CPU reads and executes various programs stored in the ROM and the storage unit 22, and the control unit 21 controls the estimation device 20 using various functions”
wherein the reliability value calculation unit is configured to calculate the reliability value based on at least an association ratio that is a ratio of a number of the measurement points associated with the voxels to a number of the measurement points of the point cloud data
(Nagata – [0054]) “The control unit 21 compares the voxel data generated from the point cloud data with the voxel data obtained from the map data, and estimates the position with the highest matching rate as the more accurate current position of the vehicle 5. This matching rate may be the ratio of the number of voxels whose voxels approximately match those of voxels obtained from the map data, among those generated from the point cloud data.”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings, modifying the position estimation device of Watanabe et al. with the position estimation device of Nagata et al. Both are directed toward the same field of endeavor; therefore, a person of ordinary skill in the art would have recognized that the matching rate of Nagata et al. could be used as a metric for the device of Watanabe et al. with predictable results. One would have been motivated to do this because a ratio requires less computational work than an RMS value.
Nagata et al. teaches determining reliability of data based on a matching rate; however, Nagata et al. does not explicitly teach determining a measurement point density. Chowdhury et al. teaches:
wherein the association ratio reflects a density of the measurement points associated with the voxels to indicate an occlusion state
(Chowdhury – Segment Motion Detection from Range-Image Pairs) “The density d associated with each point cloud k at a given voxel i in this volume is then defined by (4) and (5).
d
i
k
=
c
i
k
/
S
k
… where S is the total number of points from a given segment point cloud in the volume of intersection.”
(Chowdhury – Real-Time Moving Segment Registration) “If the sum of absolute density differences D across all voxels is lower than a maximum threshold value (i.e., a hyper-parameter) the pair are considered potentially matching.”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings, modifying the matching rate of Nagata et al. with the density calculation of Chowdhury et al. Both Nagata et al. and Chowdhury et al. are directed towards matching voxel data to point cloud data; therefore, a person of ordinary skill in the art would have recognized that this substitution could be made with predictable results. One would have been motivated to do this because the density data can additionally be used in detecting motion between adjacent timesteps (Chowdhury – Segment Motion Detection from Range-Image Pairs).
Claim 2.
The combination of Watanabe et al., Nagata et al., and Chowdhury et al. teaches all the limitations of claim 1, as discussed above. Watanabe et al. further teaches:
wherein the reliability value calculation unit is configured to determine the reliability value based on at least a score value indicating a fitting degree of the matching
(Watanabe – [0038]) “when the evaluation result of the first evaluation unit 7 indicates that the first measurement value is good, the error variance value calculation unit 9 sets the error variance value Q1 to a first value, and when the evaluation result of the first evaluation unit 7 indicates that the first measurement value is bad, the error variance value calculation unit 9 sets the error variance value Q1 to a second value greater than the first value.”
Claim 6.
The combination of Watanabe et al., Nagata et al., and Chowdhury et al. teaches all the limitations of claim 5, as discussed above. Watanabe et al. further teaches:
wherein the reliability value calculation unit is configured to calculate the reliability that has a proportional relation with or a positive correlation with: the association ratio; a size of down-sampling to be applied to first point cloud data that is point cloud data outputted by the external sensor or the number of measurement points of the first point cloud data; and a score value indicating a fitting degree of the matching
(Watanabe – [0038]) “when the evaluation result of the first evaluation unit 7 indicates that the first measurement value is good, the error variance value calculation unit 9 sets the error variance value Q1 to a first value, and when the evaluation result of the first evaluation unit 7 indicates that the first measurement value is bad, the error variance value calculation unit 9 sets the error variance value Q1 to a second value greater than the first value.”
(Watanabe – [0051]) “as a second index, an RMS which is the root mean square value of the map data and the input point cloud, and NUM which is the number of points in the input point cloud”
Claim 7.
The combination of Watanabe et al., Nagata et al., and Chowdhury et al. teaches all the limitations of claim 1, as discussed above. Watanabe et al. further teaches:
wherein the estimated position determination unit is configured to determine the estimated position by a weighted mean of the first candidate position and the second candidate position, which are weighted based on the reliability value
(Watanabe – [0041]) “if the quality of the first measurement value or the second measurement value is good, the integration unit 5 increases the weight of the measurement value and performs sensor fusion, and if the quality of the first measurement value or the second measurement value is poor, the integration unit 5 estimates the self-position by decreasing the weight of the measurement value and performing sensor fusion.”
Claim 9.
The combination of Watanabe et al., Nagata et al., and Chowdhury et al. teaches all the limitations of claim 1, as discussed above. Watanabe et al. further teaches:
wherein the first candidate position acquisition unit is configured to acquire the position of the moving object determined by dead reckoning as the first candidate position
(Watanabe – [0006]) “a self-position estimation device comprising a measurement unit that measures the self-position of a moving body, and an estimation unit that corrects an estimated value of the self-position estimated by dead reckoning by reflecting a measurement value of the self-position measured by the measurement unit, wherein the estimation unit adjusts the degree to which the measurement value is reflected depending on the quality of the measurement value.”
Claim 10.
Watanabe et al. teaches:
A control method executed by a computer
(Watanabe – [0014]) “a method in which the integration unit 5 according to one embodiment of the present invention estimates the self-position of the transport vehicle M at time k using the first measurement value and the second measurement value.”
The rest of the claim is rejected by the same rationale as claim 1.
Claim 13.
Watanabe et al. teaches:
a second candidate position acquisition unit configured to acquire a second candidate position of the moving body, the second candidate position being determined based on matching between data based on an output from a measurement device provided in the moving body and map data
(Watanabe – [0009]) “the measurement unit measures a second measurement value of the self-location by performing scan matching of an ICP algorithm using a point cloud of measurement points measured by a distance sensor scanning around the moving body”
(Watanabe – [0012]) “the scan matching unit calculates the mean square value of the point cloud of measurement points measured by the distance sensor at the map data used for the scan matching and the current position of the moving body”
an acquisition unit configured to acquire a measurement accuracy of the measurement device
(Watanabe – [0029]) “the scan matching unit 4 generates an index (hereinafter referred to as a ‘second index’) indicating the measurement state (e.g., measurement accuracy) of the self-position measured by the scan matching unit 4”
a reliability value calculation unit configured to calculate a reliability value representing a reliability of the second candidate position determined based on the matching, and based on a factor of the measurement accuracy
(Watanabe – [0029]) “the scan matching unit 4 generates an index (hereinafter referred to as a ‘second index’) indicating the measurement state (e.g., measurement accuracy) of the self-position measured by the scan matching unit 4”
(Watanabe – [0036]) “The second evaluation unit 8 evaluates the quality of the second measurement value based on the second index.”
The rest of the claim is rejected by the same rationale as claim 1.
Claim(s) 3 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Watanabe et al., Nagata et al., and Chowdhury et al. as applied to claim 1 above, and further in view of Yendluri et al. (US 20190323844, previously cited).
Claim 3.
The combination of Watanabe et al., Nagata et al., and Chowdhury et al. teaches all the limitations of claim 1, as discussed above. Watanabe et al. further teaches:
wherein the reliability value calculation unit is configured to determine the reliability value based on at least one of a size of the down-sampling or the number of measurement points of the first point cloud data
(Watanabe – [0051]) “as a second index, an RMS which is the root mean square value of the map data and the input point cloud, and NUM which is the number of points in the input point cloud”
Watanabe et al. does not explicitly teach down-sampling of data; however, Yendluri et al. teaches:
wherein the data is second point cloud data that is point cloud data obtained by applying down-sampling to first point cloud data that is point cloud data outputted from the external sensor
(Yendluri – [0029]) “process 500 can also down sample the point clouds (e.g., remove duplicative data points, remove stray data points).”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings, modifying the localization system of Watanabe et al. with the point cloud down-sampling of Yendluri et al. One would have been motivated to do this because it allows the localization process to run faster (Yendluri – [0029]).
Claim 14.
The combination of Watanabe et al., Nagata et al., and Chowdhury et al. teaches all the limitations of claim 1, as discussed above. Watanabe et al. further teaches:
wherein the reliability value calculation unit is configured to determine the reliability value based on a size of the
(Watanabe – [0029]) “the greater the number of points in the input point cloud, NUM, the smaller the measurement error of the self-position by scan matching.”
Watanabe et al. does not explicitly teach down-sampling of the point cloud prior to input; however, Yendluri et al. teaches:
wherein the data is second point cloud data that is point cloud data obtained by applying down-sampling to first point cloud data that is point cloud data outputted from the external sensor
(Yendluri – [0029]) “process 500 can also down sample the point clouds (e.g., remove duplicative data points, remove stray data points).”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings for the reasons given in discussion of claim 3.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Watanabe et al., Nagata et al., and Chowdhury et al. as applied to claim 7 above, and further in view of "Feature scaling" (NPL 1, previously cited).
Claim 8.
The combination of Watanabe et al., Nagata et al., and Chowdhury et al. teaches all the limitations of claim 7, as discussed above. Watanabe et al. further teaches:
wherein the estimated position determination unit is configured to calculation a reliability index that is the reliability value
(Watanabe – [0041]) “if the quality of the first measurement value or the second measurement value is good, the integration unit 5 increases the weight of the measurement value and performs sensor fusion, and if the quality of the first measurement value or the second measurement value is poor, the integration unit 5 estimates the self-position by decreasing the weight of the measurement value and performing sensor fusion.”
Watanabe et al. does not explicitly teach normalization from 0 to 1; however, “Feature scaling” teaches:
value normalized to range from 0 to 1
(“Feature scaling” – Methods) “rescaling the range of features to scale the range in [0, 1]”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings, modifying the variable weights of Watanabe et al. with the rescaling of “Feature scaling”. One would have been motivated to do this because a range of 0 to 1 is more intuitively understood than a range between two relatively arbitrary values.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SARAH A MUELLER whose telephone number is (703)756-4722. The examiner can normally be reached M-Th 7:30-12:00, 1:00-5:30; F 8:00-12:00.
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/S.A.M./Examiner, Art Unit 3669
/NAVID Z. MEHDIZADEH/Supervisory Patent Examiner, Art Unit 3669
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