CTNF 18/331,351 CTNF 99996 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 Under 37 CFR 1.114 07-42-04 AIA 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/30/2026 has been entered. Response to Amendment This action is in response to remarks filed on 01/30/2026. Claims 16-29 are considered in this office action. Claims 16-29 are pending examination. Applicant's amendment necessitated new grounds of rejection therefore, claims 16-29 are rejected. This action is non-final. Response to Arguments Applicant presents the following arguments regarding the previous office action: Kishin does not perform object detection per recording time window to generate object level datasets. Kishin does not combine multiple object detections obtained since a preceding SLAM dataset or keyframe into a single real object. Applicant’s arguments A and B, appear to be directed solely to instantly added subject matter, and are addressed within the rejection below. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim s 16-29 are all rejected under 35 U.S.C. 103 as being unpatentable over Kishin et al (WO2021125697A1) in view of Iqbal et al. (Localization of Classified Objects in SLAM using Nonparametric Statistics and Clustering) . Regarding claim 16, Kishin discloses, a method for allocating objects in an environment using SLAM and a mobile device in the environment (Kishin, Description, Paragraph 14, Lines 1-11, embodiments of the present disclosure provide systems and methods for an electronic device to generate a map that includes boundaries and obstacles in the environment as the electronic device travels. The electronic device can also localize itself within the map. In certain embodiments, the electronic device performs SLAM to generate the map and localize itself within an environment ), which has at least one sensor for acquiring information about the environment and/or about objects in the environment and/or about the mobile device, the method comprising the following steps: providing sensor data, which include information about the environment and/or about objects in the environment and/or the mobile device, which are acquired or were acquired using the at least one sensor (Kishin, Description, Paragraph 25, Lines 1-14, the electronic device 100 further includes one or more sensors 165 that can meter a physical quantity to identify the location of the electronic device as well as nearby obstacles of the electronic device 100 and convert metered or detected information into an electrical signal ); performing an object detection based on the sensor data for a recording time window in each case, to obtain first object datasets for detected objects (Kishin, Description, Paragraph 87, Lines 2-6, the electronic device 300 starts with the first scan of a frame. in step 522, the electronic device 300 identifies the grid point (such as a particular cell of the grid) that intersect with the scan based on the pose of the electronic device 300, the direction of the scan, and the range measurements, based on one or more of the sensors ) … (Kishin, Description, Paragraph 62, Lines 3-7, over a period of time the radar filter, of FIGURE 4a, stores and updates a list of the history radar scan points in a fixed global coordinate frame (step 441). It is noted that a radar scan point is defined as a collection of target detection results from the radar sensor ); and performing object tracking for a new SLAM dataset, which is to be added to a SLAM graph (Kishin, Description, Paragraph 61, Lines 1-11, the SLAM layer 420 performs the localization and generates (or builds) a fusion map to be used by the navigation layer 430. The SLAM layer 420 performs the task of Lidar-based SLAM with radar aid. The SLAM layer 420 includes Lidar SLAM 422, a radar occupancy mapping engine 424, and a map fusion engine 426. The Lidar SLAM 422 uses the Lidar scan from the sensing layer 410 to generate a Lidar map and a Lidar pose for localization of the electronic device 300. The radar occupancy mapping engine 424 generates a radar map using the Lidar pose and the filtered radar scan from the Radar filter 416 of the sensing layer 410. radar occupancy mapping engine 424 is described in in greater detail below in FIGURE 4f. The radar map and the Lidar map 478 are then combined into a fusion map 479, via the map fusion engine ), including: allocating objects detected since a preceding SLAM dataset using the object detection, to real objects based on the first object datasets to obtain second object datasets for real objects to be considered in the SLAM graph (Kishin, Abstract, Lines 4-9, while the electronic device travels the area, the processor is configured to generate a first map indicating one or more objects within an area based on the Lidar scans and a second map based on the radar signals. The processor is configured to determine whether the second map indicates a missed object at the portion of the first map that is unoccupied. In response to a determination that the second map indicates the missed object, the processor is configured to modify the first map with information on the missed object ) … (Kishin, Description, Paragraph 160, Lines 2-10, electronic device 300 can maintain a list of historical radar scan points in a fixed global frame. The electronic device 300 then identifies a pose of the electronic device within the area based on the Lidar scan. The electronic device 300 can modify a point in the list of historical radar points from the fixed global coordinate frame to an electronic device frame, based on the pose of the electronic device 300. The pose of the electronic device 300 can include the orientation, and location of the electronic device 300 within the area. The electronic device 300 can also determine whether to discard a point based on a set of criteria in order to reduce noise and false alarms ). wherein the first object datasets are transformed from a sensor coordinate system to a reference coordinate system (67, the densified radar points are transferred from the sensor coordinate frame to the global coordinate frame ), associated with a preceding SLAM dataset prior to the allocation of detected objects to real objects (70, both in the global coordinate frame, a data association step is taken to assign the scan points to the radar history points (step 457) ). However, Kishin does not explicitly disclose, the allocation clusters multiple detected objects, detected in different recording time windows since the preceding SLAM dataset, into a single real object , in the exact manner the applicant claims. Nevertheless, Iqbal who is in the same field of endeavor of the localization of classified objects in SLAM discloses, the allocation clusters multiple detected objects (E. Intermittent Clustering with NPDA, we query these new observations to fit any existing clusters. If these associated objects belong to existing clusters, then no new objects have been observed during this period ), detected in different recording time windows since the preceding SLAM dataset (C. Object Back-Projection using SLAM Pose, each detected object k in it is back-projected to W using dtk, estimated camera pose at t and intrinsic parameters. If there is an object with successful association using NPDA for a time period t−n to t, then all the back-projected points from pWt−nk to pWtk are combined in a joint distribution to represent a single feature vector ), into a single real object (IV. EXPERIMENTALRESULTS, Multiple objects are generally detected in an environment during a sequence, but an object is associated and then Clustered only if it is observed for a certain number of frames ). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kishin’s disclosure to incorporate the teachings of Iqbal. Both Kishin and Iqbal improve SLAM based mapping by using detected objects over time to produce a more reliable map for localization and navigation. Kishin teaches transforming incoming sensor detections from a sensor coordinate frame into a global coordinate frame before performing data association with radar history points. Iqbal teaches associating detected objects across frames and clustering the associated observations do they represent unique objects in the SLAM map. A person of ordinary skill in the art would be motivated to apply Iqbal’s object level association and clustering technique to Kishin’s SLAM mapping system to merge multiple detections observed over different time windows into a single real object. This would reduce duplicate object entries while also improving the accuracy of the SLAM map used for navigation. Further justification for combining Kishin with Iqbal not only comes from the state of the art but from Iqbal (V. CONCLUSIONS, our method can easily incorporate any SLAM system and add semantics to the environment using any deep neural network based object detector ). Regarding claim 17, Kishin and Iqbal disclose the method as recited in claim 16 as discussed supra. Additionally Kishin discloses, the performing of the object tracking furthermore includes:(i) allocating the real objects to be considered in the SLAM graph based on the second object datasets to real objects already included in the SLAM graph and/or the preceding SLAM dataset, and updating object data for the included real objects with the second object datasets (Kishin, Description, Paragraph 61, Lines 1-11, the SLAM layer 420 performs the localization and generates (or builds) a fusion map to be used by the navigation layer 430. The SLAM layer 420 performs the task of Lidar-based SLAM with radar aid. The SLAM layer 420 includes Lidar SLAM 422, a radar occupancy mapping engine 424, and a map fusion engine 426. The Lidar SLAM 422 uses the Lidar scan from the sensing layer 410 to generate a Lidar map and a Lidar pose for localization of the electronic device 300. The radar occupancy mapping engine 424 generates a radar map using the Lidar pose and the filtered radar scan from the Radar filter 416 of the sensing layer 410. radar occupancy mapping engine 424 is described in in greater detail below in FIGURE 4f. The radar map and the Lidar map 478 are then combined into a fusion map 479, via the map fusion engine ), and/or (ii) setting up new object data for real objects in the new SLAM dataset when real objects to be considered are unable to be allocated to any of the real objects already included in the SLAM graph and/or the preceding SLAM dataset (Kishin, Description, Paragraph 62, Lines 3-7, over a period of time the radar filter, of FIGURE 4a, stores and updates a list of the history radar scan points in a fixed global coordinate frame (step 441). It is noted that a radar scan point is defined as a collection of target detection results from the radar sensor ) … (Kishin, Description, Paragraph 180, Lines 8-14, the electronic device 300 can also determine whether to discard a point based on a set of criteria in order to reduce noise and false alarms. In step 706, the electronic device 300 generate a first map and a second map while traveling an area. The first map can indicate locations of objects as detected by the Lidar sensor while the second map indicates objects as detected by the received radar reflections ); wherein the new SLAM dataset is provided and added to the SLAM graph, and based on the SLAM graph (Kishin, Description, Paragraph 3, Lines 5-10, in response to a determination that a portion of the first map is unoccupied, the processor configured to determine whether the second map indicates a missed object at the portion of the first map that is unoccupied. In response to a determination that the second map indicates the missed object, the processor configured to modify the first map with information on the missed object ), navigation information being also provided for the mobile device, which includes object data for real objects in the environment, and a geometrical map of the environment and/or a trajectory of the mobile device in the environment (Kishin, Description, Paragraph 65, Lines 1-12, the navigation layer 430 performs the navigation using the sensor data from the sensing layer 410 and the fusion map generated by the SLAM layer 420. The navigation layer 430 includes a localization engine 432 and a planning/navigation engine 434. The localization engine 432 localizes the electronic device 300 in a map generated by the SLAM layer by matching the Lidar scan data with the obstacles in the map. The planning/navigation engine 434 receives a navigation goal, the map in which the electronic device 300 is operating (generated by the SLAM layer 420), and the pose of the electronic device 300 in the map (from the localization engine 432). The planning/navigation engine 434 identifies a path to accomplish navigational goal while avoiding obstacles that are already presented in the map and/or detected by the sensors in real-time as the electronic device travels the path ). Regarding claim 18, Kishin and Iqbal disclose the method as recited in claim 16 as discussed supra. Additionally Kishin discloses, determining the second object datasets for each real object to be considered based on the first object datasets of the detected objects that are allocated to the real object, using average values of values of the first object datasets of the detected objects that are allocated to the real object (Kishin, Description, Paragraph 165, Lines 4-12, the electronic device 300 can also identify a single range value of the set of points based on an average of the range values of the set of points. The electronic device 300 can also determine that the single range value is closer to the electronic device than one of the one or more objects detected by the Lidar scans along the azimuth angle. Based on the determination that the single range value is closer to the electronic device than one of the one or more objects detected by the Lidar scans, the electronic device can merge the single range value with the one or more objects detected by the Lidar scans to generate a fused scan ). Regarding claim 19, Kishin and Iqbal disclose the method as recited in claim 16 as discussed supra. Additionally Kishin discloses, determining an uncertainty of values in the second object datasets, each based on the first object datasets of the detected objects which are allocated to the real object pertaining to the respective second object dataset (Kishin, Description, Paragraph 127, Lines 1-3, the probability of a grid cell being occupied is modeled based on a function of the occupied space and a function of the empty space ). Regarding claim 20, Kishin and Iqbal disclose the method as recited in claim 16 as discussed supra. Additionally Kishin discloses, determining, according to a consideration criterion, the real objects to be taken into consideration in the SLAM graph from the real objects, the consideration criterion including that more than a predefined number of detected objects is allocated to a real object (Kishin, Description, Paragraph 65, Lines 3-10, the transformed copy of the point is used to determine whether the point needs to be discarded based on multiple criteria (step 445). For example, if the distance between the point and the electronic device 300 exceeds a maximum distance threshold, the point will be discarded. For another example, if the point has not been validated by any new measurements while it is inside the radar FOV for a given time period, the point will be discarded. Discarding certain points based on the criteria removes noise and false alarm points from the point list, and it also improves the decreases the memory size ). Regarding claim 21, Kishin and Iqbal disclose the method as recited in claim 16 as discussed supra. Additionally Kishin discloses, the allocation of the objects detected since the preceding SLAM dataset using the object detection to real objects is carried out using an algorithm in which the detected objects are sorted according to an allocation criterion (Kishin, Description, Paragraph 112, Lines 2-7, in certain embodiments, the selected grid point is the first cell that was scanned by the electronic device. In step 527b, the electronic device 300 determines whether the expression #visits for that grid point (cell) is great than zero. If the number of visits is equal to or less than zero, then in step 528 that grid point is labeled as unknown since that grid point (cell)was never included in a scan by the electronic device ), in which a distance measure is determined between two detected objects in each case, and in which two detected objects are allocated to the same real object for which the distance measure undershoots a predefined distance threshold value (Kishin, Description, Paragraph 73, Lines 1-4, in certain embodiments, the data association is performed by first calculating an association score for each pair of scan point and radar history point. The association score describes how closely the two points are associated. For example, the closer the two points, the higher the association score and vice versa ) … (Kishin, Description, Paragraph 74, Lines 5-8, If an incoming scan point is associated with at least one radar history points, the data step 457 picks the association which results in the highest association score ). Regarding claim 22, Kishin and Iqbal disclose the method as recited in claim 16 as discussed supra. Additionally Kishin discloses, synchronizing and/or preprocessing the object and/or environment information, the sensor data including information acquired using different types of sensors (Kishin, Description, Paragraph 61, Lines 15-19, the electronic device 300 performs real-time obstacle detection with the fused scan data from the sensor fusion engine 418. In certain embodiments, the navigation task of the planning/navigation engine 434 terminates when either the electronic device 300 arrives to its goal or when there is no available path for the electronic device 300 to reach its goal ), and execution of the object detection taking place based on the synchronized and/or preprocessed sensor data for the recording time window in each case (Kishin, Description, Paragraph 75, Lines 1-7, since the two sensors (Lidar sensor 312 and radar sensor 314) could publish sensor data at different rates, the sensor fusion engine 418 buffers the data from the higher-rate sensor while waiting for the data from the lower-rate sensor. As described in the flowchart 410c the radar sensor 314 higher-rate sensor and the Lidar sensor is the lower-rate sensor. In certain embodiments, if the Lidar sensor is the higher-rate sensor, then the Lidar scans are stored in the buffer while waiting for the data from the radar sensor ). Regarding claim 23 Kishin and Iqbal disclose the method as recited in claim 16 as discussed supra. Additionally Kishin discloses, the first object datasets for the detected objects include values for spatial parameters (Kishin, Description, Paragraph 75, Lines 2-8, the electronic device 300 can identify a set of points, of the one or more points, that have azimuth angles within an angle threshold and range values within a range threshold. The electronic device 300 can also identify a single range value of the set of points based on an average of the range values of the set of points. The electronic device 300 can also determine that the single range value is closer to the electronic device than one of the one or more objects detected by the Lidar scans along the azimuth angle ), the spatial parameters including a position and/or an orientation and/or a dimension (Kishin, Description, Paragraph 40, Lines 2-8, the SLAM engine 330 generates a map of the area based on the received information from the sensors 310 (such as the Lidar sensor 312 and the radar sensor 314). The SLAM engine 330 also identifies the location of the electronic device 300 as its travels through an area. The SLAM engine 330 can also identify the pose of the electronic device 300. The pose can include both the heading and the location of the electronic device within the area. The pose can include the spatial world coordinates and heading direction ), and spatial uncertainties of the spatial parameters (Kishin, Description, Paragraph 112, Lines 2-8, the function of the occupied space, accounts for the uncertainties in range and azimuthal angles, as described in Math Figure (4). Similarly, the function of the empty space, accounts for the uncertainties in range and azimuthal angles, as described in Math Figure (7) . Regarding claim 24, Kishin and Iqbal disclose the method as recited in claim 16 as discussed supra. Additionally Kishin discloses, the first object datasets for the detected objects include information about a detection accuracy and/or a class allocation (Kishin, Description, Paragraph 93, Lines 1-6, embodiments of the present disclosure take into consideration that the accuracy of SLAM is based on the resolution and accuracy of the measurements obtained from the sensors 310 such as the Lidar sensor 312 and the radar sensor 314 of FIGURE 3a. For example, the radar sensor 314 can miss one or more objects during the mapping process due to a stronger reflection of a faraway object as compared to a near object ). Regarding claim 25, Kishin and Iqbal disclose the method as recited in claim 16 as discussed supra. Additionally Kishin discloses, the at least one sensor includes one or more of the following: a lidar sensor, a camera, an inertial sensor (Kishin, Description, Paragraph 45, Lines 4-9, the pose of the electronic device 300 is estimated based on the motion of the electronic device 300 obtained from one or more of the sensors 310, for example inertial measurement unit (IMU), wheel encoders, or vision sensors. Mapping is done based on range measurements obtained from a sensor like Lidar (such as the Lidar sensor 312), radar (such as the radar sensor 314), vision sensors (such as a camera), and the like ). Regarding claim 26, Kishin discloses, a system for data processing, the system configured to allocate objects in an environment using SLAM and a mobile device in the environment, which has at least one sensor for acquiring information about the environment and/or about objects in the environment and/or about the mobile device, the system configured to: provide sensor data, which include information about the environment and/or about objects in the environment and/or the mobile device, which are acquired or were acquired using the at least one sensor (Kishin, Description, Paragraph 25, Lines 1-14, the electronic device 100 further includes one or more sensors 165 that can meter a physical quantity to identify the location of the electronic device as well as nearby obstacles of the electronic device 100 and convert metered or detected information into an electrical signal ); perform an object detection based on the sensor data for a recording time window in each case (Kishin, Description, Paragraph 87, Lines 2-6, the electronic device 300 starts with the first scan of a frame. in step 522, the electronic device 300 identifies the grid point (such as a particular cell of the grid) that intersect with the scan based on the pose of the electronic device 300, the direction of the scan, and the range measurements, based on one or more of the sensors ) … (Kishin, Description, Paragraph 62, Lines 3-7, over a period of time the radar filter, of FIGURE 4a, stores and updates a list of the history radar scan points in a fixed global coordinate frame (step 441). It is noted that a radar scan point is defined as a collection of target detection results from the radar sensor ), to obtain first object datasets for detected objects; and perform object tracking for a new SLAM dataset, which is to be added to a SLAM graph, including: allocate objects detected since a preceding SLAM dataset using the object detection, to real objects based on the first object datasets to obtain second object datasets for real objects to be considered in the SLAM graph (Kishin, Abstract, Lines 4-9, while the electronic device travels the area, the processor is configured to generate a first map indicating one or more objects within an area based on the Lidar scans and a second map based on the radar signals. The processor is configured to determine whether the second map indicates a missed object at the portion of the first map that is unoccupied. In response to a determination that the second map indicates the missed object, the processor is configured to modify the first map with information on the missed object ) … (Kishin, Description, Paragraph 160, Lines 2-10, electronic device 300 can maintain a list of historical radar scan points in a fixed global frame. The electronic device 300 then identifies a pose of the electronic device within the area based on the Lidar scan. The electronic device 300 can modify a point in the list of historical radar points from the fixed global coordinate frame to an electronic device frame, based on the pose of the electronic device 300. The pose of the electronic device 300 can include the orientation, and location of the electronic device 300 within the area. The electronic device 300 can also determine whether to discard a point based on a set of criteria in order to reduce noise and false alarms ), wherein the first object datasets are transformed from a sensor coordinate system to a reference coordinate system (67, the densified radar points are transferred from the sensor coordinate frame to the global coordinate frame ), associated with a preceding SLAM dataset prior to the allocation of detected objects to real objects (70, both in the global coordinate frame, a data association step is taken to assign the scan points to the radar history points (step 457) ). Additionally, Iqbal discloses, the allocation clusters multiple detected objects (E. Intermittent Clustering with NPDA, we query these new observations to fit any existing clusters. If these associated objects belong to existing clusters, then no new objects have been observed during this period ), detected in different recording time windows since the preceding SLAM dataset (C. Object Back-Projection using SLAM Pose, each detected object k in it is back-projected to W using dtk, estimated camera pose at t and intrinsic parameters. If there is an object with successful association using NPDA for a time period t−n to t, then all the back-projected points from pWt−nk to pWtk are combined in a joint distribution to represent a single feature vector ), into a single real object (IV. EXPERIMENTALRESULTS, Multiple objects are generally detected in an environment during a sequence, but an object is associated and then Clustered only if it is observed for a certain number of frames ). Regarding claim 27, Kishin discloses, a mobile device, comprising: at least one sensor for acquiring object and/or environment information (Kishin, Description, Paragraph 25, Lines 1-14, the electronic device 100 further includes one or more sensors 165 that can meter a physical quantity to identify the location of the electronic device as well as nearby obstacles of the electronic device 100 and convert metered or detected information into an electrical signal ); a system for data processing (Kishin, Abstract, Lines 1-2, an electronic device includes a Lidar sensor, a radar sensor, and a processor. The processor is configured to identify one or more objects from Lidar scans ), the system configured to allocate objects in an environment using SLAM and the mobile device in the environment, (Kishin, Description, Paragraph 14, Lines 1-11, the electronic device can generate a map and identify its location within a map based on identifying its trajectory and pose as the electronic device traverses through an area without directions from a user. The environment or area can be a yard, lawn, a single room, a body of water, a residence, an office complex, a street, a warehouse, a school, or any other consumer or commercial space. The electronic device of the present disclosure can navigate an area and avoid colliding with obstacles or boundaries (such as walls). Therefore, embodiments of the present disclosure provide systems and methods for an electronic device to generate a map that includes boundaries and obstacles in the environment as the electronic device travels. The electronic device can also localize itself within the map. In certain embodiments, the electronic device performs SLAM to generate the map and localize itself within an environment ), the system configured to: provide sensor data, which include information about the environment and/or about objects in the environment and/or the mobile device, which are acquired or were acquired using the at least one sensor; (Kishin, Description, Paragraph 25, Lines 1-14, the electronic device 100 further includes one or more sensors 165 that can meter a physical quantity to identify the location of the electronic device as well as nearby obstacles of the electronic device 100 and convert metered or detected information into an electrical signal ); perform an object detection based on the sensor data for a recording time window in each case, to obtain first object datasets for detected objects (Kishin, Description, Paragraph 87, Lines 2-6, the electronic device 300 starts with the first scan of a frame. in step 522, the electronic device 300 identifies the grid point (such as a particular cell of the grid) that intersect with the scan based on the pose of the electronic device 300, the direction of the scan, and the range measurements, based on one or more of the sensors ) … (Kishin, Description, Paragraph 62, Lines 3-7, over a period of time the radar filter, of FIGURE 4a, stores and updates a list of the history radar scan points in a fixed global coordinate frame (step 441). It is noted that a radar scan point is defined as a collection of target detection results from the radar sensor ); and perform object tracking for a new SLAM dataset, which is to be added to a SLAM graph, (Kishin, Description, Paragraph 61, Lines 1-11, the SLAM layer 420 performs the localization and generates (or builds) a fusion map to be used by the navigation layer 430. The SLAM layer 420 performs the task of Lidar-based SLAM with radar aid. The SLAM layer 420 includes Lidar SLAM 422, a radar occupancy mapping engine 424, and a map fusion engine 426. The Lidar SLAM 422 uses the Lidar scan from the sensing layer 410 to generate a Lidar map and a Lidar pose for localization of the electronic device 300. The radar occupancy mapping engine 424 generates a radar map using the Lidar pose and the filtered radar scan from the Radar filter 416 of the sensing layer 410. radar occupancy mapping engine 424 is described in in greater detail below in FIGURE 4f. The radar map and the Lidar map 478 are then combined into a fusion map 479, via the map fusion engine ), including: allocate objects detected since a preceding SLAM dataset using the object detection, to real objects based on the first object datasets to obtain second object datasets for real objects to be considered in the SLAM graph (Kishin, Abstract, Lines 4-9, while the electronic device travels the area, the processor is configured to generate a first map indicating one or more objects within an area based on the Lidar scans and a second map based on the radar signals. The processor is configured to determine whether the second map indicates a missed object at the portion of the first map that is unoccupied. In response to a determination that the second map indicates the missed object, the processor is configured to modify the first map with information on the missed object ) … (Kishin, Description, Paragraph 160, Lines 2-10, electronic device 300 can maintain a list of historical radar scan points in a fixed global frame. The electronic device 300 then identifies a pose of the electronic device within the area based on the Lidar scan. The electronic device 300 can modify a point in the list of historical radar points from the fixed global coordinate frame to an electronic device frame, based on the pose of the electronic device 300. The pose of the electronic device 300 can include the orientation, and location of the electronic device 300 within the area. The electronic device 300 can also determine whether to discard a point based on a set of criteria in order to reduce noise and false alarms ); wherein the new SLAM dataset is provided and added to the SLAM graph, and based on the SLAM graph, navigation information being also provided for the mobile device, which includes object data for real objects in the environment, and a geometrical map of the environment and/or a trajectory of the mobile device in the environment (Kishin, Description, Paragraph 3, Lines 5-10, in response to a determination that a portion of the first map is unoccupied, the processor configured to determine whether the second map indicates a missed object at the portion of the first map that is unoccupied. In response to a determination that the second map indicates the missed object, the processor configured to modify the first map with information on the missed object ) … (Kishin, Description, Paragraph 65, Lines 1-12, the navigation layer 430 performs the navigation using the sensor data from the sensing layer 410 and the fusion map generated by the SLAM layer 420. The navigation layer 430 includes a localization engine 432 and a planning/navigation engine 434. The localization engine 432 localizes the electronic device 300 in a map generated by the SLAM layer by matching the Lidar scan data with the obstacles in the map. The planning/navigation engine 434 receives a navigation goal, the map in which the electronic device 300 is operating (generated by the SLAM layer 420), and the pose of the electronic device 300 in the map (from the localization engine 432). The planning/navigation engine 434 identifies a path to accomplish navigational goal while avoiding obstacles that are already presented in the map and/or detected by the sensors in real-time as the electronic device travels the path ) and a control unit and a drive unit for moving the mobile device according to the navigation information Kishin, Description, Paragraph 39, Lines 1-12, the drive system 320 can include one or more wheels, and motors, that are configured to propel and steer the electronic device 300 throughout an area. For example, the drive system 320 can include a one or more wheels that when rotated by a motor or drive mechanism propel the electronic device. The motor can be provided power from one or more power sources such as (i) an electric motor supplied power from a battery, or fuel cell, (ii) an internal/external combustion engine powered by an onboard fuel source, (iii) a hydraulic/pneumatic motor powered by an above aforementioned power source, (iv) compressed air, (v) the energy harvester unit 350, and the like. One or more of the wheels can swivel to aid navigation or adjustment of yaw of the electronic device 300. One or more of the wheels can be provided rotational power individually aid navigation or adjustment of yaw of the electronic device ). wherein the first object datasets are transformed from a sensor coordinate system to a reference coordinate system (67, the densified radar points are transferred from the sensor coordinate frame to the global coordinate frame ), associated with a preceding SLAM dataset prior to the allocation of detected objects to real objects (70, both in the global coordinate frame, a data association step is taken to assign the scan points to the radar history points (step 457) ). Additionally, Iqbal discloses, the allocation clusters multiple detected objects (E. Intermittent Clustering with NPDA, we query these new observations to fit any existing clusters. If these associated objects belong to existing clusters, then no new objects have been observed during this period ), detected in different recording time windows since the preceding SLAM dataset (C. Object Back-Projection using SLAM Pose, each detected object k in it is back-projected to W using dtk, estimated camera pose at t and intrinsic parameters. If there is an object with successful association using NPDA for a time period t−n to t, then all the back-projected points from pWt−nk to pWtk are combined in a joint distribution to represent a single feature vector ), into a single real object (IV. EXPERIMENTALRESULTS, Multiple objects are generally detected in an environment during a sequence, but an object is associated and then Clustered only if it is observed for a certain number of frames ). Regarding claim 28, Kishin and Iqbal disclose the mobile device as recited in claim 27, as discussed supra. Additionally Kishin discloses, the mobile device is a vehicle moving in an at least semiautomated manner (Kishin, Description, Paragraph 15, Lines 1-3, embodiments of the present disclosure further takes into consideration that autonomous electronic devices use very little human intervention to perform a given task ), the mobile device being a passenger vehicle or a vehicle transporting goods or a household robot or a lawnmower robot or a drone (Kishin, Description, Paragraph 11, Lines 3-6, automatic lawn mower that traverses an area and trims the grass, a floor cleaner (such as a vacuum cleaner or a mop) that traverses an area to collect dirt and debris, a pool cleaner that traverses an area to collect dirt and debris, a delivery drone, a surveillance drone, a search and rescue type drone, or any other type of device that can generate a map of an area and localize itself within the map ) … (Kishin, Description, Paragraph 12, Lines 3-5, the environment or area can be a yard, lawn, a single room, a body of water, a residence, an office complex, a street, a warehouse, a school, or any other consumer or commercial space ). Regarding claim 29, Kishin teaches, a non-transitory computer-readable memory medium on which is stored a computer program method for allocating objects in an environment using SLAM and a mobile device in the environment, which has at least one sensor for acquiring information about the environment and/or about objects in the environment and/or about the mobile device (Kishin, Description, Paragraph 6, Lines 1-3, moreover, various functions described below can be implemented or supported by one or more computer programs, each of which is formed from computer readable program code and embodied in a computer readable medium ) … (Kishin, Description, Paragraph 14, Lines 9-11, the electronic device can also localize itself within the map. In certain embodiments, the electronic device performs SLAM to generate the map and localize itself within an environment ) … (Kishin, Description, Paragraph 25, Lines 1-14, the electronic device 100 further includes one or more sensors 165 that can meter a physical quantity to identify the location of the electronic device as well as nearby obstacles of the electronic device 100 and convert metered or detected information into an electrical signal ), the computer program, when executed by a computer, causing the computer to perform the following steps: providing sensor data, which include information about the environment and/or about objects in the environment and/or the mobile device, which are acquired or were acquired using the at least one sensor (Kishin, Description, Paragraph 25, Lines 1-14, the electronic device 100 further includes one or more sensors 165 that can meter a physical quantity to identify the location of the electronic device as well as nearby obstacles of the electronic device 100 and convert metered or detected information into an electrical signal ); performing an object detection based on the sensor data for a recording time window in each case, to obtain first object datasets for detected objects (Kishin, Description, Paragraph 87, Lines 2-6, the electronic device 300 starts with the first scan of a frame. in step 522, the electronic device 300 identifies the grid point (such as a particular cell of the grid) that intersect with the scan based on the pose of the electronic device 300, the direction of the scan, and the range measurements, based on one or more of the sensors ) … (Kishin, Description, Paragraph 62, Lines 3-7, over a period of time the radar filter, of FIGURE 4a, stores and updates a list of the history radar scan points in a fixed global coordinate frame (step 441). It is noted that a radar scan point is defined as a collection of target detection results from the radar sensor ); and performing object tracking for a new SLAM dataset, which is to be added to a SLAM graph (Kishin, Description, Paragraph 61, Lines 1-11, the SLAM layer 420 performs the localization and generates (or builds) a fusion map to be used by the navigation layer 430. The SLAM layer 420 performs the task of Lidar-based SLAM with radar aid. The SLAM layer 420 includes Lidar SLAM 422, a radar occupancy mapping engine 424, and a map fusion engine 426. The Lidar SLAM 422 uses the Lidar scan from the sensing layer 410 to generate a Lidar map and a Lidar pose for localization of the electronic device 300. The radar occupancy mapping engine 424 generates a radar map using the Lidar pose and the filtered radar scan from the Radar filter 416 of the sensing layer 410. radar occupancy mapping engine 424 is described in in greater detail below in FIGURE 4f. The radar map and the Lidar map 478 are then combined into a fusion map 479, via the map fusion engine ), including :allocating objects detected since a preceding SLAM dataset using the object detection, to real objects based on the first object datasets to obtain second object datasets for real objects to be considered in the SLAM graph (Kishin, Abstract, Lines 4-9, while the electronic device travels the area, the processor is configured to generate a first map indicating one or more objects within an area based on the Lidar scans and a second map based on the radar signals. The processor is configured to determine whether the second map indicates a missed object at the portion of the first map that is unoccupied. In response to a determination that the second map indicates the missed object, the processor is configured to modify the first map with information on the missed object ) … (Kishin, Description, Paragraph 160, Lines 2-10, electronic device 300 can maintain a list of historical radar scan points in a fixed global frame. The electronic device 300 then identifies a pose of the electronic device within the area based on the Lidar scan. The electronic device 300 can modify a point in the list of historical radar points from the fixed global coordinate frame to an electronic device frame, based on the pose of the electronic device 300. The pose of the electronic device 300 can include the orientation, and location of the electronic device 300 within the area. The electronic device 300 can also determine whether to discard a point based on a set of criteria in order to reduce noise and false alarms ), wherein the first object datasets are transformed from a sensor coordinate system to a reference coordinate system (67, the densified radar points are transferred from the sensor coordinate frame to the global coordinate frame ), associated with a preceding SLAM dataset prior to the allocation of detected objects to real objects (70, both in the global coordinate frame, a data association step is taken to assign the scan points to the radar history points (step 457) ). Additionally, Iqbal discloses, the allocation clusters multiple detected objects (E. Intermittent Clustering with NPDA, we query these new observations to fit any existing clusters. If these associated objects belong to existing clusters, then no new objects have been observed during this period ), detected in different recording time windows since the preceding SLAM dataset (C. Object Back-Projection using SLAM Pose, each detected object k in it is back-projected to W using dtk, estimated camera pose at t and intrinsic parameters. If there is an object with successful association using NPDA for a time period t−n to t, then all the back-projected points from pWt−nk to pWtk are combined in a joint distribution to represent a single feature vector ), into a single real object (IV. EXPERIMENTALRESULTS, Multiple objects are generally detected in an environment during a sequence, but an object is associated and then Clustered only if it is observed for a certain number of frames ). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHANE E DOUGLAS whose telephone number is (703)756-1417. The examiner can normally be reached Monday - Friday 7:30AM - 5:00PM. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christian Chace can be reached on (571) 272-4190. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /S.E.D./ Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665 Application/Control Number: 18/331,351 Page 2 Art Unit: 3665 Application/Control Number: 18/331,351 Page 4 Art Unit: 3665 Application/Control Number: 18/331,351 Page 5 Art Unit: 3665 Application/Control Number: 18/331,351 Page 6 Art Unit: 3665 Application/Control Number: 18/331,351 Page 7 Art Unit: 3665 Application/Control Number: 18/331,351 Page 8 Art Unit: 3665 Application/Control Number: 18/331,351 Page 9 Art Unit: 3665 Application/Control Number: 18/331,351 Page 10 Art Unit: 3665 Application/Control Number: 18/331,351 Page 11 Art Unit: 3665 Application/Control Number: 18/331,351 Page 12 Art Unit: 3665 Application/Control Number: 18/331,351 Page 13 Art Unit: 3665 Application/Control Number: 18/331,351 Page 14 Art Unit: 3665 Application/Control Number: 18/331,351 Page 15 Art Unit: 3665 Application/Control Number: 18/331,351 Page 16 Art Unit: 3665 Application/Control Number: 18/331,351 Page 17 Art Unit: 3665 Application/Control Number: 18/331,351 Page 18 Art Unit: 3665 Application/Control Number: 18/331,351 Page 19 Art Unit: 3665 Application/Control Number: 18/331,351 Page 20 Art Unit: 3665 Application/Control Number: 18/331,351 Page 21 Art Unit: 3665 Application/Control Number: 18/331,351 Page 22 Art Unit: 3665 Application/Control Number: 18/331,351 Page 23 Art Unit: 3665 Application/Control Number: 18/331,351 Page 24 Art Unit: 3665 Application/Control Number: 18/331,351 Page 25 Art Unit: 3665
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