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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 6 November, 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 22 and 32 are objected to because of the following informalities:
Claim 22 does not explicitly make clear what “wherein the controller determines the objective function in a bi-directional manner”. As written, it isn’t clear how a function is determined bi-directionally as the plain definition of bi-directionally implies determining the objective function from two directions simultaneously. When referencing the applicant’s specification for bi-directional objective functions, page 16, line 17 – 18 discloses that an objective function can be refined in a bidirectional manner. This appears to be shown in figure 6 where the optimization process is optimized with respect to the first point cloud followed by optimizing with respect to the second point cloud. The examiner believes claim 22 should be amended to more clearly define how the determination with respect to the objective function relies on a “bi-directional manner”.
Claim 32 is objected for the same rationale.
Appropriate correction is required.
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 14 - 34 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
When reviewing independent claim 14, and based upon consideration of all of the relevant factors with respect to the claim as a whole, claim 14 are held to claim an abstract idea without reciting elements that amount to significantly more than the abstract idea and is/are therefore rejected as ineligible subject matter under 35 U.S.C. 101. The Examiner will analyze claim 14, and similar rationale applies to independent claims 24 and 34. The rationale, under MPEP § 2106, for this finding is explained below:
The claimed invention (1) must be directed to one of the four statutory categories, and (2) must not be wholly directed to subject matter encompassing a judicially recognized exception, as defined below. The following two step analysis is used to evaluate these criteria.
Step 1: Is the claim directed to one of the four patent-eligible subject matter categories: process, machine, manufacture, or composition of matter?
When examining the claim under 35 U.S.C. 101, the Examiner interprets that the claims is related to a machine since the claim is directed to a system for unsupervised estimation of motion of objects through 3D space.
Step 2a, Prong 1: Does the claim wholly embrace a judicially recognized exception, which includes laws of nature, physical phenomena, and abstract ideas, or is it a particular practical application of a judicial exception?
The Examiner interprets that the judicial exception applies since the claim 14 limitations of “a controller configured to determine a correspondence between at least a first point in a first point cloud of the plurality of point clouds and a composite value” is directed to an abstract idea. The claim is related to mathematical concept by the claim limitation merely performing a mathematical comparison between points and values . If the claim recites a judicial exception (i.e., an abstract idea enumerated in MPEP § 2106.04(a), a law of nature, or a natural phenomenon), the claim requires further analysis in Prong Two.
Step 2a, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
The Examiner interprets that claim 14 limitations do not provide additional elements or combination of additional elements to a practical application since the claim is generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). See, MPEP §2106.04(a), Because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"). OR Genetic Techs. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (eligibility "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself."). For a claim reciting a judicial exception to be eligible, the additional elements (if any) in the claim must "transform the nature of the claim" into a patent-eligible application of the judicial exception, Alice Corp., 573 U.S. at 217, 110 USPQ2d at 1981, either at Prong Two or in Step 2B. If there are no additional elements in the claim, then it cannot be eligible. In such a case, after making the appropriate rejection (see MPEP § 2106.07 for more information on formulating a rejection for lack of eligibility), it is a best practice for the examiner to recommend an amendment, if possible, that would resolve eligibility of the claim.
Step 2b: If a judicial exception into a practical application is not recited in the claim, the Examiner must interpret if the claim recites additional elements that amount to significantly more than the judicial exception.
The Examiner interprets that the claims do not amount to significantly more since the claim states “wherein points in the plurality of point clouds obtained from the sensor provide distance information about the points relative to the location of the sensor in the 3D space” , “wherein the composite value is a function of a plurality of points in a second point cloud of the plurality of point clouds”, “wherein the correspondence is represented as a flow vector directed from the first point in the first point cloud to a location within the second point cloud corresponding to the composite value”, and “wherein the composite value weighs certain of the plurality of points of the second point cloud relatively more than other points of the plurality of points of the second point cloud”. These claim limitations apply constraints to the data or additional aspects of mathematical concepts, but do not amount to significantly more than the judicial exception.
Furthermore, the generic computer components of the sensor and controller recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system.
Claims 15 – 23 and 25 - 33 depending on the independent claims include all the limitation of the independent claim. The Examiner finds that claims 15 – 23 and 25 - 33 do not state significantly more since the claims only recites “wherein the sensor is moveable within the 3D space.” in claims 15 and 25; “further comprising a vehicle, wherein the sensor is coupled to the vehicle, wherein the vehicle is configured to autonomously navigate within the 3D space.” in claims 16 and 26; “wherein the controller determines the correspondence using an objective function based on a distance measured between points in the first point cloud and corresponding composite values in the second point cloud.” in claims 17 and 27; “wherein the controller determines the correspondence and an estimate of motion of the sensor from a location where the first point cloud is obtained to a location where the second point cloud is obtained, wherein the determining of the correspondence and the motion estimate uses an objective function based on a distance measure between points in the first point cloud and corresponding composite values in the second point cloud, and wherein the distance measure compensates for the motion.” in claims 18 and 28; “wherein the controller determines distances from the first point in the first point cloud to each point of a plurality of points in the second point cloud, wherein the controller compares the determined distances to a distance threshold, and wherein the controller uses points whose determined distance is less than the distance threshold in the determining of the composite value.” in claims 19 and 29; “wherein the controller determines the correspondence iteratively, and wherein the distance threshold has an initial value for a first iteration and the controller updates the distance threshold value in subsequent iterations of the determining of the correspondence.” in claims 20 and 30; “wherein the controller determines the flow vectors in a bi- directional manner.” in claims 21 and 31; “wherein the controller determines the objective function in a bi-directional manner.” in claims 22 and 32; and “wherein the objective function incorporates a rigidity constraint.” in claims 23 and 33.
Thus, claims 15 – 23 and 25 - 33 recite the same abstract idea or insignificant extra activities and therefore are not drawn to the eligible subject matter as they are directed to the abstract idea without significantly more.
Therefore, the Examiner interprets that the claims are rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of pre-AIA 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a) the invention was known or used by others in this country, or patented or described in a printed publication in this or a foreign country, before the invention thereof by the applicant for a patent.
(b) the invention was patented or described in a printed publication in this or a foreign country or in public use or on sale in this country, more than one year prior to the date of application for patent in the United States.
Claims 14 – 19, 24 – 29, and 34 are rejected under pre-AIA 35 U.S.C. 102(a)(2) as being anticipated by Liang et al (U.S. Patent Publication No. 2023/0342954 A1, hereinafter “Liang”).
Regarding claim 14, Liang teaches a system for unsupervised estimation of motion of objects through a 3D space comprising:
a physical sensor located within the 3D space and configured to generate a plurality of point clouds (¶ 0037: The first point cloud W1 is determined on the basis of a first measurement using the lidar sensor 4 and the second point cloud W2 is determined on the basis of a second measurement using the lidar sensor in this case.); and
a controller (¶ 0003: The system additionally comprises an assignment processor… the system additionally comprises a longitudinal status estimation processor…) configured to determine a correspondence between at least a first point in a first point cloud of the plurality of point clouds and a composite value (¶ 0014: The corresponding point pairs in the first point cloud and the second point cloud can then be determined.; ¶ 0037: The ego motion of the vehicle 1 can then be determined on the basis of an assignment of corresponding first points P1 and second points P2…; ¶ 0043: In a step S6, the respective displacement between corresponding first points P1 and second points P2 is then determined…),
wherein points in the plurality of point clouds obtained from the sensor provide distance information about the points relative to the location of the sensor in the 3D space (¶ 0040: The areas 11 and 12 of the point clouds W1, W2 originate here from the guide rails 9. The points P1, P2, which originate from the guide rails 9, form elongate clusters which extend along the vehicle longitudinal direction x.),
wherein the composite value is a function of a plurality of points in a second point cloud of the plurality of point clouds (¶ 0043: For this purpose, the iterative closest point method is used. A translation and a rotation can thus be determined between the corresponding points P1, P2 and the ego motion of the vehicle 1 can be derived therefrom.),
wherein the correspondence is represented as a flow vector directed from the first point in the first point cloud to a location within the second point cloud corresponding to the composite value (Figure 2; FIG. 2 is a schematic illustration of point clouds which are determined on the basis of measurements by the lidar sensor, wherein the measurements are recorded at different points in time; ¶ 0037: FIG. 2 shows, in a very simplified illustration, a first point cloud W1, which comprises first points P1, and a second point cloud W2, which comprises second points P2.), and
wherein the composite value weighs certain of the plurality of points of the second point cloud relatively more than other points of the plurality of points of the second point cloud (¶ 0014: In addition, a weighting of the points can be performed. The points can be weighted in dependence on their surroundings and thus features and regions of interest can be highlighted.).
Regarding claim 15, Liang teaches the system of claim 14.
Additionally, Liang teaches wherein the sensor is moveable within the 3D space (¶ 0036: FIG. 1 shows a schematic illustration of a vehicle 1, which is designed as a passenger vehicle, in a top view. The vehicle 1 has a vehicle longitudinal direction x and a vehicle transverse direction y. The vehicle 1 comprises a sensor system 2, which has a computing device 3. This computing device 3 can be formed, for example, by an electronic control unit of the vehicle 1. Furthermore, the sensor system 2 comprises a lidar sensor 4, using which an object in an environment 5 of the vehicle 1 can be acquired.).
Regarding claim 16, Liang teaches the system of claim 15.
Additionally, Liang teaches further comprising a vehicle, wherein the sensor is coupled to the vehicle, wherein the vehicle is configured to autonomously navigate within the 3D space (¶ 0002: The determination of the ego motion of a vehicle is of decisive importance for driver assistance systems or automated or autonomous driving (emphasis added).; ¶ 0036: FIG. 1 shows a schematic illustration of a vehicle 1, which is designed as a passenger vehicle, in a top view. The vehicle 1 has a vehicle longitudinal direction x and a vehicle transverse direction y. The vehicle 1 comprises a sensor system 2, which has a computing device 3. This computing device 3 can be formed, for example, by an electronic control unit of the vehicle 1. Furthermore, the sensor system 2 comprises a lidar sensor 4, using which an object in an environment 5 of the vehicle 1 can be acquired.).
Regarding claim 17, Liang teaches the system of claim 14.
Additionally, Liang teaches wherein the controller determines the correspondence using an objective function based on a distance measured between points in the first point cloud and corresponding composite values in the second point cloud (¶ 0043: In a step S6, the respective displacement between corresponding first points P1 and second points P2 is then determined. For this purpose, the iterative closest point method (emphasis added) is used. A translation and a rotation can thus be determined between the corresponding points P1, P2 and the ego motion of the vehicle 1 can be derived therefrom; Examiner’s note: The examiner is interpreting “Objective function” to be any function that performs optimization. As the iterative closest point method optimizes the distance between two point clouds, this is understood to read on “objective function”.).
Regarding claim 18, Liang teaches the system of claim 15.
Additionally, Liang teaches wherein the controller determines the correspondence and an estimate of motion of the sensor from a location where the first point cloud is obtained to a location where the second point cloud is obtained (¶ 0043: In a step S6, the respective displacement between corresponding first points P1 and second points P2 is then determined. For this purpose, the iterative closest point method (emphasis added) is used. A translation and a rotation can thus be determined between the corresponding points P1, P2 and the ego motion of the vehicle 1 can be derived therefrom),
wherein the determining of the correspondence and the motion estimate uses an objective function based on a distance measure between points in the first point cloud and corresponding composite values in the second point cloud (¶ 0043: In a step S6, the respective displacement between corresponding first points P1 and second points P2 is then determined. For this purpose, the iterative closest point method (emphasis added) is used.; Examiner’s note: The examiner is interpreting “Objective function” to be any function that performs optimization. As the iterative closest point method optimizes the distance between two point clouds, this is understood to read on “objective function”.), and
wherein the distance measure compensates for the motion (¶ 0043: A translation and a rotation can thus be determined between the corresponding points P1, P2 and the ego motion of the vehicle 1 can be derived therefrom).
Regarding claim 19, Liang teaches the system of claim 17.
Additionally, Liang teaches wherein the controller determines distances from the first point in the first point cloud to each point of a plurality of points in the second point cloud (¶ 0043: In a step S6, the respective displacement between corresponding first points P1 and second points P2 is then determined. For this purpose, the iterative closest point method (emphasis added) is used. A translation and a rotation can thus be determined between the corresponding points P1, P2 and the ego motion of the vehicle 1 can be derived therefrom), and
wherein the controller compares the determined distances to a distance threshold, and wherein the controller uses points whose determined distance is less than the distance threshold in the determining of the composite value (¶ 0022: After the determination of the filtered point clouds, the first points of the first point cloud can be assigned to the second points of the point cloud. Closest neighbor searches can be used for this purpose… If, for example, the distance between points assigned to one another exceeds a predetermined value, these points can be sorted out.).
The rejection of device claim 14 above applies mutatis mutandis to the corresponding limitations of method claim 24 while noting that the rejection above cites to both device and method disclosures.
The rejection of device claim 15 above applies mutatis mutandis to the corresponding limitations of method claim 25 while noting that the rejection above cites to both device and method disclosures.
The rejection of device claim 16 above applies mutatis mutandis to the corresponding limitations of method claim 26 while noting that the rejection above cites to both device and method disclosures.
The rejection of device claim 17 above applies mutatis mutandis to the corresponding limitations of method claim 27 while noting that the rejection above cites to both device and method disclosures.
The rejection of device claim 18 above applies mutatis mutandis to the corresponding limitations of method claim 28 while noting that the rejection above cites to both device and method disclosures.
The rejection of device claim 19 above applies mutatis mutandis to the corresponding limitations of method claim 29 while noting that the rejection above cites to both device and method disclosures.
The rejection of device claim 14 above applies mutatis mutandis to the corresponding limitations of manufacture claim 34 while noting that the rejection above cites to both device and method disclosures.
For the manufacture limitations of claim 34 see Liang’s teaching on:
A non-transitory computer-readable medium storing thereon sequences of computer-executable instructions for unsupervised estimation of motion of objects through a 3D space, the sequences of computer-executable instructions including instructions that instruct at least one processor to (Claim 19: A computer product comprising a non-transitory computer-readable medium having stored thereon program code that, when executed by a computing device, causes the acts of:)…
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.
Claims 21, 22, 31, and 32 are rejected under 35 U.S.C. 103 as being unpatentable over Liang et al (U.S. Patent Publication No. 2023/0342954 A1, hereinafter “Liang”) in view of Park et al (U.S. Patent Publication No. 2025/0014196 A1, hereinafter “Park”).
Regarding claim 21, Liang teaches the system of claim 14.
Additionally, Park teaches wherein the controller determines the flow vectors in a bi-directional manner (¶ 0049: The bi-directional feature detector 130 may be defined as a bi-directional flow embedding structure among four hierarchical structures of the bi-point flow net.; ¶ 0104: In the second detection step S730, the bi-directional feature detector 130 may detect a bi-directional scene flow feature for each step of the up sampled source point (SP) and target point (TP).).
Park is considered to be analogous art as it pertains to point cloud flow estimation. Therefore, it would have been obvious to one of ordinary skill in the art to combine the method for estimating an ego motion of a vehicle (as taught by Liang) and the scene flow estimation apparatus (as taught by Park) before the effective filing date of the claimed invention. The motivation for this combination of references would be Park applies an initial flow inference scheme using a voting function which reduces the number of computational parameters and enhance the flow prediction accuracy (See ¶ 0008).
This motivation for the combination of Liang and Park is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Regarding claim 22, Liang teaches the system of claim 17.
Additionally, Park teaches wherein the controller determines the objective function in a bi-directional manner (¶ 0084: The bi-directional feature detector 130 introduces SoftMax in a bi-directional flow propagate (BFP) block of the bi-directional flow embedding (BFE) structure to infer a voting value of group points of the source point SP and the target point TP, and adds the group points by using the voting value as a weight to extract an initial movement location.; ¶ 0086: The bi-directional feature detector 130 detects the initial scene flow, and then inputs the initial scene flow into the flow predictor FP, and corrects the initial scene flow through a feature of a bi-directional scene flow to finally detect a precise scene flow.; Examiner’s note: It is not explicitly defined in the claim what it means to determine the objective function in a bi-directional manner. As such, under broadest reasonable interpretation, the examiner is interpreting this claim to plainly mean that the determination of the objective function relies on bi-directional information. The examiner believes this claim should be amended to explicitly define what “determines the objective function in a bi-directional manner” means).
Park is considered to be analogous art as it pertains to point cloud flow estimation. Therefore, it would have been obvious to one of ordinary skill in the art to combine the method for estimating an ego motion of a vehicle (as taught by Liang) and the scene flow estimation apparatus (as taught by Park) before the effective filing date of the claimed invention. The motivation for this combination of references would be Park applies an initial flow inference scheme using a voting function which reduces the number of computational parameters and enhance the flow prediction accuracy (See ¶ 0008).
The rejection of device claim 21 above applies mutatis mutandis to the corresponding limitations of method claim 31 while noting that the rejection above cites to both device and method disclosures.
The rejection of device claim 22 above applies mutatis mutandis to the corresponding limitations of method claim 32 while noting that the rejection above cites to both device and method disclosures.
Claims 23 and 33 are rejected under 35 U.S.C. 103 as being unpatentable over Liang et al (U.S. Patent Publication No. 2023/0342954 A1, hereinafter “Liang”) in view of Wang et al (Wang, Yun, Cheng Chi, and Xin Yang. "Exploiting implicit rigidity constraints via weight-sharing aggregation for scene flow estimation from point clouds." arXiv preprint arXiv:2303.02454 (2023), hereinafter “Wang”).
Regarding claim 23, Liang teaches the system of claim 17.
Liang does not explicitly teach wherein the objective function incorporates a rigidity constraint.
However, Wang does teach wherein the objective function incorporates a rigidity constraint (Figure 1 and 2; Page 2, Col. 1, Section 3.2: In scene flow estimation task, due to the sparsity of point cloud data, the point clouds from two consecutive frames are not one-to-one correspondence. This results in matching errors and causes the warped point clouds to undergo structural deformation. To alleviate the problem of mismatch, we a weight-sharing aggregation model that utilizing implicitly rigidity constraints during the upsampling process.; Page 3, Col. 2, ¶ 2: In this paper, we design a weight-sharing aggregation module which implicitly enforces rigidity constraints by considering the relationship between coordinates, features, and scene flow).
Wang is considered to be analogous art as it pertains to point cloud flow estimation. Therefore, it would have been obvious to one of ordinary skill in the art to combine the method for estimating an ego motion of a vehicle (as taught by Liang) and the weight-sharing aggregation for scene flow estimation from point clouds (as taught by Wang) before the effective filing date of the claimed invention. The motivation for this combination of references would be Wang utilizes weight-sharing aggregation modules which use implicit rigidity constraints which reduces matching errors and prevent structural deformation of the warped point clouds (See section 3.2).
This motivation for the combination of Liang and Wang is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
The rejection of device claim 23 above applies mutatis mutandis to the corresponding limitations of method claim 33 while noting that the rejection above cites to both device and method disclosures.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Longman et al (U.S. Patent Publication No. 2023/0260078 A1) teaches a spatial monitoring system that employs a partial dimension iterative closest point analysis to provide improved accuracy for point cloud registration.
Agarwal et al (U.S. Patent No. 8886387 B1) teaches a computing device that may determine a reference point on the object in the first 3D point cloud, and receive a second 3D point cloud depicting a second view of the object. The computing device may determine a transformation between the first view and the second view, and estimate a projection of the reference point from the first view relative to the second view based on the transformation so as to trace the reference point from the first view to the second view. The computing device may determine one or more motion characteristics of the object based on the projection of the reference point.
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/ANDREW B. JONES/Examiner, Art Unit 2667
Prosecution Insights