DETAILED ACTION
This action is in response to the remarks and amendments filed on September 29th, 2025. Claims 1-7 are pending and have been examined.
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 Interpretation
Independent claim 1 requires “carrying out local map interception on the to-be-evaluated point cloud map to obtain a local to-be-evaluated point cloud map, [and] carrying out local interception on point cloud samples to obtain local point cloud samples.” The claim terms “local map interception” and “local interception” do not have an ordinary and customary meaning to one of ordinary skill in the art. Therefore, we turn to the specification to define these claim terms. In paragraph [0096] of the specification, local point cloud interception is described (alongside downsampling) as a method of reducing the quantity of points in a point cloud. Local map interception is then described as a method where a sphere is drawn around a location, and then all the point within a radius (in this case 25m) are “intercepted out” and a local point cloud is obtained. Therefore, local interception of a point cloud is defined as a method where a certain subset of points within some predefined distance to a location are chosen, and those chosen points are then used as a local point cloud.
Independent claim 1 further requires “obtain an estimated three-dimensional coordinate system transform with different initial three-dimensional coordinate system transforms as initial values” This can be best understood to mean obtain a transformation which has an initial value which is different from the final estimated transform. The underlined portion seems to suggest that the initial three dimensional coordinate system transforms are the initial values.
Claim Objections
Claim 1 is objected to because of the following informalities:
“making the local point cloud sampled matched with the local to-be-evaluated point cloud map by a point cloud matching algorithm” contains a grammatical error, this should read “making the local point cloud sample match with the local to-be-evaluated point cloud map by a point cloud matching algorithm”
Appropriate correction is required.
Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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 and 4-7 are rejected under 35 U.S.C. 103 as being unpatentable over “Virtual Namesake Point Multi-Source Point Cloud Data Fusion Based on FPFH Feature Difference” (hereinafter referred to by its primary author, Zheng) in view of US20220189062 (hereinafter referred to by its primary author, Seo)
In regards to claim 1, Zheng teaches a method for evaluating a quality of a point cloud map based on matching, comprising the following steps: S1, acquiring a to-be-evaluated point cloud map as a point cloud matching algorithm input; S2, acquiring a point cloud sample set for matching; (Zheng Figure 1 “Source & Target Point Cloud” Examiner note: The source point cloud is analogous to the to be evaluated point cloud map, and the target point cloud is analogous to the point cloud sample.) S3, carrying out local map interception on the to-be-evaluated point cloud map to obtain a local to-be-evaluated point cloud map, carrying out local interception on point cloud samples to obtain local point cloud samples (Zheng Section 3.2 “Put all the obtained conversion points p’i into the target point cloud Q, find the neighbor points of the conversion point in the target point cloud Q, set a threshold r at this time, calculate the Euclidean distance d between the conversion point and the nearest neighbor point in the target point cloud, compare the calculated distance with the threshold. If it is greater than the threshold, it indicates that the conversion point does not overlap in the source point cloud, delete the conversion point and keep the conversion point qi, qi 2 R3, i = 1, . . . , Nq less than the threshold.” Examiner note: This section describes how this invention uses a neighborhood of points in the target point cloud to determine if any of the conversion points from the source point cloud match. In this scenario, a single one of the conversion points is analogous to the local to-be-evaluated point cloud map, as the conversion point is a subset of the source point cloud, and the neighborhood of points that are within threshold distance r to a single conversion point are analogous to the local point cloud samples, as they are a subset of points from the target point cloud, and the subset of points changes based on which conversion point is used.), making the local point cloud samples matched with the local to-be-evaluated point cloud map by a point cloud matching algorithm, and iterating and averaging to obtain an estimated three-dimensional coordinate system transform with different initial three-dimensional coordinate system transforms as initial values; (Zheng Figure 7; Section 3.2 Steps 1-10; Section 3.3 “In the previous section, the low-precision point cloud was registered with reference to the high-precision point cloud, and the optimal rigid transformation matrix was calculated. However, this is only a rotation and translation operation for the entire point cloud. The geometric difference of the point cloud may be different, so the improvement of the point cloud accuracy of the ICP algorithm is limited to the whole, which requires further improvement in the detailed area. For the point cloud in the overlapping area of the source point cloud and the target point cloud, after performing the point cloud registration algorithm operation based on the virtual namesake point, it is necessary to search for the virtual namesake point based on the FPFH feature difference again, that is, this time in the existing” Examiner note: The initial optimal rigid transformation is calculated, which is the initial value, and then that translation is iterated upon in this section for further matching, as can be seen in figure 7. This initial optimal rigid transformation is found by repetition, as can be seen in step 10 of section 3.2.) and S4, calculating point cloud matching errors; (Zheng Section 4.2 “We evaluate the registration by computing the mean isotropic rotation and translation errors”)
Zheng does not teach S5, calculating a quality evaluation score of the to-be-evaluated point cloud map as a point cloud matching algorithm output.
However, Seo teaches S5, calculating a quality evaluation score of the to-be-evaluated point cloud map as a point cloud matching algorithm output. (Seo Paragraph [0085] “The error function f.sub.Error is an average value of the squared Euclidean distance (SED) of X.sub.ref and X.sub.i′ and is defined as in Equation 7.” Examiner note: The score in this reference is the error function, which is an average of all errors.)
Seo is considered to be analogous to the claimed invention because they are both in the same field of aligning 3D point clouds. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Zheng to include the teachings of Seo, to provide the benefit of reducing error further, by using the error to update parameters (Seo Paragraph [0090] “Because the error of the currently transformed coordinates can be obtained through Equation 7, the parameter is updated such that the error is reduced using Equation 8.”)
In regards to claim 4, Zheng in view of Seo teaches the method according to claim 1, wherein the original three-dimensional coordinate system transforms briefly describe a transform relation between Lidar coordinate systems where the point cloud samples are located and a navigation coordinate system. (Zheng Figure 7 Examiner note: The coordinate system in which the “source” point cloud was obtained is analogous to the LIDAR coordinate system from which it was captured, and the coordinate system in which the “target” point cloud was obtained is analogous to the navigation coordinate system. Since this reference shows a transformation from source point cloud to target point cloud, it also shows a transformation from LIDAR coordinate system to navigation coordinate system.)
In regards to claim 5, Zhen in view of Seo teaches the method according to claim 1, wherein the point cloud matching algorithm input is two point clouds, and the point cloud matching algorithm output is an estimated transform relation between two point cloud coordinate systems; and the point cloud matching algorithm input is the point cloud samples and the to-be-evaluated point cloud map; (Zheng Figure 1 “Source & Target Point Cloud”) and the point cloud matching algorithm output is an estimated transform relation between Lidar coordinate systems where the point cloud samples are located and a navigation coordinate system. (Zhang Figure 7 Examiner note: The arrow in this figure represents the final translation from the to be registered point cloud and the registered point cloud.)
In regards to claim 6, Zhen in view of Seo teaches the method according to claim 1, wherein the point cloud matching errors are quantitatively-evaluated overlap degrees between the point cloud samples subjected to the estimated three-dimensional coordinate system transforms and the to-be-evaluated point cloud map. (Zheng Section 4.2 “In addition, the Chamfer Distance (CD) is used to evaluate the distance between the point clouds. If there are two point clouds S1, S2, the Chamfer Distance (CD) is calculated by Formula (10).” Examiner note: The Chamfer Distance is a measurement of the average distance between all points in two point clouds. This is analogous to overlap degrees, as the amount of overlap between two points depends on the distance between those two points.)
In regards to claim 7, Zheng in view of Seo teaches the method according to claim 1, wherein the quality evaluation score is obtained by calculating a mean of the point cloud matching errors corresponding to all the point cloud samples, and normalizing the mean. (Seo Paragraph [0085] “The error function f.sub.Error is an average value of the squared Euclidean distance (SED) of X.sub.ref and X.sub.i′ and is defined as in Equation 7.” Examiner note: In this reference, point cloud matching error is analogous to the squared Euclidean distance, since both are measurements of the difference between the two point clouds. Then, those errors are averaged into an error function.)
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Zheng in view of Seo, and further in view of US20160379366 (hereinafter referred to by its primary author Shah)
In regards to claim 2, Zheng in view of Seo teaches the method according to claim 1, wherein the to-be-evaluated point cloud map is a point cloud, wherein a coordinate system for the point cloud is a navigation coordinate system.
Zheng in view of Seo fails to teach the step of acquiring the to-be-evaluated point cloud map comprises the following steps: collecting point cloud data by a mobile mapping vehicle equipped with a plurality of sensors comprising a global positioning system (GPS), an inertial measurement unit (IMU) and a mechanical rotary Lidar, moving along a road, and establishing/acquiring the to-be-evaluated point cloud map; and the point cloud data comprises a GPS positioning result, an IMU mapping result and a mechanical rotary Lidar scanning result, and data collected by sensors comprising an electronic compass, a wheel odometer and a barometer are further allowed to be added according to actual situations.
However, Shah teaches the step of acquiring the to-be-evaluated point cloud map comprises the following steps: collecting point cloud data by a mobile mapping vehicle equipped with a plurality of sensors comprising a global positioning system (GPS), an inertial measurement unit (IMU) and a mechanical rotary Lidar, moving along a road, and establishing/acquiring the to-be-evaluated point cloud map (Shah Paragraph [0029] “Generally, the computing system 200 illustrates an environment in which sensor data points (for instance, Light Detection and Ranging (“LiDAR”), Global Positioning System (“GPS”) and Inertial Measurement Unit (“IMU”) data points) may be collected and resultant point clouds may be spatially aligned.”); and the point cloud data comprises a GPS positioning result, an IMU mapping result and a mechanical rotary Lidar scanning result (Shah Paragraph [0058] “The estimated vehicle location and orientation, derived from on-board GPS and IMU sensors is also associated with each point cloud frame, and allows them to be approximately aligned in a global coordinate system.” Examiner note: The IMU and GPS data is used to determine the location of the vehicle taking the LIDAR data, which can be used in combination for the point cloud data alignment.), and data collected by sensors comprising an electronic compass, a wheel odometer and a barometer are further allowed to be added according to actual situations. (Examiner note: Due to the wording “are further allowed to be added”, these elements are not required by the claim and therefore are not taught.)
Shah is considered to be analogous to the claimed invention because they are both in the same field of aligning 3D point clouds. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Zheng in view of Seo to include the teachings of Shah to provide the advantage of integrating high confidence GPS and IMU sensor data to improve consistency of point cloud registration (Shah Paragraph [0058] “Finally, the loop point clouds may be aligned via a closed-form, least squares inter-region registration step that also integrates high-confidence GPS/IMU data, to produce a globally consistent and accurate city-scale point cloud.”)
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Zheng in view of Seo, and further in view of “Trends in 3D Point Cloud Contents Sampling in Mobile AR/VR Platforms” (hereinafter referred to by its primary author Baek)
In regards to claim 3, Zheng in view of Seo teaches the method according to claim 1, wherein each element in the point cloud sample set consists of a point cloud sample and an original three-dimensional coordinate system transform; (Zheng Section 3.3 Examiner note: As previously recited with reference to step S3, the original three-dimensional coordinate system transform is analogous to the initial optimal rigid transformation, which is a description of a transform from the point cloud to be registered and the high-precision point cloud.) coordinate systems of the point cloud samples are Lidar coordinate systems; (Zheng Figure 1 “Source Point Cloud” Examiner note: Inherently, since the LIDAR system that captures a point cloud does not know the coordinate system of the navigation unit, the source point cloud will be in the coordinate system of the LIDAR system which captured it.) and the Lidar coordinate systems refer to sensor coordinate systems where the point cloud data collected by the mechanical rotary Lidar is located (Zheng Figure 1 “Source Point Cloud” Examiner note: When a point cloud is captured, it is from the coordinate system of the LIDAR system from which it was captured.).
Zheng in view of Seo fails to teach a quantity of points of the point cloud samples is less than a quantity of points of the to-be-evaluated point cloud map.
However, Baek teaches a quantity of points of the point cloud samples is less than a quantity of points of the to-be-evaluated point cloud map. (Baek Figure 3 Examiner note: This reference shows that when a point cloud is sampled, it will have less points than the point cloud from which it was sampled.)
Baek is considered to be analogous to the claimed invention because they are both in the same field of point cloud sampling Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Zheng in view of Seo to include the teachings of Baek to provide the advantage of lower run time and decreased memory usage (Baek Section III “Down sampling the point clouds results in lower run time memory usage”)
Response to Arguments
Applicant's arguments filed on September 29th, 2025 have been fully considered but they are not persuasive.
Applicant alleges on page 6-7 of “Remarks” that “theoretically, the target point cloud is analogous to the point cloud sample, and the source point cloud is analogous to the to be evaluated point cloud map”. Examiner believes that the relationship between the target and source point cloud is trivial, as there are no operations which are performed solely on the target point cloud or source point cloud and they therefore can be used interchangeably. Nonetheless, in an effort to forward prosecution, examiner has swapped the interpretation of the target and source point cloud.
Applicant alleges on page 7 of “Remarks” that “Kim fails to suggest or teach…” Examiner believes this is meant to refer to Zheng, not Kim, as no prior art labelled “Kim” is relied on for any rejection. Therefore, this argument actually read “Zheng fails to suggest or teach the following features recited in the amended claim 1, ‘iterating and averaging to obtain an estimated three-dimensional coordinate system transform with different initial three-dimensional coordinate system transforms as initial values.’ The above feature have reduce the influence of the original three-dimensional coordinate system transform on a final matching result as much as possible.” Examiner respectfully disagrees. As discussed in the claim interpretation section, this claim language (as currently presented) only requires that the initial transform which is iterated upon be different from the final transform. Since no further argument is presented with respect to this limitation, the rejection is maintained.
Conclusion
THIS ACTION IS MADE FINAL. 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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/CALEB L ESQUINO/Examiner, Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677
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