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 .
Notice to Applicant
Limitations appearing inside of {} are intended to indicate the limitations not taught by said prior art(s)/combinations.
The declaration of Lintong Zhang under 37 CFR §1.130, filed on 06/12/2026, has been entered.
Claims 1-20 are pending in this application.
Response to Amendment
The Amendment filled 06/12/2026 in response to Non-Final Office Action mailed 03/12/2026 has been entered. Claim 20 has been amended. Rejection under 35 USC §101 has been withdrawn in light of amended claims. Rejections under 35 USC §§102 and 103 are withdrawn in light of said declaration filed 06/12/2026.
Response to Arguments
Remarks Item 1. Applicant's arguments, see Remarks pages 6-8, filed 06/12/2026, with respect to the claim interpretations under 35 USC §112(f) have been fully considered but they are not persuasive. Applicant asserts that the claim interpretation invoked in the Non-Final office action of 03/12/2026 (the recited limitations in claim 17: input module, segmentation module, descriptor module, matching module), appears to be based merely on a conclusory statement and that said office action fails to show that the term is a nonce world or a verbal construct that is not recognized as the name of structure (see Remarks, filed 06/12/2026, page 7). Applicant further submits that an “input module” is known as an electronic hardware interface”, and, for example, the “segmentation module” and “descriptor module” are each described in the present application in the context of a neural network.
However, the courts have found the specific term “module” to be a nonce term. Per the MPEP, the following is a list of non-structural generic placeholders that may invoke 35 U.S.C. 112(f): "mechanism for," "module for," "device for," "unit for," "component for," "element for," "member for," "apparatus for," "machine for," or "system for." Welker Bearing Co., v. PHD, Inc., 550 F.3d 1090, 1096, 89 USPQ2d 1289, 1293-94 (Fed. Cir. 2008); Mass. Inst. of Tech. v. Abacus Software, 462 F.3d 1344, 1354, 80 USPQ2d 1225, 1228 (Fed. Cir. 2006); Personalized Media, 161 F.3d at 704, 48 USPQ2d at 1886–87; Mas-Hamilton Group v. LaGard, Inc., 156 F.3d 1206, 1214-1215, 48 USPQ2d 1010, 1017 (Fed. Cir. 1998); MPEP §2181(I)(A).
While “input module” may be understood as an electronic hardware interface, it may also be interpreted as a software interface according to the courts. Specifically, with respect to the term “module”, the courts have found this term to be a generic description for software or hardware that performs a specific function (Williamson v. Citrix, 792 F.3d 1350: 13-1130.opinion.11-3-2014.1.pdf).
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Applicant has not recited structure within the claim nor provided evidence of structure for these terms within the remarks, therefore examiner respectfully disagrees. If applicant wishes to avoid invocation of the said claim interpretation, Applicant is encouraged to recite the structure for each limitation within the claim. Accordingly, the claim interpretation of 03/12/2026 has been maintained.
Remarks Item 2. Applicant’s arguments, see Remarks - page 8, filed 06/12/2026, with respect to 35 USC §101 have been fully considered and are persuasive. The rejection of 03/12/2026 has been withdrawn.
Remarks Item 3. Examiner respectfully agrees with applicant’s arguments, see Remarks - page 8, filed 06/12/2026, with respect to 35 USC §102(a)(1), and thanks applicant for providing the declaration that Zhang is a co-inventor. Applicant’s arguments with respect to the rejections of claims 1-20 under 35 USC §102 has been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Remarks Item 4. Applicant’s arguments, see Remarks, page 9, filed 06/12/2026, with respect to the rejection of claim 20 under 35 USC §103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Liu in view of Ali.
All arguments/remarks have been addressed.
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.
Claims1-7, and 9-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, in view “Liu” (Liu et al. HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation With a Wearable Solid-State LiDAR Sensor. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, October 2021, pp. 1780-1790) in view of “Ali” (Ali et al., US 20230126333 A1).
1. Liu teaches a computer-implemented method of localisation of an imaging apparatus in an environment, the method comprising:
receiving a point cloud map indicative of the environment, the point cloud map captured by the imaging apparatus located at a position within the environment (Liu, [§3, p 1782, Col 1 ¶1]; a lightweight solid-state LiDAR sensor is attached to a belt for collecting point clouds);
segmenting the point cloud map into a plurality of object segments each comprising an object feature (Liu, [§3.2, p1783, Col 1, ¶3]; One decoder extracts semantic information);
assigning a unique descriptor to each of the object features (Liu, [§3.2, p1783, col 1, ¶2]; The extracted feature will be decoded into 2 branches: semantic branch and offset branch. The semantic branch builds the clusters that have the same semantic labels (i.e., unique descriptor).);
{matching at least one of the object features to a corresponding feature in an existing map of the environment using the unique descriptor} and localising the position of the imaging apparatus in the environment {based on the at least one matched object feature} (Liu, [§3.1, p 3,Col 1-2, ¶1]; the system also needs to obtain the user’s position (the user is wearing the LIDAR on their belt so the position of the user is also the position of the imaging apparatus) in the point cloud map. These can be achieved through Simultaneous Localization and Mapping)).
Liu does not explicitly disclose matching at least one of the object features to a corresponding feature in an existing map of the environment using the unique descriptor. Liu teaches localizing camera position, but does not explicitly disclose based on the at least one matched object feature.
However, Ali, a similar field of endeavor of pose estimation using point cloud data for navigating an environment, teaches matching at least one of the object features to a corresponding feature in an existing map of the environment using the unique descriptor (Ali, ¶[0051]; the pose graph loop-closure sub-module 66 determines the two-dimensional feature descriptors of the hyper-local submaps 84 corresponding to the latest filtered data point cloud scan 50, and finds matching two-dimensional feature descriptors stored within the one or more spatial databases 68); and localizing the position of the imaging apparatus in the environment based on at least one matched object feature (Ali, ¶[0054]; In block 204, the ICP scan matching sub-module 60 of the scan matching and radar pose estimator module 46 determines the initial estimated pose 80 by aligning the latest aggregated filtered data point cloud scan 50 with the most recent hyper-local submap 84 based on the ICP alignment algorithm. Specifically, the ICP scan matching sub-module 60 determines the predicted pose as outlined in sub-blocks 204A-204C.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include matching features to those in a database as taught by Ali to the invention of Liu. The motivation to do so would be to reduce computational complexity thereby improving lag time as well as accuracy of pose estimation.
2. The combination of Liu and Ali teaches the method of claim 1. Ali further teaches comprising matching a plurality of the object features to respective corresponding features in the existing map of the environment using the unique descriptors of the plurality of the object features; and localising the position of the imaging apparatus in the environment based on the plurality of matched object features. (Ali, ¶[0051]; the pose graph loop-closure sub-module 66 determines the two-dimensional feature descriptors of the hyper-local submaps 84 corresponding to the latest filtered data point cloud scan 50, and finds matching two-dimensional feature descriptors stored within the one or more spatial databases 68. Ali, ¶[0054]; In block 204, the ICP scan matching sub-module 60 of the scan matching and radar pose estimator module 46 determines the initial estimated pose 80 by aligning the latest aggregated filtered data point cloud scan 50 with the most recent hyper-local submap 84 based on the ICP alignment algorithm. Specifically, the ICP scan matching sub-module 60 determines the predicted pose as outlined in sub-blocks 204A-204C.).
3. The combination of Liu and Ali teaches the method of claim 2. Liu further teaches wherein each object feature belongs to an object category indicating the type of object, and at least two of the plurality of the object features belong to different object categories (See Liu, Table 1, shown below, exhibits features belonging to different object categories;
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4. The combination of Liu and Ali teaches the method of claim 1. Liu further teaches wherein the point cloud map represents an image of the environment captured by the imaging apparatus in a single position and a single orientation within the environment (Liu, [p 3, Col 1, §2,¶3]; 3D point cloud segmentation results and aligns them onto 2D top-view representations; and See Fig 2, shown below, exhibits top view:
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5. The combination of Liu and Ali teaches the method of claim 1. Liu further teaches wherein segmenting the point cloud map comprises representing the point cloud map using sparse tensors (Liu, [§3.2, p1783, col 1, ¶2]; PointGroup uses a 7-layer sparse convolution U-Net [46] to extract features; the features are extracted from the point cloud map using sparse convolution U-Net which is used for processing sparse tensors, therefore it is understood that the point cloud map is represented as sparse tensors).
6. The combination of Liu and Ali teaches the method of claim 1. Liu further teaches wherein segmenting the point cloud map comprises, for each point in the point cloud map:
predicting a semantic label indicating an object class of the object feature comprising the point (Liu, [p 4, Col 1, § 3.2, ¶2]; The semantic branch builds the clusters that have the same semantic labels); and
predicting an instance label indicating a unique instance of the object feature comprising the point (Liu, [p 4, Col 1, § 3.2, ¶2]; The offset branch predicts per-point offset vectors to shift each point towards the instance centroid and builds shifted point clusters that belong to the same instances).
7. The combination of Liu and Ali teaches the method of claim 1. Liu further teaches wherein segmenting the point cloud map (Liu, [§3.2, p1783, col 1, ¶1]; 3D instance segmentation architecture) comprises grouping a plurality of points in the point cloud map, the plurality of points associated with an object feature (Liu, [§3.2, p1783, col 1, ¶1]; The semantic branch builds the clusters that have the same semantic labels. The offset branch predicts per-point offset vectors to shift each point towards the instance centroid and builds shifted point clusters that belong to the same instances. Those cluster proposals will be voxelized and then scored.).
9. The combination of Liu and Ali teaches the method of claim 1. Liu further teaches wherein segmenting the point cloud map is performed using a neural network (See Liu, Fig 3, shown below, exhibits a neural network for point cloud segmentation using a dual-decoder U-Net:
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10. The combination of Liu and Ali teaches the method of claim 1. Liu further teaches wherein assigning the unique descriptor comprises:
for each of the object features, each of the object features comprising a plurality of points of the point cloud map (See Liu, Fig 2, provided with claim 4, exhibits object features comprising a plurality of points of the point cloud map, i.e., chairs, bed, sofa, etc.);
using a network comprising a series of convolutional layers to generate a plurality of point descriptors each corresponding to a particular point in the plurality of points of the object feature (See Liu, Fig 4, shown below exhibits a series of convolutional layers generating offset and semantic features used to generation point descriptors for objects,
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pooling the plurality of point descriptors to generate the unique descriptor of the object feature (Liu, [§3.2, p1783, col 1, ¶2]; The offset branch predicts per-point offset vectors to shift each point towards the instance centroid and builds shifted point clusters that belong to the same instances.)
11. The combination of Liu and Ali teaches the method of claim 1. Liu further teaches wherein assigning the unique descriptor is performed using a neural network (See Liu, Fig 3, shown above, exhibits instance prediction).
12. The combination of Liu and Ali teaches the method of claim 1. Ali further teaches wherein matching at least one of the object features to a corresponding feature in an existing map of the environment using the unique descriptor comprises:
matching one or more object features in the point cloud map to a corresponding feature in the existing map; and
identifying a closest match to the corresponding feature from the one or more matched object features using a correspondence grouping method (Ali, ¶[0051]; the pose graph loop-closure sub-module 66 determines the two-dimensional feature descriptors of the hyper-local submaps 84 corresponding to the latest filtered data point cloud scan 50, and finds matching two-dimensional feature descriptors stored within the one or more spatial databases 68. Ali, ¶[0054]; In block 204, the ICP scan matching sub-module 60 of the scan matching and radar pose estimator module 46 determines the initial estimated pose 80 by aligning the latest aggregated filtered data point cloud scan 50 with the most recent hyper-local submap 84 based on the ICP alignment algorithm. Specifically, the ICP scan matching sub-module 60 determines the predicted pose as outlined in sub-blocks 204A-204C.)..
13. The combination of Liu and Ali teaches the method of claim 1. Liu further teaches wherein the plurality of object segments comprises a first object feature in a first object segment and a second object feature in a second object segment, and wherein the first object feature comprises a different number of points of the point cloud map than the second object feature (See Liu, Fig 2 (Right), exhibits different object features (e.g., table and chairs etc.) each comprising a different number of points of the point cloud
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14. The combination of Liu and Ali teaches he method of claim 1. Liu further teaches wherein the environment is an indoor environment (Liu, [p2, Col 1, §1, ¶3]; We propose HIDA, a wearable system with a solidstate LiDAR sensor, which helps the visually impaired to obtain a holistic understanding of indoor surroundings with object detection and obstacle avoidance; Fig. 6 is a functional example in an indoor scene).
15. The combination of Liu and Ali teaches the method of claim 1. Liu further teaches wherein the imaging apparatus is a lidar and the point cloud map captured by the imaging apparatus is a lidar map (Liu, [p 3, Col 1, §3, ¶1]; high-resolution LiDAR depth camera; and see Fig 2, shown above, exhibits a LiDAR map).
16. The combination of Liu and Ali teaches the method of claim 1. Liu further teaches wherein localising the position of the imaging apparatus comprises identifying the spatial position and directional orientation of the imaging apparatus (See Liu, Fig 5, shown below, exhibits spatial position and directional orientation
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Claim 17 is similarly analyzed as analogous claim 1.
18. The combination of Liu and Ali teaches the apparatus of claim 17. Liu further teaches wherein one or more of: the segmentation module comprises a neural network configured to segment the point cloud map into the plurality of object segments; or the descriptor module comprises a neural network configured to assign the unique descriptor to each of the object features (See Liu, Fig 4, shown below, exhibits neural network for segmenting the image into a plurality of segments:
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19. The combination of Liu and Ali teaches the apparatus of claim 17,
wherein:
the apparatus comprises the imaging apparatus located with the apparatus (Liu, [p 3, Col 1, §3, ¶1]; a lightweight solid-state LiDAR sensor is attached to a belt for collecting point clouds. The RealSense L515, as the world’s smallest high-resolution LiDAR depth camera, is well suitable as part of wearable devices.);
the imaging apparatus is configured to capture the point cloud map and provide the point cloud map to the input module (Liu, [p 3, Col 1, §3, ¶1]; A laptop placed in a backpack is the second component of our system. The laptop with a GPU processor ensures that the instance segmentation can be performed in an online manner); and
the localisation module is configured to localise the position of the apparatus (Liu, [p3, Col 1, §3.1, ¶1]; The users will collect independently the point cloud under the audio guidance. At the same time, the system also needs to obtain the user’s position in the point cloud map.).
20. The combination of Liu and Ali teaches a machine-readable medium having program code stored thereon which, when executed by a computer, causes the computer to perform the method of claim 1 (Liu, [p 3, Col 1, §3, ¶1]; A laptop placed in a backpack is the second component of our system. The laptop with a GPU processor).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Liu in view of Ali, and further in view of “Lu” (Lu, Guangman, et al. "A lightweight real-time 3D LiDAR SLAM for autonomous vehicles in large-scale urban environment." IEEE Access 11 (2023): 12594-12606.).
8. The combination of Liu and Ali teaches the method of claim 7. Liu further teaches wherein the point cloud map is obtained using depth imaging (Liu, [p 3, Col 1, §3, ¶1]; high-resolution LiDAR depth camera), and
{grouping the plurality of points is performed using an adaptive radius threshold proportionate to a vertical distance between two depth imaging beams used to capture the point cloud map}.
Liu teaches “cluster voxel size as 0:02m, and cluster radius as 0:03m”;( [§4.4.1, p 1784, col 1, ¶1]). Liu does not explicitly disclose using an adaptive radius threshold proportionate to a vertical distance between two depth imaging beams used to capture the point cloud map.
Ali teaches grouping the plurality of points is performed using an adaptive radius threshold {proportionate to a vertical distance between two depth imaging beams used to capture the point cloud map} (Ali, ¶[0042]; The ICP scan matching sub-module 60 then adjusts a maximum distance threshold parameter of an ICP alignment algorithm to determine correspondences between the aggregated filtered data point cloud scans 50 and the point located on the most recent hyper-local submap 84).
The adaptive radius threshold of Ali is based on the maximum distance threshold is adjusted based on both the linear and angular velocity of the autonomous vehicle (¶[0042]). Ali does not explicitly teach an adaptive radius that is proportionate to a vertical distance between two depth imaging beams used to capture the point cloud map.
However, Lu, a similar field of endeavor of real-time localization and mapping, teaches adaptive thresholding proportionate to a vertical distance between two depth imaging beams used to capture the point cloud map (Lu, [§IV. D., p12598, col 1, ¶1-2]; we propose an adaptive selection method of feature points based on distance. … the index Did of each point cloud is obtained in the encoding stage. The threshold χ of feature point selection is adaptively calculated according to the distance intervals as:
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include adaptive thresholding based on distance between lidar beams as taught by Lu to the combined invention of Liu and Ali. The motivation to do so would be to account for the sparse point clouds at long distances and dense at short distances when estimating poses.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892 Notice of References Cited for full citations.
Ou, Wenyan et al. (2022) teaches indoor navigation for visually impaired people, and would have been relied upon for teaching feature descriptors matching in ego-motion estimation.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHANDHANA PEDAPATI whose telephone number is (571)272-5325. The examiner can normally be reached M-F 8:30am-6pm (ET).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chan Park can be reached at 5712727409. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHANDHANA PEDAPATI/Examiner, Art Unit 2669 /CHAN S PARK/Supervisory Patent Examiner, Art Unit 2669
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