DETAILED ACTION
This is the First Office Action on the Merits and is directed towards claims 1-7 and 9-21 as originally amended and or filed on 07/03/2024.
Notice of Pre-AIA or AIA Status
Priority is claimed as set forth below, accordingly the earliest effective filing date is January 4, 2022 (20220104).
The present application, effectively filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d).
This application is a U.S. National Stage of International Application No. PCT/CN2022/105276, filed on July 12, 2022, which claims priority to Chinese Patent Application No. 202210006545.8 filed on January 4, 2022 (20220104).
Information Disclosure Statement
As required by M.P.E.P. 609 [R-07.2022], Applicant's 07/03/2024, 06/26/2025, 10/20/2025 and 01/26/2026 submission(s) of Information Disclosure Statement (IDS)(s) is/are acknowledged by the Examiner and the reference(s) cited therein has/have been considered in the examination of the claim(s) now pending. A copy of the submitted IDS(s) initialed and dated by the Examiner is/are attached to the instant Office action.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-7 and 9-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over CN 111638526 A to LUO, De-yuan et al. (hereinafter Luo, cited in the 07/03/2024 IDS) in view of US 20210124354 A1 to Munich; Mario et al. (hereinafter Munich).
Regarding claim 1 Luo teaches in for example the Figure(s) reproduced immediately below:
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and associated descriptive texts a map-building method for a robot (as shown in the figures above a robot (e.g. fig. 4) is building maps (e.g. figs. 2, 7(a)-(d), via method shown in fig. 1 as explained in for example para:
“FIG. 1 shows a flowchart of a method for automatically building a robot in a stranger environment, comprising the following steps:
S1, taking the position of the robot as the reference point, according to the wall random tree algorithm, generating all the front point between the known area and the unknown area.
The method uses the open source SLAM algorithm Gmapping based on filter SLAM frame to construct the 2 D grid map; the known area and the unknown area are divided according to the 2 D grid map. In the grid pattern, the grid cells have three states: Idle, occupation (i.e., obstacle existence) and unknown. the front edge point in the grid graph is defined as the boundary between the idle and unknown grids; the invention subsequently uses the wall-through random tree algorithm to generate the front edge point on the map, also based on the grid graph. For the closed environment, the wall random tree algorithm has integrity, which can ensure the robot to explore all areas in the autonomous search process and construct a complete map.“),
comprising:
controlling the robot to travel toward a target point (as shown in the figures above the robot travels towards target point as explained in for example paras:
“In the moving process, the laser radar will detect the new area and generate new front point, at this time, the target front point may not be optimal, then the front point is selected again and re-planning path to the new target front point. After the experiment, the method will cause the target point position change too fast, and even generate the most advantage between the two front point jumping condition, the robot swing cannot advance. Therefore, in order to reduce the refresh frequency of the target point, it can set motion radius and fixed step length dual standard for the robot, once the robot position distance exceeds the motion radius and the motion distance exceeds the fixed step length, resetting the random tree screening new target point and planning a new path. After experiment, setting the fixed step length is 1.5 times of the motion radius; the motion radius is set to be 0.1 times of the shortest side length of the predicted exploration environment; it can achieve better effect.
at the same time, because the random tree later growth is slow, in order to make the robot more quickly search the front point near the robot, need to reset the random tree, not always taking the initial position as the root node, but each time refreshing the target point and the current position of the robot as the root node to generate new wall random tree, In this way, it can ensure that the front edge point which is explored every time is closer to the robot. The purpose is to make the search efficiency more efficient and reduce the moving distance of the robot.”),
wherein the target point is a first exploration point of a region to be explored,
and the first exploration point is in an unexplored state (as shown in for example Fig. 7(a) and explained in for example paras:
“With the method of the invention, the construction of the unknown area map of the robot is realized. The experiment field is an idle field of 8m * 8m, the paper box is simply arranged as shown in FIG. 3. The robot used for experiment is two-wheel differential robot, the size is 85cm * 75cm, the experimental robot diagram is as shown in FIG. 4. it is configured with laser radar; the angle is 180 degrees, the measuring radius is 8m, the picture of the laser radar is shown in FIG. 5.
the laser radar uses USB to transmit the data to the mini-PC; the bottom layer of the laser radar drives the leimin_lidar-node node to realize the reading and correction of the laser radar original data, At the same time, the corrected laser radar data is converted into the LaserScan format under the standard ROS and sent out by the Topic name of /uestc003/scan. then through /uestc003/slam - gmapping node subscription and mapping, by /searching the front point and sending to the node/filter, the node /filter the cluster screening sent to the node /the er, at last, sending the leading edge point with the highest evaluation value to the moving-base packet to navigated.
the graphical interface is shown in FIG. 6, the terminal three interfaces are respectively the start of the gmapping node and the moving-base node, rviz visualization interface starting and self-search algorithm related node operation. As shown in FIG. 7 (a), the laser radar scans a part of the obstacle. As shown in FIG. 7 (d), the line is a wall random tree, the final whole map is finished, there is no new front point needs to search, the robot is still, the complete structure is shown in FIG. 7 (b), FIG. 7 (b) is not display the random tree. FIG. 7 (c) is an actual field map.”);
detecting exploration point information corresponding to a preset range wherein in which the target point is located during travelling of the robot toward the target point (see for example para:
“S3, taking the current position of the robot as the starting point; the front target point as the end point; finishing the path planning.
in each forward target point moving process, using the DWA algorithm (full name dynamic window, dynamic window method) to perform local path planning, so as to avoid the obstacle. The local-planner plug-in provided in the ROS-provided base-base packet is well integrated with the DWA algorithm, and the plug-in is directly applied in the actual navigation.“);
processing the target point according to the exploration point information;
and in response to the foregoing, arriving at the target point and collecting map-building data of the region to be explored (in for example the figures and paras:
“S4, in the moving process of the robot to the front target point, when the distance between the current position of the robot distance is greater than the preset moving radius or the preset step length, judging whether there is an unknown area, if there is, returning to step S1, the robot current position is the root node resetting the random tree, re-screening the front edge point, determining the new target front point and re-planning path, if not, stopping advancing, finishing the map drawing.”).
Luo does not appear to expressly disclose “in response to the foregoing, arriving at the target point and collecting map-building data of the region to be explored in order to update an environmental spatial map of the robot.” (Emphasis added).
In analogous art Munich teaches in for example, the figures below:
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And associated descriptive texts “collecting map-building data of the region to be explored in order to update an environmental spatial map of the robot” (in for example paras:
“[0087] After the operation 208, the operation 210 and/or the operation 212 can be performed. At the operation 210, the robot 100 initiates a behavior based on a feature associated with one of the labels. The robot 100 can generate the mapping data at the operation 202 during a first cleaning mission, and can initiate the behavior at the operation 210 in a second cleaning mission. In this regard, the map constructed at the operation 208 can represent a persistent map that the robot 100 can use across multiple discrete cleaning missions. The robot 100 can collect mapping data in each cleaning mission and can update the map constructed at the operation 208 as well as the labels on the map provided at the operation 208. The robot 100 can update the map with newly collected mapping data in subsequent cleaning missions.”).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the updating disclosed in Munich with the robot taught in Luo with a reasonable expectation of success because it would have “allowed the robot to use the data in a second cleaning mission” as taught by Munich Para(s):
“[0015] In another aspect, a mobile computing device including a user input device, a display, and a controller operably connected to the user input device and the display. The controller is configured to execute instructions to perform operations including presenting, using the display, a visual representation of an environment based on mapping data produced by an autonomous cleaning robot in the environment during a first cleaning mission, a visual indicator of a label associated with a portion of the mapping data, and a visual indicator of a state of a feature in the environment associated with the label. The operations include updating the visual indicator of the label and the visual indicator of the state of the feature based on mapping data produced by the autonomous cleaning robot during a second cleaning mission.”.
Regarding claim 2 and the limitation the map building method for the robot according to claim 1, wherein processing the target point according to the exploration point information comprises:
maintaining the target point in response to the exploration point information indicating that a second exploration point exists within the preset range;
and updating the target point to the second exploration point in response to the exploration point information indicating that no second exploration point exists within the preset range and the second exploration point exists outside the preset range,
wherein the second exploration point is a first exploration point other than the target point (see the teachings of Luo wherein it is understood the information gain is embodied in terms of the spatial fraction of the unknown area around the leading edge point, the specific steps are as follows: the leading edge point detection circle is formed by the radius of the detection distance of the LIDAR, as shown in Figure 2: the information gain is quantified by calculating the spatial proportion of the unknown region in the circle to the entire circle. A Person of Ordinary Skill In The Art (POSITA) would understand that the target point is maintained if there are unexplored points within the pre-set range of the target point, however if there are no unexplored points within the pre-set range of the target point and no unexplored points outside the pre-set range, then the selection of the target point needs to be reproduced, as known in the art and explained therein as cited above).
Regarding claim 3 and the limitation the map building method for the robot according to claim 2, further comprising:
controlling the robot to stop traveling based on no second exploration point existing in the region to be explored (see for example Luo para:
“S4, in the moving process of the robot to the front target point, when the distance between the current position of the robot distance is greater than the preset moving radius or the preset step length, judging whether there is an unknown area, if there is, returning to step S1, the robot current position is the root node resetting the random tree, re-screening the front edge point, determining the new target front point and re-planning path, if not, stopping advancing,”);
and storing the environmental spatial map (see the teachings of Munich and the obviousness to combine and the rejection of corresponding parts of claims 2 and 1 above incorporated herein by reference.).
Regarding claim 4 and the limitation the map building method for the robot according to claim 1, wherein prior to the step of controlling the robot to travel toward the target point, the method further comprises:
acquiring the environmental spatial map, wherein the environmental spatial map comprises at least one of a known region or the region to be explored, and the known region has corresponding map building data;
extracting a plurality of boundary points of the region to be explored overlapping the known region;
and determining a boundary point in an unexplored state as the first exploration point (see the teachings of Luo Fig. 1 above and especially item S1, generating all leading points between known and unknown regions according to a through wall random tree algorithm, taking as reference point the position of the robot. S11. Constructing a 2D grid map using Gmapping, an open source SLAM algorithm based on a filtered SLAM framework, with the current position of the robot as a reference point; s12. The LIDAR scans the range of radius R to obtain the positional relationship of the known area to the robot. i.e. The LIDAR ascertains the known area that can be travelled, the relative positional relationship of obstacles to the robot, obtaining a 2D grid map of the known area, the location of presence of obstacles and the unknown area, centered on the location of the robot. S13. Starting from where the robot is located, a random tree is generated using a through wall random tree algorithm, extending outwards, and when the random tree generates a new branch, a leading point is determined by two points of the new branch if one point of the new branch is in a known area of the 2D raster map and another point is in an unknown area of the 2D raster map. Repeating step S13, until all leading points between known and unknown areas in the 2D grid map that can travel are found (equivalent to before the step of controlling the robot to travel to a target point), further comprising obtaining the environmental space map comprising a known region and/or a region to be explored, the known region corresponding to mapping data; extracting a plurality of boundary points of the region to be explored that overlap with the known region; extracting a plurality of boundary points of the region to be explored that overlap with the known region; determining a boundary point in an unexplored state as the first explored point).
Regarding claim 5 and the limitation the map building method for the robot according to claim 1, further comprising:
in response to a plurality of first exploration points in the region to be explored, sequentially connecting the plurality of first exploration points to acquire a connection line;
determining a first distance between each of the first exploration points and a center point of the connection line;
and determining a first exploration point corresponding to a smallest value of the first distance as the target point (see the obviousness to combine and the rejection of corresponding parts of claim 1 above incorporated herein by reference wherein it is understood that Luo teaches S2, evaluating the leading points of said preliminary screening according to the expected distance and the spatial fraction of the unknown zone, resulting in a leading target point; front point evaluation is the basis for selecting a front point. Leader points are generally evaluated from three factors: information gain of the leading point, navigation costs, and precision of robot localization; among these factors, the navigation cost is defined as the expected distance to be traversed by the robot from the current position to reach the leading point, expressed in terms of the Euclidean distance of the robot from the current position to the leading point. Thus, it is readily conceivable for a POSITA that a plurality based on a number of the first exploration points within the region to be explored, sequentially connecting a plurality of the first exploration points resulting in a connecting line, determining a first distance between each of the first exploration points and a center point of the connecting line, determining the first exploration point corresponding to a minimum of the first distances as the target point, as known in the art).
Regarding claim 6 and the limitation the map-building method for the robot according to claim l, wherein after collecting the map-building data of the region to be explored, the method further comprises:
updating the target point to be in an explored state (given the Broadest Reasonable Interpretation (BRI) it would appear that once a building is explored and a map is updated that a target point would be considered explored).
Regarding claim 7 and the limitation the map-building method for the robot according to claim l further comprising:
controlling, during travelling of the robot toward the target point, the robot to travel along an outer edge of an obstacle to bypass the obstacle based on presence of the obstacle between the target point and a current position of the robot (see the teachings of Luo with regard to path planning and edge searches and edge points as well as the teachings of Munich and the obviousness to combine and the rejection of corresponding parts of claim 1 above incorporated herein by reference, see for example para:
“[0072] The mapping data 197 represent data indicative of the features 1 . . . N in the environment 20. The sets of data 1 . . . N of the mapping data 197 can be indicative of the current states and types of the features 1 . . . N in the environment 20. The mapping data 197 can be indicative of geometry of an environment. For example, the mapping data 197 can be indicative of a size of a room (e.g., an area or a volume of a room), a dimension of a room (e.g., a width, a length, or a height of a room), a size of an environment (e.g., an area or a volume of an environment), a dimension of an environment (e.g., a width, a length, or a height of a room), a shape of a room, a shape of an environment, a shape of an edge of a room (e.g., an edge defining a boundary between a traversable area and a nontraversable area of a room), a shape of an edge of an environment (e.g., an edge defining a boundary between a traversable area and a nontraversable area of an environment), and/or other geometric features of a room or environment. The mapping data 197 can be indicative of an object in an environment. For example, the mapping data 197 can be indicative of a location of an object, a type of an object, a size of an object, a footprint of an object on a floor surface, whether an object is an obstacle for one or more devices in an environment, and/or other features of an object in the environment.”).
Regarding claim 9 and the limitation A robot, comprising a processor and a memory, wherein:
the memory is configured to store operation instructions;
and the processor is configured to perform following operations by invoking the operation instructions:
controlling the robot to travel toward a target point, wherein the target point is a first exploration point of a region to be explored, and the first exploration point is in an unexplored state;
detecting exploration point information corresponding to a preset range wherein the target point is located during travelling of the robot toward the target point;
processing the target point according to the exploration point information;
and in response to the foregoing, the robot arrives at the target point,
collecting map-building data of the region to be explored to update an environmental spatial map of the robot (see the obviousness to combine and the rejection of corresponding parts of claim 1 above incorporated herein by reference).
Regarding claim 10 and the limitation the robot according to claim 9, wherein the processor is further configured to perform following operations by invoking the operation instructions:
maintaining the target point in response to the exploration point information indicates that a second exploration point exists within the preset range;
and updating the target point to the second exploration point in response to the exploration point information indicating that no second exploration point exists within the preset range and the second exploration point exists outside the preset range,
wherein the second exploration point is a first exploration point other than the target point (see the obviousness to combine and the rejection of corresponding parts of claim 2 above incorporated herein by reference).
Regarding claim 11 and the limitation the robot according to claim 10, wherein the processor is further configured to perform following operations by invoking the operation instructions:
controlling the robot to stop traveling based on no second exploration point existing in the region to be explored;
and storing the environmental spatial map (see the obviousness to combine and the rejection of corresponding parts of claim 3 above incorporated herein by reference).
Regarding claim 12 and the limitation the robot according to claim 9, wherein the processor is further configured to perform following operations by invoking the operation instructions:
acquiring the environmental spatial map, wherein the environmental spatial map comprises at least one of a known region or the region to be explored, and the known region has corresponding map-building data;
extracting a plurality of boundary points of the region to be explored overlapping the known region;
and determining a boundary point in an unexplored state as the first exploration point (see the obviousness to combine and the rejection of corresponding parts of claim 4 above incorporated herein by reference).
Regarding claim 13 and the limitation the robot according to claim 9, wherein the processor is further configured to perform following operations by invoking the operation instructions:
in response to that there a plurality of first exploration points in the region to be explored, sequentially connecting the plurality of first exploration points to acquire a connection line;
determining a first distance between each of the first exploration points and a center point of the connection line;
and determining a first exploration point corresponding to a smallest value of the first distance as the target point (see the obviousness to combine and the rejection of corresponding parts of claim 5 above incorporated herein by reference).
Regarding claim 14 and the limitation the robot according to claim 9, wherein the processor is further configured to perform following operations by invoking the operation instructions: updating the target point to be in an explored state (see the obviousness to combine and the rejection of corresponding parts of claim 6 above incorporated herein by reference).
Regarding claim 15 and the limitation the robot according to claim 9, wherein the processor s further configured to perform following operations by invoking the operation instructions: controlling, during travelling of the robot toward the target point, the robot to travel along an outer edge of an obstacle to bypass the obstacle based on presence of the obstacle between the target point and a current position of the robot (see the obviousness to combine and the rejection of corresponding parts of claim 7 above incorporated herein by reference).
Regarding claim 16 and the limitation A storage medium storing computer programs thereon, wherein when the computer programs are executed by a processor, following operations are implemented:
controlling a robot to travel toward a target point, wherein the target point is a first exploration point of a region to be explored, and the first exploration point is in an unexplored state;
detecting exploration point information corresponding to a preset range in which the target point is located during travelling of the robot toward the target point;
processing the target point according to the exploration point information;
and in response to that the robot arrives at the target point,
collecting map-building data of the region to be explored to update an environmental spatial map of the robot (see the obviousness to combine and the rejection of corresponding parts of claim 1 above incorporated herein by reference).
Regarding claim 17 and the limitation the storage medium according to claim 16, wherein when the computer programs are executed by the processor, following operations are implemented:
maintaining the target point in response to that the exploration point information is that a second exploration point exists within the preset range;
and updating the target point to the second exploration point in response to that the exploration point information is that no second exploration point exists within the preset range and the second exploration point exists outside the preset range,
wherein the second exploration point is a first exploration point other than the target point (see the obviousness to combine and the rejection of corresponding parts of claim 2 above incorporated herein by reference).
Regarding claim 18 and the limitation the storage medium according to claim 17, wherein when the computer programs are executed by the processor, following operations are implemented:
controlling the robot to stop traveling based on no second exploration point existing in the region to be explored;
and storing the environmental spatial map (see the obviousness to combine and the rejection of corresponding parts of claim 3 above incorporated herein by reference).
Regarding claim 19 and the limitation the storage medium according to claim 16, wherein when the computer programs are executed by the processor, following operations are implemented:
acquiring the environmental spatial map,
wherein the environmental spatial map comprises at least one of a known region or a region to be explored, and the known region has corresponding map-building data;
extracting a plurality of boundary points of the region to be explored overlapping the known region;
and determining a boundary point in an unexplored state as the first exploration point (see the obviousness to combine and the rejection of corresponding parts of claim 4 above incorporated herein by reference).
Regarding claim 20 and the limitation the storage medium according to claim 16, wherein when the computer programs are executed by the processor, the following operations are implemented:
in response to a plurality of first exploration points in the region to be explored, sequentially connecting the plurality of first exploration points to acquire a connection line;
determining a first distance between each of the first exploration points and a center point of the connection line;
and determining a first exploration point corresponding to a smallest value of the first distance as the target point (see the obviousness to combine and the rejection of corresponding parts of claim 5 above incorporated herein by reference).
Regarding claim 21 and the limitation the storage medium according to claim 16, wherein when the computer programs are executed by the processor, the following operation is implemented: updating the target point to be in an explored state (see the obviousness to combine and the rejection of corresponding parts of claim 6 above incorporated herein by reference).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure as teaching, inter alia, the state of the art of robot map-building at the time of the invention. For example:
US 20210018929 A1 to CHOI; Jieun teaches, inter alia a MOBILE ROBOT AND CONTROL METHOD THEREOF in for example the ABSTRACT, Figures and/or Paragraphs below:
“1. A mobile robot comprising: a wheel configured to move a main body of the mobile robot; a sensor configured to collect sensor data regarding a distance from the main body to at least one object in a region outside the main body; and a controller configured to: create node data associated with a plurality of nodes corresponding to locations in the regions where the sensor data is collected by the sensor, create a map of the region based on the sensor data and the node data, create first map data identifying a path through at least one of the nodes based on the node data, update the map based on the first map data to include an indication of the path, and perform image-processing to the updated map to create second map data to identify another location in the region that is not represented in the node data for collecting additional sensor data by the sensor.”.
US 20210404834 A1 to Del Pero; Luca et al. teaches, inter alia a Localization Based on Multi-Collect Fusion in for example the ABSTRACT, Figures and/or Paragraphs below:
“[0086] In example embodiments, the misalignments and inaccuracies of geometries of the new local map structure data and its visual features and poses can all be transformed independently to conform with the coordinates of the existing map 1275. Aligning and connecting and/or fusing the new visual map portion to be localized can be accomplished by an optimization process and/or by using common data such as common images between sets of data to align or constrain alignment of points within each map portion together. In example embodiments the new visual map portion, or local map portion, can be transformed using constraints-based optimization with variables. Variables include, but is not limited to, poses of all of the images obtained by each mapping vehicle, and constraint include, but is not limited to, poses of images within each of the map segments and the relative pose of images within each of the map portions. In this way, both local and remote resources can be used to provide substantially real time of a device. Specifically, the location 1270 of the device can be determined within the global coordinate system based on the local map portion's transformation onto the existing map 1275. In embodiments, when localizing devices using the multi-pass fusion technique, the new data (for example, data 1205) is used to as a reference to localize the device only, not to update or add to the existing map (e.g., the global map). In example embodiments, the existing or global map is not associated with a particular reference device, vehicle, sensor or collect, but instead is a global reference with a variety of collects, samples, sensor input data, map types or map segments from a plurality and/or variety of devices, vehicles, robots or sensors obtained at different times, places and environmental conditions which may be continuously updated. In this context, the structure data of a local map can be transformed onto the structure data of a global map.”.
US 11037320 B1 to Ebrahimi Afrouzi; Ali et al. teaches, inter alia Method for estimating distance using point measurement and color depth in for example the ABSTRACT, Figures and/or Paragraphs below:
“A method including detecting an object in a line of sight of at least one sensor; adjusting a current path of the robot to include a detour path around the object, instructing the robot to resume along the current path after avoiding the object, discarding at least some data collected by sensors of the robot in overlapping areas covered, inferring previously visited areas and unvisited areas, generating a planar representation of a workspace of the robot by stitching data collected by at least some sensors of the robot at overlapping points, and presenting at least the planar representation and coverage statistics on an application of a communication device.”.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL LAWSON GREENE JR whose telephone number is (571)272-6876. The examiner can normally be reached on MON-THUR 7-5:30PM (EST) or via email at DanielL.GreeneJr@USPTO.GOV under the guidance of MPEP Section 502.03 Communications via Internet Electronic Mail (email) [R-07.2022]. The written authorization may be found at https://www.uspto.gov/patents/apply/forms and submitted via EFS-Web, mail, or fax. The Examiner’s Fax number is 571-273-6876.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hunter Lonsberry can be reached on (571) 272-7298. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL L GREENE/Primary Examiner, Art Unit 3665 20260416
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