CTFR 18/495,843 CTFR 96410 DETAILED ACTION 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. This a Final Action on the Merits. Claims 1-4, 6-13, and 15-20 are currently pending and are addressed below. Response to Amendments The amendment filed on February 20 th , 2026 has been considered and entered. Accordingly, claims 1, 4, 6, 8, 10, 13, 15, 18, and 19 have been amended. Claims 5 and 14 have been cancelled. Response to Arguments The previous rejection of claims 10-20 under 35 U.S.C. 101 has have been maintained in view of the applicant’s amendments. The broadest reasonable interpretation of “computer readable storage media” as recited by the applicant can encompass non-statutory transitory forms of signal transmission. The applicant’s arguments with respect to claims 1-4, 6-13, and 15-20 have been considered but are moot in view of the newly formulated grounds of rejections necessitated by the applicant’s amendments. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 10-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because claims 10 and 19 are directed to a computer readable medium which can encompass non-statutory transitory forms of signal transmission. See In re Nuijten, 500 F.3d 1346, 84 USPQ2d 1495 (Fed. Cir. 2007). While the specification indicates that the computer readable medium could be a semiconductor memory, a hard disk memory or an optical memory, the broadest reasonable interpretation of “computer readable medium”, in light of the specification, could be interpreted by one of ordinary skill in the art encompasses transitory forms of signal transmission. The Examiner suggests amending the claims to specify that the computer readable medium is a non-transitory computer readable medium. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21-aia AIA Claim s 1-3, 10-12, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Rakshit (US 20210109544 A1) (“Rakshit”) in view of Li (An algorithm for safe navigation of mobile robots by a sensor network in dynamic cluttered industrial environments) (“Li”) (Attached) . With respect to claim 1, Rakshit teaches a computer-implemented method, comprising: receiving, by a centralized computer, a set of sensor data from at least one distance measuring sensor located in an enclosed environment (See at least Rakshit FIG. 4 and Paragraph 64 “Processing proceeds to step S270 where shared data mod 370 collects shared data from vehicles in the travel cluster. Shared data is data made available to the vehicle cluster by a member vehicle. In this example, shared data supporting travel decisions including location, speed, and direction of travel is collected by the reference vehicle from the member vehicles. Data collected from onboard sensors and systems including cameras, traction sensors, and location systems are provided to the reference vehicle for processing. It should be noted that shared data may also be processed locally by a member vehicle to make driving decisions not delegated to the reference vehicle or to override certain driving decisions made by the reference vehicle. Alternatively, other combinations of sensor data and system data are collected for use by the reference vehicle. Alternatively, all data collected by the member vehicles is available to the reference vehicle for processing or for delivery to a remote server for processing.”) ; detecting, by the centralized computer, at least one object in the enclosed environment based on the received set of sensor data (See at least Rakshit FIG. 4 and Paragraph 67 “Shared date is collected by vehicles within the cluster. These member vehicles, moving in the same direction along a roadway or other common trajectory, will collect information during the journey including: (i) nearby vehicles, (ii) obstacles in the road, (iii) sign board information, (iv) driving directions, (v) weather related information, (vi) information obtained from IOT (internet of things) sensors on the road, and/or (vii) the condition of road. A considerable portion of this collected information will be duplicated among member vehicles in cases where each member vehicle is collecting the data from the similar sensors.”); coordinating, by the centralized computer, real-time autonomous movements of a plurality of navigation devices within the enclosed environment, based on a plurality of machine- readable movement paths corresponding to the plurality of navigation devices generated by the centralized computer based on the generated obstacle map, wherein the plurality of machine-readable movement paths are configured to enable the plurality of navigation devices to perform real-time obstacle avoidance of enclosed environment (See at least Rakshit FIG. 4 and Paragraphs 67-69 “Shared date is collected by vehicles within the cluster. These member vehicles, moving in the same direction along a roadway or other common trajectory, will collect information during the journey including: (i) nearby vehicles, (ii) obstacles in the road, (iii) sign board information, (iv) driving directions, (v) weather related information, (vi) information obtained from IOT (internet of things) sensors on the road, and/or (vii) the condition of road. A considerable portion of this collected information will be duplicated among member vehicles in cases where each member vehicle is collecting the data from the similar sensors. Processing proceeds to step S280 where instructions mod 380 processes the travel dataset, the vehicle locations, and the travel conditions to generate driving instructions. In this example, the reference vehicle generates the travel dataset, identifies the vehicle locations within the cluster, and identifies travel conditions. This data is processed locally by the reference vehicle for making driving decisions in real time as the vehicles travel along a trajectory. The instructions mod generates instructions that will be followed by the reference vehicle as well as the member vehicles. Alternatively, the data obtained by the reference vehicle is transmitted to a remote server where the instructions module generates the driving instructions. Alternatively, the remote server identifies the vehicle locations and the travel conditions. In that way, the travel dataset and other data is combined at the remote server for generating driving instructions. In some embodiments, the travel dataset is generated by the remote server from the received shared data. In some embodiments, the travel dataset is provided to the remote server and added to the other identified information to generate driving instructions. Processing proceeds to step S285 where driving mod 385 directs the vehicles of the travel cluster to operate according to the driving instructions. In this example, the reference vehicle instructs the member vehicles with the driving instructions as well as follows the driving instructions. Alternatively, the remote server directs the operation of the reference vehicle with the driving instructions. The reference vehicle in turn sends the driving instructions to the member vehicles. Alternatively, the remote server instructs each of the vehicles within a cluster according to the driving instructions.”). Rakshit fails to explicitly disclose that the at least one stationary distance measuring sensor being configured to output distance measurements by transmitting energy and converting reflected energy into a distance measure; generating, by the centralized computer, a baseline map of the enclosed environment based on a first set of measurements in the received set of sensor data indicating first distances between the at least one stationary distance measuring sensor and one or more boundaries that define the enclosed environment; detecting, by the centralized computer, at least one object in the enclosed environment based on a second set of measurements in the received set of sensor data, the detected at least one object being a physical object located in the enclosed environment; generating, by the centralized computer, an obstacle map of the enclosed environment by determining a position of the detected at least one object on the baseline map from the second set of measurements in the sensor data indicating second distances between the at least one stationary distance measuring sensor and the detected at least one object and plotting the determined position of the detected at least one object in the generated baseline map of the enclosed environment; wherein the plurality of machine-readable movement paths are configured to enable the plurality of navigation devices to perform real-time obstacle avoidance of enclosed environment without relying on respective onboard navigation sensors. Li teaches that the at least one stationary distance measuring sensor being configured to output distance measurements by transmitting energy and converting reflected energy into a distance measure (See at least Li Page 67 “In the industrial environment, there are some static and moving obstacles, like walls, other robots and moving people. The obstacles can be non-convex and their velocity can be dynamic and unknown. To detect the obstacles, a sensor network is deployed in the workspace. The sensor network consists of some range finder sensor nodes. Each node measures the distances to the nearest obstacles in different directions within a measurement range denoted by Rs (see Fig. 2). Each range finder is deployed higher than any mobile robots in the work space. It means any mobile robots on the factory floor cannot be detected by the sensor network. Furthermore, a central computer node connects to the sensor network to obtain the real-time measurements from each sensor node. The central computer also can obtain the lo cation and direction of any robots in the workspace by wireless communication. After obtaining the environment information and the location and direction of each robot, the sensor network dynamically generates a safe path for each robot according to the proposed navigation algorithm and send the paths to the robots for tracking. Remark 2.1. It should be noticed that because the height of the scanning plane of the sensor network is higher than the height of the robots, there is a potential danger that the robot may collide with obstacles which is shorter than robots. To avoid this case, the cheap and micro range sensors (e.g. sonar sensor) can be mounted on the low position of the robots to measure the minimum distance from any obstacles. Then, some local obstacle avoidance algorithms like [9,43] can be applied as the optional strategy to deal with this situation”). generating, by the centralized computer, a baseline map of the enclosed environment based on a first set of measurements in the received set of sensor data indicating first distances between the at least one stationary distance measuring sensor and one or more boundaries that define the enclosed environment (See at least Li Page 67 “With the help of the location and direction of each sensor node, the local detected areas of all the sensor nodes at any time t can be con verted to the global coordinate system, then a total detected area, which is the union of the local detected areas, is obtained.”); detecting, by the centralized computer, at least one object in the enclosed environment based on a second set of measurements in the received set of sensor data, the detected at least one object being a physical object located in the enclosed environment; generating, by the centralized computer, an obstacle map of the enclosed environment by determining a position of the detected at least one object on the baseline map from the second set of measurements in the sensor data indicating second distances between the at least one stationary distance measuring sensor and the detected at least one object and plotting the determined position of the detected at least one object in the generated baseline map of the enclosed environment (See at least Li Pages 70-80 “In the following subsection, a graph based candidate paths generation algorithm is proposed. The paths generated by the candidate paths generation algorithm are adjusted by the proposed path planner. Due to the unknown changes of the dynamic environment, the path generation and path adjustment algorithms run at each time step to provide the real-time safe path P*, which is planned according to the current measurements. It is obvious that with unknown dynamic environment, it is impossible to design a path planning algorithm to improve the cost of the final solution regarding to the future movement of the robot. However, with the proposed real-time path planning algorithm, the path is at least updated and adjusted at every time step ac cording to the current measurements. A graph search algorithm is proposed here to generate some target reaching candidate paths belonging to the different homotopy classes. Then, these candidate paths are adjusted according to the algorithm A1–A3 to search the path P*. Firstly we introduce a graph as follows … A graph denoted byG is introduced that its vertices are the target , C C, , , T robot’s position p0(x(t), y(t)), the points of (A), (A′), (B), (B′), (S) and (V) types. Its edges are the segments of the curves … 0 1 the arc of the initial circles and the segments of (AT), (AA′), (BT) and (BB′) types (see Fig. 16) … To confirm the performance of the proposed navigation algorithm in static industrial environments, we build two static scenes with same static obstacles but different deployment of the sensor nodes. In the first scene, there are some static, non-convex and irregular shaped obstacles. To detect these obstacles, there are some range finder sensors deployed in the scene (see Fig. 19). The main parameters in this simulation are indicated in Table 1. The parameters Vmax and T are 0.5m/s and 4. In this scene, a robot moves from the initial position to a target (see Fig. 20). It can be seen that the robot’s trajectory is relatively short. During the travelling, the robot avoids all the obstacles and undetected areas successfully and keeps the given safety margin (see Fig. 21). The total length of the robot’s trajectory is 88m with the runtime of 88s. In the second scene, there are the same obstacles with the first scene. Comparing with the first scene, less range finder sensors are deployed (see Fig. 22). All the parameters, robot’s initial position and target are same as the first simulation. In this scene, a robot successfully reaches the target with a safe and relatively short trajectory in the unoccupied area (see Fig. 23). During the travelling, the robot keeps the given safety margin to the undetected areas (see Fig. 24). The total length of the robot’s trajectory is 90m with the runtime of 90s. To confirm the performance of the proposed navigation algorithm in dynamic industrial environments, we build another two scenes with moving obstacles. The moving obstacles can be walking people and other robots in the factory. In the third scene, there are four obstacles moving in the plane. To detect these obstacles, there are four range finder sensors deployed in the scene (see Fig. 25). The safety margin ds, the measurement range Rs of the sensor nodes, the parameter T and the maximum speed of obstacle Vmax are 3m, 50m, 7 and 0.5m/s in this simulation. The other parameters are the same as Table 1. In this scene, these obstacles can be detected completely by the sensor network. A robot moves towards a given target in this scene and avoids the moving obstacles (see Fig. 26). During the travelling, the robot keeps the given safety margin to the obstacles as we expect (see Fig. 27). The total length of the robot’s trajectory is 78 m with the runtime of 78s … In this subsection, a computer simulation is carried out to show the case that multiple robots are navigated by the proposed sensor network. In this simulation, five mobile robots are considered in a static environment monitored by three sensor nodes. The dimension of the scene in this simulation is 10m×10m. The safety margin is 0.7m. The simulation result is shown in Fig. 34. According to this simulation, we can see that our sensor network can be implemented in industrial multi robot systems for safe navigation. In some cases where the number of robots is more than the number of sensor nodes, our sensor network based navigation system involves a significant advantage that, com paring with traditional local navigation methods where each robot must be equipped with one sensor, the number of sensors used for navigation is less than the traditional local navigation methods and it will not change along with the changes of the number of robots … In the third experiment (see Fig. 42), we tested the proposed navigation algorithm in a dynamic environment. In this scene, some folding cartons were arranged as two static obstacles and two volunteers were walking in this scene. The volunteers’ speed were smaller than the given maximum speed Vmax, which is 0.4m/s. The parameter T is determined as 2. Other parameters for this experiment are indicated in Table 3. According to the experimental result in Fig. 43, it can be seen that the sensor network built the real-time map of the dynamic environment. The mobile robot successfully avoided both the static obstacles and the moving obstacles. Fig. 44 shows that the mobile robot was keeping the safety margin ds while travelling. The total length of the robot’s trajectory is 16.2 m with the runtime of 54s. The minimum distance from obstacles is 0.5124m which is 102.48% of the safety margin.”). wherein the plurality of machine-readable movement paths are configured to enable the plurality of navigation devices to perform real-time obstacle avoidance of enclosed environment without relying on respective onboard navigation sensors (See at least Li Page 66 “The main feature of the proposed method is that the navigation tasks for all the mobile robots in the smart factory are completely transferred and integrated into the sensor network. Different types of robots can be navigated simultaneously in the workspace by the sensor network. Each robot is only required to have a low-level path tracking controller and some basic navigation sensors, like inertial navigation sensors or odometry sensors. It does not require any robot sensor for obstacle detection and any other extra navigation algorithm. Moreover, the sensor network based navigation is more flexible in configuration than local navigation in an industrial environment with different types of robots working cooperatively. New robots can be added into the workspace directly without any specialization in navigation. Additionally, a sensor network navigates robots according to the ex tensive measurements of the environment and perform a shorter and more efficient trajectory than local navigation algorithm. Therefore, this is an efficient, safe and economic navigation system for multiple robots in a dynamic industrial workspace. Furthermore, a practical non holonomic industrial mobile robot model is considered in our method and dynamic environments with moving obstacles are supposed n our method, unlike other path planning algorithms”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Rakshit to that the at least one stationary distance measuring sensor being configured to output distance measurements by transmitting energy and converting reflected energy into a distance measure; generating, by the centralized computer, a baseline map of the enclosed environment based on a first set of measurements in the received set of sensor data indicating first distances between the at least one stationary distance measuring sensor and one or more boundaries that define the enclosed environment; detecting, by the centralized computer, at least one object in the enclosed environment based on a second set of measurements in the received set of sensor data, the detected at least one object being a physical object located in the enclosed environment; generating, by the centralized computer, an obstacle map of the enclosed environment by determining a position of the detected at least one object on the baseline map from the second set of measurements in the sensor data indicating second distances between the at least one stationary distance measuring sensor and the detected at least one object and plotting the determined position of the detected at least one object in the generated baseline map of the enclosed environment; wherein the plurality of machine-readable movement paths are configured to enable the plurality of navigation devices to perform real-time obstacle avoidance of enclosed environment without relying on respective onboard navigation sensors, as taught by Li as disclosed above, in order to ensure accurate vehicle movements (Li Page 65 “In this paper, we focus on the problem of ground industrial mobile robot navigation in dynamic cluttered industrial environments based on a range finder sensor network deployed on the floor of smart factory.”). With respect to claim 2, and similarly claims 11 and 20, Rakshit in view of Li teaches that the generated baseline map includes a plurality of boundaries that define the enclosed environment (See at least Li Page 67). With respect to claim 3, and similarly claim 12, Rakshit in view of Li teach transmitting, by the centralized computer, a respective machine-readable movement path of the plurality of machine-readable movement paths to a corresponding navigation device of the plurality of navigation devices for traversing through the enclosed environment (See at least Rakshit FIG. 4 and Paragraphs 67-69 “Shared date is collected by vehicles within the cluster. These member vehicles, moving in the same direction along a roadway or other common trajectory, will collect information during the journey including: (i) nearby vehicles, (ii) obstacles in the road, (iii) sign board information, (iv) driving directions, (v) weather related information, (vi) information obtained from IOT (internet of things) sensors on the road, and/or (vii) the condition of road. A considerable portion of this collected information will be duplicated among member vehicles in cases where each member vehicle is collecting the data from the similar sensors. Processing proceeds to step S280 where instructions mod 380 processes the travel dataset, the vehicle locations, and the travel conditions to generate driving instructions. In this example, the reference vehicle generates the travel dataset, identifies the vehicle locations within the cluster, and identifies travel conditions. This data is processed locally by the reference vehicle for making driving decisions in real time as the vehicles travel along a trajectory. The instructions mod generates instructions that will be followed by the reference vehicle as well as the member vehicles. Alternatively, the data obtained by the reference vehicle is transmitted to a remote server where the instructions module generates the driving instructions. Alternatively, the remote server identifies the vehicle locations and the travel conditions. In that way, the travel dataset and other data is combined at the remote server for generating driving instructions. In some embodiments, the travel dataset is generated by the remote server from the received shared data. In some embodiments, the travel dataset is provided to the remote server and added to the other identified information to generate driving instructions. Processing proceeds to step S285 where driving mod 385 directs the vehicles of the travel cluster to operate according to the driving instructions. In this example, the reference vehicle instructs the member vehicles with the driving instructions as well as follows the driving instructions. Alternatively, the remote server directs the operation of the reference vehicle with the driving instructions. The reference vehicle in turn sends the driving instructions to the member vehicles. Alternatively, the remote server instructs each of the vehicles within a cluster according to the driving instructions.” | With respect to claim 10, Rakshit teaches a computer system for dynamic indoor navigation, the computer system comprising: one or more processors, one or more computer-readable memories and one or more computer-readable storage media: program instructions, stored on at least one of the one or more storage media to receiving, by a centralized computer, a set of sensor data from at least one distance measuring sensor located in an enclosed environment (See at least Rakshit FIG. 4 and Paragraph 64 “Processing proceeds to step S270 where shared data mod 370 collects shared data from vehicles in the travel cluster. Shared data is data made available to the vehicle cluster by a member vehicle. In this example, shared data supporting travel decisions including location, speed, and direction of travel is collected by the reference vehicle from the member vehicles. Data collected from onboard sensors and systems including cameras, traction sensors, and location systems are provided to the reference vehicle for processing. It should be noted that shared data may also be processed locally by a member vehicle to make driving decisions not delegated to the reference vehicle or to override certain driving decisions made by the reference vehicle. Alternatively, other combinations of sensor data and system data are collected for use by the reference vehicle. Alternatively, all data collected by the member vehicles is available to the reference vehicle for processing or for delivery to a remote server for processing.”) ; program instructions, stored on at least one of the one or more storage media detecting, by the centralized computer, at least one object in the enclosed environment based on the received set of sensor data (See at least Rakshit FIG. 4 and Paragraph 67 “Shared date is collected by vehicles within the cluster. These member vehicles, moving in the same direction along a roadway or other common trajectory, will collect information during the journey including: (i) nearby vehicles, (ii) obstacles in the road, (iii) sign board information, (iv) driving directions, (v) weather related information, (vi) information obtained from IOT (internet of things) sensors on the road, and/or (vii) the condition of road. A considerable portion of this collected information will be duplicated among member vehicles in cases where each member vehicle is collecting the data from the similar sensors.”); program instructions, stored on at least one of the one or more storage media to coordinating, by the centralized computer, real-time autonomous movements of a plurality of navigation devices within the enclosed environment, based on a plurality of machine- readable movement paths corresponding to the plurality of navigation devices generated by the centralized computer based on the generated obstacle map, wherein the plurality of machine-readable movement paths are configured to enable the plurality of navigation devices to perform real-time obstacle avoidance of enclosed environment (See at least Rakshit FIG. 4 and Paragraphs 67-69 “Shared date is collected by vehicles within the cluster. These member vehicles, moving in the same direction along a roadway or other common trajectory, will collect information during the journey including: (i) nearby vehicles, (ii) obstacles in the road, (iii) sign board information, (iv) driving directions, (v) weather related information, (vi) information obtained from IOT (internet of things) sensors on the road, and/or (vii) the condition of road. A considerable portion of this collected information will be duplicated among member vehicles in cases where each member vehicle is collecting the data from the similar sensors. Processing proceeds to step S280 where instructions mod 380 processes the travel dataset, the vehicle locations, and the travel conditions to generate driving instructions. In this example, the reference vehicle generates the travel dataset, identifies the vehicle locations within the cluster, and identifies travel conditions. This data is processed locally by the reference vehicle for making driving decisions in real time as the vehicles travel along a trajectory. The instructions mod generates instructions that will be followed by the reference vehicle as well as the member vehicles. Alternatively, the data obtained by the reference vehicle is transmitted to a remote server where the instructions module generates the driving instructions. Alternatively, the remote server identifies the vehicle locations and the travel conditions. In that way, the travel dataset and other data is combined at the remote server for generating driving instructions. In some embodiments, the travel dataset is generated by the remote server from the received shared data. In some embodiments, the travel dataset is provided to the remote server and added to the other identified information to generate driving instructions. Processing proceeds to step S285 where driving mod 385 directs the vehicles of the travel cluster to operate according to the driving instructions. In this example, the reference vehicle instructs the member vehicles with the driving instructions as well as follows the driving instructions. Alternatively, the remote server directs the operation of the reference vehicle with the driving instructions. The reference vehicle in turn sends the driving instructions to the member vehicles. Alternatively, the remote server instructs each of the vehicles within a cluster according to the driving instructions.”). Rakshit fails to explicitly disclose that the at least one stationary distance measuring sensor being configured to output distance measurements by transmitting energy and converting reflected energy into a distance measure; program instructions, stored on at least one of the one or more storage media to generating, by the centralized computer, a baseline map of the enclosed environment based on a first set of measurements in the received set of sensor data indicating first distances between the at least one stationary distance measuring sensor and one or more boundaries that define the enclosed environment; program instructions, stored on at least one of the one or more storage media to detecting, by the centralized computer, at least one object in the enclosed environment based on a second set of measurements in the received set of sensor data, the detected at least one object being a physical object located in the enclosed environment; program instructions, stored on at least one of the one or more storage media to generating, by the centralized computer, an obstacle map of the enclosed environment by determining a position of the detected at least one object on the baseline map from the second set of measurements in the sensor data indicating second distances between the at least one stationary distance measuring sensor and the detected at least one object and plotting the determined position of the detected at least one object in the generated baseline map of the enclosed environment; wherein the plurality of machine-readable movement paths are configured to enable the plurality of navigation devices to perform real-time obstacle avoidance of enclosed environment without relying on respective onboard navigation sensors. Li teaches that the at least one stationary distance measuring sensor being configured to output distance measurements by transmitting energy and converting reflected energy into a distance measure (See at least Li Page 67 “In the industrial environment, there are some static and moving obstacles, like walls, other robots and moving people. The obstacles can be non-convex and their velocity can be dynamic and unknown. To detect the obstacles, a sensor network is deployed in the workspace. The sensor network consists of some range finder sensor nodes. Each node measures the distances to the nearest obstacles in different directions within a measurement range denoted by Rs (see Fig. 2). Each range finder is deployed higher than any mobile robots in the work space. It means any mobile robots on the factory floor cannot be detected by the sensor network. Furthermore, a central computer node connects to the sensor network to obtain the real-time measurements from each sensor node. The central computer also can obtain the lo cation and direction of any robots in the workspace by wireless communication. After obtaining the environment information and the location and direction of each robot, the sensor network dynamically generates a safe path for each robot according to the proposed navigation algorithm and send the paths to the robots for tracking. Remark 2.1. It should be noticed that because the height of the scanning plane of the sensor network is higher than the height of the robots, there is a potential danger that the robot may collide with obstacles which is shorter than robots. To avoid this case, the cheap and micro range sensors (e.g. sonar sensor) can be mounted on the low position of the robots to measure the minimum distance from any obstacles. Then, some local obstacle avoidance algorithms like [9,43] can be applied as the optional strategy to deal with this situation”). program instructions, stored on at least one of the one or more storage media to generating, by the centralized computer, a baseline map of the enclosed environment based on a first set of measurements in the received set of sensor data indicating first distances between the at least one stationary distance measuring sensor and one or more boundaries that define the enclosed environment (See at least Li Page 67 “With the help of the location and direction of each sensor node, the local detected areas of all the sensor nodes at any time t can be con verted to the global coordinate system, then a total detected area, which is the union of the local detected areas, is obtained.”); program instructions, stored on at least one of the one or more storage media to detecting, by the centralized computer, at least one object in the enclosed environment based on a second set of measurements in the received set of sensor data, the detected at least one object being a physical object located in the enclosed environment; program instructions, stored on at least one of the one or more storage media to generating, by the centralized computer, an obstacle map of the enclosed environment by determining a position of the detected at least one object on the baseline map from the second set of measurements in the sensor data indicating second distances between the at least one stationary distance measuring sensor and the detected at least one object and plotting the determined position of the detected at least one object in the generated baseline map of the enclosed environment (See at least Li Pages 70-80 “In the following subsection, a graph based candidate paths generation algorithm is proposed. The paths generated by the candidate paths generation algorithm are adjusted by the proposed path planner. Due to the unknown changes of the dynamic environment, the path generation and path adjustment algorithms run at each time step to provide the real-time safe path P*, which is planned according to the current measurements. It is obvious that with unknown dynamic environment, it is impossible to design a path planning algorithm to improve the cost of the final solution regarding to the future movement of the robot. However, with the proposed real-time path planning algorithm, the path is at least updated and adjusted at every time step ac cording to the current measurements. A graph search algorithm is proposed here to generate some target reaching candidate paths belonging to the different homotopy classes. Then, these candidate paths are adjusted according to the algorithm A1–A3 to search the path P*. Firstly we introduce a graph as follows … A graph denoted byG is introduced that its vertices are the target , C C, , , T robot’s position p0(x(t), y(t)), the points of (A), (A′), (B), (B′), (S) and (V) types. Its edges are the segments of the curves … 0 1 the arc of the initial circles and the segments of (AT), (AA′), (BT) and (BB′) types (see Fig. 16) … To confirm the performance of the proposed navigation algorithm in static industrial environments, we build two static scenes with same static obstacles but different deployment of the sensor nodes. In the first scene, there are some static, non-convex and irregular shaped obstacles. To detect these obstacles, there are some range finder sensors deployed in the scene (see Fig. 19). The main parameters in this simulation are indicated in Table 1. The parameters Vmax and T are 0.5m/s and 4. In this scene, a robot moves from the initial position to a target (see Fig. 20). It can be seen that the robot’s trajectory is relatively short. During the travelling, the robot avoids all the obstacles and undetected areas successfully and keeps the given safety margin (see Fig. 21). The total length of the robot’s trajectory is 88m with the runtime of 88s. In the second scene, there are the same obstacles with the first scene. Comparing with the first scene, less range finder sensors are deployed (see Fig. 22). All the parameters, robot’s initial position and target are same as the first simulation. In this scene, a robot successfully reaches the target with a safe and relatively short trajectory in the unoccupied area (see Fig. 23). During the travelling, the robot keeps the given safety margin to the undetected areas (see Fig. 24). The total length of the robot’s trajectory is 90m with the runtime of 90s. To confirm the performance of the proposed navigation algorithm in dynamic industrial environments, we build another two scenes with moving obstacles. The moving obstacles can be walking people and other robots in the factory. In the third scene, there are four obstacles moving in the plane. To detect these obstacles, there are four range finder sensors deployed in the scene (see Fig. 25). The safety margin ds, the measurement range Rs of the sensor nodes, the parameter T and the maximum speed of obstacle Vmax are 3m, 50m, 7 and 0.5m/s in this simulation. The other parameters are the same as Table 1. In this scene, these obstacles can be detected completely by the sensor network. A robot moves towards a given target in this scene and avoids the moving obstacles (see Fig. 26). During the travelling, the robot keeps the given safety margin to the obstacles as we expect (see Fig. 27). The total length of the robot’s trajectory is 78 m with the runtime of 78s … In this subsection, a computer simulation is carried out to show the case that multiple robots are navigated by the proposed sensor network. In this simulation, five mobile robots are considered in a static environment monitored by three sensor nodes. The dimension of the scene in this simulation is 10m×10m. The safety margin is 0.7m. The simulation result is shown in Fig. 34. According to this simulation, we can see that our sensor network can be implemented in industrial multi robot systems for safe navigation. In some cases where the number of robots is more than the number of sensor nodes, our sensor network based navigation system involves a significant advantage that, com paring with traditional local navigation methods where each robot must be equipped with one sensor, the number of sensors used for navigation is less than the traditional local navigation methods and it will not change along with the changes of the number of robots … In the third experiment (see Fig. 42), we tested the proposed navigation algorithm in a dynamic environment. In this scene, some folding cartons were arranged as two static obstacles and two volunteers were walking in this scene. The volunteers’ speed were smaller than the given maximum speed Vmax, which is 0.4m/s. The parameter T is determined as 2. Other parameters for this experiment are indicated in Table 3. According to the experimental result in Fig. 43, it can be seen that the sensor network built the real- time map of the dynamic environment. The mobile robot successfully avoided both the static obstacles and the moving obstacles. Fig. 44 shows that the mobile robot was keeping the safety margin ds while travelling. The total length of the robot’s trajectory is 16.2 m with the runtime of 54s. The minimum distance from obstacles is 0.5124m which is 102.48% of the safety margin.”). wherein the plurality of machine-readable movement paths are configured to enable the plurality of navigation devices to perform real-time obstacle avoidance of enclosed environment without relying on respective onboard navigation sensors (See at least Li Page 66 “The main feature of the proposed method is that the navigation tasks for all the mobile robots in the smart factory are completely transferred and integrated into the sensor network. Different types of robots can be navigated simultaneously in the workspace by the sensor network. Each robot is only required to have a low-level path tracking controller and some basic navigation sensors, like inertial navigation sensors or odometry sensors. It does not require any robot sensor for obstacle detection and any other extra navigation algorithm. Moreover, the sensor network based navigation is more flexible in configuration than local navigation in an industrial environment with different types of robots working cooperatively. New robots can be added into the workspace directly without any specialization in navigation. Additionally, a sensor network navigates robots according to the ex tensive measurements of the environment and perform a shorter and more efficient trajectory than local navigation algorithm. Therefore, this is an efficient, safe and economic navigation system for multiple robots in a dynamic industrial workspace. Furthermore, a practical non holonomic industrial mobile robot model is considered in our method and dynamic environments with moving obstacles are supposed n our method, unlike other path planning algorithms”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Rakshit to that the at least one stationary distance measuring sensor being configured to output distance measurements by transmitting energy and converting reflected energy into a distance measure; generating, by the centralized computer, a baseline map of the enclosed environment based on a first set of measurements in the received set of sensor data indicating first distances between the at least one stationary distance measuring sensor and one or more boundaries that define the enclosed environment; detecting, by the centralized computer, at least one object in the enclosed environment based on a second set of measurements in the received set of sensor data, the detected at least one object being a physical object located in the enclosed environment; generating, by the centralized computer, an obstacle map of the enclosed environment by determining a position of the detected at least one object on the baseline map from the second set of measurements in the sensor data indicating second distances between the at least one stationary distance measuring sensor and the detected at least one object and plotting the determined position of the detected at least one object in the generated baseline map of the enclosed environment; wherein the plurality of machine-readable movement paths are configured to enable the plurality of navigation devices to perform real-time obstacle avoidance of enclosed environment without relying on respective onboard navigation sensors, as taught by Li as disclosed above, in order to ensure accurate vehicle movements (Li Page 65 “In this paper, we focus on the problem of ground industrial mobile robot navigation in dynamic cluttered industrial environments based on a range finder sensor network deployed on the floor of smart factory.”). With respect to claim 19, Doh teaches a computer program product for dynamic indoor navigation, the computer program product comprising: one or more computer-readable storage media: program instructions, stored on at least one of the one or more storage media to receiving, by a centralized computer, a set of sensor data from at least one distance measuring sensor located in an enclosed environment (See at least Rakshit FIG. 4 and Paragraph 64 “Processing proceeds to step S270 where shared data mod 370 collects shared data from vehicles in the travel cluster. Shared data is data made available to the vehicle cluster by a member vehicle. In this example, shared data supporting travel decisions including location, speed, and direction of travel is collected by the reference vehicle from the member vehicles. Data collected from onboard sensors and systems including cameras, traction sensors, and location systems are provided to the reference vehicle for processing. It should be noted that shared data may also be processed locally by a member vehicle to make driving decisions not delegated to the reference vehicle or to override certain driving decisions made by the reference vehicle. Alternatively, other combinations of sensor data and system data are collected for use by the reference vehicle. Alternatively, all data collected by the member vehicles is available to the reference vehicle for processing or for delivery to a remote server for processing.”) ; program instructions, stored on at least one of the one or more storage media detecting, by the centralized computer, at least one object in the enclosed environment based on the received set of sensor data (See at least Rakshit FIG. 4 and Paragraph 67 “Shared date is collected by vehicles within the cluster. These member vehicles, moving in the same direction along a roadway or other common trajectory, will collect information during the journey including: (i) nearby vehicles, (ii) obstacles in the road, (iii) sign board information, (iv) driving directions, (v) weather related information, (vi) information obtained from IOT (internet of things) sensors on the road, and/or (vii) the condition of road. A considerable portion of this collected information will be duplicated among member vehicles in cases where each member vehicle is collecting the data from the similar sensors.”); program instructions, stored on at least one of the one or more storage media to coordinating, by the centralized computer, real-time autonomous movements of a plurality of navigation devices within the enclosed environment, based on a plurality of machine- readable movement paths corresponding to the plurality of navigation devices generated by the centralized computer based on the generated obstacle map, wherein the plurality of machine-readable movement paths are configured to enable the plurality of navigation devices to perform real-time obstacle avoidance of enclosed environment (See at least Rakshit FIG. 4 and Paragraphs 67-69 “Shared date is collected by vehicles within the cluster. These member vehicles, moving in the same direction along a roadway or other common trajectory, will collect information during the journey including: (i) nearby vehicles, (ii) obstacles in the road, (iii) sign board information, (iv) driving directions, (v) weather related information, (vi) information obtained from IOT (internet of things) sensors on the road, and/or (vii) the condition of road. A considerable portion of this collected information will be duplicated among member vehicles in cases where each member vehicle is collecting the data from the similar sensors. Processing proceeds to step S280 where instructions mod 380 processes the travel dataset, the vehicle locations, and the travel conditions to generate driving instructions. In this example, the reference vehicle generates the travel dataset, identifies the vehicle locations within the cluster, and identifies travel conditions. This data is processed locally by the reference vehicle for making driving decisions in real time as the vehicles travel along a trajectory. The instructions mod generates instructions that will be followed by the reference vehicle as well as the member vehicles. Alternatively, the data obtained by the reference vehicle is transmitted to a remote server where the instructions module generates the driving instructions. Alternatively, the remote server identifies the vehicle locations and the travel conditions. In that way, the travel dataset and other data is combined at the remote server for generating driving instructions. In some embodiments, the travel dataset is generated by the remote server from the received shared data. In some embodiments, the travel dataset is provided to the remote server and added to the other identified information to generate driving instructions. Processing proceeds to step S285 where driving mod 385 directs the vehicles of the travel cluster to operate according to the driving instructions. In this example, the reference vehicle instructs the member vehicles with the driving instructions as well as follows the driving instructions. Alternatively, the remote server directs the operation of the reference vehicle with the driving instructions. The reference vehicle in turn sends the driving instructions to the member vehicles. Alternatively, the remote server instructs each of the vehicles within a cluster according to the driving instructions.”). Rakshit fails to explicitly disclose that the at least one stationary distance measuring sensor being configured to output distance measurements by transmitting energy and converting reflected energy into a distance measure; program instructions, stored on at least one of the one or more storage media to generating, by the centralized computer, a baseline map of the enclosed environment based on a first set of measurements in the received set of sensor data indicating first distances between the at least one stationary distance measuring sensor and one or more boundaries that define the enclosed environment; program instructions, stored on at least one of the one or more storage media to detecting, by the centralized computer, at least one object in the enclosed environment based on a second set of measurements in the received set of sensor data, the detected at least one object being a physical object located in the enclosed environment; program instructions, stored on at least one of the one or more storage media to generating, by the centralized computer, an obstacle map of the enclosed environment by determining a position of the detected at least one object on the baseline map from the second set of measurements in the sensor data indicating second distances between the at least one stationary distance measuring sensor and the detected at least one object and plotting the determined position of the detected at least one object in the generated baseline map of the enclosed environment; wherein the plurality of machine-readable movement paths are configured to enable the plurality of navigation devices to perform real-time obstacle avoidance of enclosed environment without relying on respective onboard navigation sensors. Li teaches that the at least one stationary distance measuring sensor being configured to output distance measurements by transmitting energy and converting reflected energy into a distance measure (See at least Li Page 67 “In the industrial environment, there are some static and moving obstacles, like walls, other robots and moving people. The obstacles can be non-convex and their velocity can be dynamic and unknown. To detect the obstacles, a sensor network is deployed in the workspace. The sensor network consists of some range finder sensor nodes. Each node measures the distances to the nearest obstacles in different directions within a measurement range denoted by Rs (see Fig. 2). Each range finder is deployed higher than any mobile robots in the work space. It means any mobile robots on the factory floor cannot be detected by the sensor network. Furthermore, a central computer node connects to the sensor network to obtain the real-time measurements from each sensor node. The central computer also can obtain the lo cation and direction of any robots in the workspace by wireless communication. After obtaining the environment information and the location and direction of each robot, the sensor network dynamically generates a safe path for each robot according to the proposed navigation algorithm and send the paths to the robots for tracking. Remark 2.1. It should be noticed that because the height of the scanning plane of the sensor network is higher than the height of the robots, there is a potential danger that the robot may collide with obstacles which is shorter than robots. To avoid this case, the cheap and micro range sensors (e.g. sonar sensor) can be mounted on the low position of the robots to measure the minimum distance from any obstacles. Then, some local obstacle avoidance algorithms like [9,43] can be applied as the optional strategy to deal with this situation”). program instructions, stored on at least one of the one or more storage media to generating, by the centralized computer, a baseline map of the enclosed environment based on a first set of measurements in the received set of sensor data indicating first distances between the at least one stationary distance measuring sensor and one or more boundaries that define the enclosed environment (See at least Li Page 67 “With the help of the location and direction of each sensor node, the local detected areas of all the sensor nodes at any time t can be con verted to the global coordinate system, then a total detected area, which is the union of the local detected areas, is obtained.”); program instructions, stored on at least one of the one or more storage media to detecting, by the centralized computer, at least one object in the enclosed environment based on a second set of measurements in the received set of sensor data, the detected at least one object being a physical object located in the enclosed environment; program instructions, stored on at least one of the one or more storage media to generating, by the centralized computer, an obstacle map of the enclosed environment by determining a position of the detected at least one object on the baseline map from the second set of measurements in the sensor data indicating second distances between the at least one stationary distance measuring sensor and the detected at least one object and plotting the determined position of the detected at least one object in the generated baseline map of the enclosed environment (See at least Li Pages 70-80 “In the following subsection, a graph based candidate paths generation algorithm is proposed. The paths generated by the candidate paths generation algorithm are adjusted by the proposed path planner. Due to the unknown changes of the dynamic environment, the path generation and path adjustment algorithms run at each time step to provide the real-time safe path P*, which is planned according to the current measurements. It is obvious that with unknown dynamic environment, it is impossible to design a path planning algorithm to improve the cost of the final solution regarding to the future movement of the robot. However, with the proposed real-time path planning algorithm, the path is at least updated and adjusted at every time step ac cording to the current measurements. A graph search algorithm is proposed here to generate some target reaching candidate paths belonging to the different homotopy classes. Then, these candidate paths are adjusted according to the algorithm A1–A3 to search the path P*. Firstly we introduce a graph as follows … A graph denoted byG is introduced that its vertices are the target , C C, , , T robot’s position p0(x(t), y(t)), the points of (A), (A′), (B), (B′), (S) and (V) types. Its edges are the segments of the curves … 0 1 the arc of the initial circles and the segments of (AT), (AA′), (BT) and (BB′) types (see Fig. 16) … To confirm the performance of the proposed navigation algorithm in static industrial environments, we build two static scenes with same static obstacles but different deployment of the sensor nodes. In the first scene, there are some static, non-convex and irregular shaped obstacles. To detect these obstacles, there are some range finder sensors deployed in the scene (see Fig. 19). The main parameters in this simulation are indicated in Table 1. The parameters Vmax and T are 0.5m/s and 4. In this scene, a robot moves from the initial position to a target (see Fig. 20). It can be seen that the robot’s trajectory is relatively short. During the travelling, the robot avoids all the obstacles and undetected areas successfully and keeps the given safety margin (see Fig. 21). The total length of the robot’s trajectory is 88m with the runtime of 88s. In the second scene, there are the same obstacles with the first scene. Comparing with the first scene, less range finder sensors are deployed (see Fig. 22). All the parameters, robot’s initial position and target are same as the first simulation. In this scene, a robot successfully reaches the target with a safe and relatively short trajectory in the unoccupied area (see Fig. 23). During the travelling, the robot keeps the given safety margin to the undetected areas (see Fig. 24). The total length of the robot’s trajectory is 90m with the runtime of 90s. To confirm the performance of the proposed navigation algorithm in dynamic industrial environments, we build another two scenes with moving obstacles. The moving obstacles can be walking people and other robots in the factory. In the third scene, there are four obstacles moving in the plane. To detect these obstacles, there are four range finder sensors deployed in the scene (see Fig. 25). The safety margin ds, the measurement range Rs of the sensor nodes, the parameter T and the maximum speed of obstacle Vmax are 3m, 50m, 7 and 0.5m/s in this simulation. The other parameters are the same as Table 1. In this scene, these obstacles can be detected completely by the sensor network. A robot moves towards a given target in this scene and avoids the moving obstacles (see Fig. 26). During the travelling, the robot keeps the given safety margin to the obstacles as we expect (see Fig. 27). The total length of the robot’s trajectory is 78 m with the runtime of 78s … In this subsection, a computer simulation is carried out to show the case that multiple robots are navigated by the proposed sensor network. In this simulation, five mobile robots are considered in a static environment monitored by three sensor nodes. The dimension of the scene in this simulation is 10m×10m. The safety margin is 0.7m. The simulation result is shown in Fig. 34. According to this simulation, we can see that our sensor network can be implemented in industrial multi robot systems for safe navigation. In some cases where the number of robots is more than the number of sensor nodes, our sensor network based navigation system involves a significant advantage that, com paring with traditional local navigation methods where each robot must be equipped with one sensor, the number of sensors used for navigation is less than the traditional local navigation methods and it will not change along with the changes of the number of robots … In the third experiment (see Fig. 42), we tested the proposed navigation algorithm in a dynamic environment. In this scene, some folding cartons were arranged as two static obstacles and two volunteers were walking in this scene. The volunteers’ speed were smaller than the given maximum speed Vmax, which is 0.4m/s. The parameter T is determined as 2. Other parameters for this experiment are indicated in Table 3. According to the experimental result in Fig. 43, it can be seen that the sensor network built the real-time map of the dynamic environment. The mobile robot successfully avoided both the static obstacles and the moving obstacles. Fig. 44 shows that the mobile robot was keeping the safety margin ds while travelling. The total length of the robot’s trajectory is 16.2 m with the runtime of 54s. The minimum distance from obstacles is 0.5124m which is 102.48% of the safety margin.”). wherein the plurality of machine-readable movement paths are configured to enable the plurality of navigation devices to perform real-time obstacle avoidance of enclosed environment without relying on respective onboard navigation sensors (See at least Li Page 66 “The main feature of the proposed method is that the navigation tasks for all the mobile robots in the smart factory are completely transferred and integrated into the sensor network. Different types of robots can be navigated simultaneously in the workspace by the sensor network. Each robot is only required to have a low-level path tracking controller and some basic navigation sensors, like inertial navigation sensors or odometry sensors. It does not require any robot sensor for obstacle detection and any other extra navigation algorithm. Moreover, the sensor network based navigation is more flexible in configuration than local navigation in an industrial environment with different types of robots working cooperatively. New robots can be added into the workspace directly without any specialization in navigation. Additionally, a sensor network navigates robots according to the ex tensive measurements of the environment and perform a shorter and more efficient trajectory than local navigation algorithm. Therefore, this is an efficient, safe and economic navigation system for multiple robots in a dynamic industrial workspace. Furthermore, a practical non holonomic industrial mobile robot model is considered in our method and dynamic environments with moving obstacles are supposed n our method, unlike other path planning algorithms”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Rakshit to that the at least one stationary distance measuring sensor being configured to output distance measurements by transmitting energy and converting reflected energy into a distance measure; generating, by the centralized computer, a baseline map of the enclosed environment based on a first set of measurements in the received set of sensor data indicating first distances between the at least one stationary distance measuring sensor and one or more boundaries that define the enclosed environment; detecting, by the centralized computer, at least one object in the enclosed environment based on a second set of measurements in the received set of sensor data, the detected at least one object being a physical object located in the enclosed environment; generating, by the centralized computer, an obstacle map of the enclosed environment by determining a position of the detected at least one object on the baseline map from the second set of measurements in the sensor data indicating second distances between the at least one stationary distance measuring sensor and the detected at least one object and plotting the determined position of the detected at least one object in the generated baseline map of the enclosed environment; wherein the plurality of machine-readable movement paths are configured to enable the plurality of navigation devices to perform real-time obstacle avoidance of enclosed environment without relying on respective onboard navigation sensors, as taught by Li as disclosed above, in order to ensure accurate vehicle movements (Li Page 65 “In this paper, we focus on the problem of ground industrial mobile robot navigation in dynamic cluttered industrial environments based on a range finder sensor network deployed on the floor of smart factory.”) . 07-21-aia AIA Claim s 4, 6, 13, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Rakshit (US 20210109544 A1) (“Rakshit”) in view of Li (An algorithm for safe navigation of mobile robots by a sensor network in dynamic cluttered industrial environments) (“Li”) (Attached) further in view of Goldman (US 20130261964 A1) (“Goldman”) . With respect to claim 4, and similarly claim 13, Rakshit in view of Li fail to explicitly disclose that the first distances in the first set of measurements are measured from the at least one stationary distance measuring sensor to surfaces that are farthest from the at least one stationary distance measuring sensor in each direction. Goldman, however, teaches the first distances in the first set of measurements are measured from the at least one stationary distance measuring sensor to surfaces that are farthest from the at least one stationary distance measuring sensor in each direction (See at least Goldman Paragraph 173 “FIGS. 23 and 24 generally depict how route planning may be performed in one embodiment. In this example embodiment, a simplified Voronoi diagram or approximate medial-axis transform is first computed using all of the boundaries of the moveable areas within an indoor space, which creates a connected graph that generalizes all potential moving routes 2302 within that indoor area based on a topology of an indoor area. These routes may then be segmented into different branches 2304 and zones 2307 based on, for example, the closeness or adjacency of isles and walkways throughout an indoor area. Hence, the Voronoi diagram is used to calculate potential moving routes 2302 which are composed of branches 2304 of which one or more branches may comprise a zone 2307. If the indoor area is sufficiently small, it may be quite reasonable for it to have just a single zone that is comprised of all of the calculated branches of the moving routes implied by the space's associated Voronoi diagram. The main purpose for segmenting groups of branches into separate zones is to ensure the computational efficiency and practicality of calculating routes within large, complex indoor spaces.”) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Rakshit in view of Li to include that the first distances in the first set of measurements are measured from the at least one stationary distance measuring sensor to surfaces that are farthest from the at least one stationary distance measuring sensor in each direction, as taught by Goldman as disclosed above, in order to ensure accurate vehicle localization (Goldman Paragraph 9 “Various embodiments of the present disclosure solve the above problems by providing a precise and accurate indoor navigation system that combines dead-reckoning with absolute position detection”). With respect to claim 6, and similarly claim 15, Rakshit in view of Li fail to explicitly disclose comparing, by the centralized computer, the first set of measurements associated with the one or more of boundaries and the second set of measurements associated with the detected at least one object to determine an exact position of the detected at least one object in the generated obstacle map of the enclosed environment. Goldman, however, teaches comparing, by the centralized computer, the first set of measurements associated with the one or more of boundaries and the second set of measurements associated with the detected at least one object to determine an exact position of the detected at least one object in the generated obstacle map of the enclosed environment (See at least Goldman Paragraph 173 “FIGS. 23 and 24 generally depict how route planning may be performed in one embodiment. In this example embodiment, a simplified Voronoi diagram or approximate medial-axis transform is first computed using all of the boundaries of the moveable areas within an indoor space, which creates a connected graph that generalizes all potential moving routes 2302 within that indoor area based on a topology of an indoor area. These routes may then be segmented into different branches 2304 and zones 2307 based on, for example, the closeness or adjacency of isles and walkways throughout an indoor area. Hence, the Voronoi diagram is used to calculate potential moving routes 2302 which are composed of branches 2304 of which one or more branches may comprise a zone 2307. If the indoor area is sufficiently small, it may be quite reasonable for it to have just a single zone that is comprised of all of the calculated branches of the moving routes implied by the space's associated Voronoi diagram. The main purpose for segmenting groups of branches into separate zones is to ensure the computational efficiency and practicality of calculating routes within large, complex indoor spaces.”) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Rakshit in view of Li to include comparing, by the centralized computer, the first set of measurements associated with the one or more of boundaries and the second set of measurements associated with the detected at least one object to determine an exact position of the detected at least one object in the generated obstacle map of the enclosed environment, as taught by Goldman as disclosed above, in order to ensure accurate vehicle localization (Goldman Paragraph 9 “Various embodiments of the present disclosure solve the above problems by providing a precise and accurate indoor navigation system that combines dead-reckoning with absolute position detection”) . 07-21-aia AIA Claim s 7, 9, 16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Rakshit (US 20210109544 A1) (“Rakshit”) in view of Li (An algorithm for safe navigation of mobile robots by a sensor network in dynamic cluttered industrial environments) (“Li”) (Attached) further in view of Saboo (US 20180300835 A1) (“Saboo”) . With respect to claim 7, and similarly claim 16, Rakshit in view of Li teach receiving, by the centralized computer a real-time update in the received set of sensor data, wherein the received real-time update in the received set of sensor data indicates a movement of the detected at least one object in the enclosed environment (See at least Rakshit Paragraphs 67 “Shared date is collected by vehicles within the cluster. These member vehicles, moving in the same direction along a roadway or other common trajectory, will collect information during the journey including: (i) nearby vehicles, (ii) obstacles in the road, (iii) sign board information, (iv) driving directions, (v) weather related information, (vi) information obtained from IOT (internet of things) sensors on the road, and/or (vii) the condition of road. A considerable portion of this collected information will be duplicated among member vehicles in cases where each member vehicle is collecting the data from the similar sensors.”). Rakshit in view of Li fail to explicitly disclose dynamically updating, by the centralized computer, the generated obstacle map of the enclosed environment to indicate the movement of the detected at least one object. Saboo teaches dynamically updating, by the centralized computer, the generated obstacle map of the enclosed environment to indicate the movement of the detected at least one object (See at least Saboo Paragraph 84 “At block 306, the method involves, based on the received task progress data, updating the map of the plurality of robotic devices within the physical environment, where the updated map includes at least one modification to the predicted future locations of at least some of the plurality of robotic devices. The updates to the map may include updates to the entire map and/or updates to portions of the map. For instance, only a portion of a map may be updated in scenarios such as when predicted future location(s) of robotic devices may be within an area of the environment corresponding to that portion of the map, and/or when an obstacle or unidentified object is present in the area, among other possible scenario, including scenarios where updating a larger portion of the map may be unnecessary with respect to time management and/or power management.”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Rakshit in view of Li to include dynamically updating the generated obstacle map of the enclosed environment to indicate the movement of the detected at least one object, as taught by Saboo as disclosed above, in order to ensure accurate vehicle movement (Saboo Paragraph 4 “In one aspect, the present application describes a method. The method may involve determining a map of a plurality of robotic devices within a physical environment, where the map includes predicted future locations of at least some of the plurality of robotic devices.”). With respect to claim 9, and similarly claim 18, Rakshit in view of Li fail to explicitly disclose detecting the plurality of navigation devices in the enclosed environment; and transmitting, by the centralized computer, a respective machine-readable movement path to a corresponding navigation device of the detected plurality of navigation devices for traversing through the enclosed environment, wherein the respective machine-readable movement path that is transmitted is tailored for the corresponding navigation device. Saboo teaches detecting the plurality of navigation devices in the enclosed environment; and transmitting, by the centralized computer, a respective machine-readable movement path to a corresponding navigation device of the detected plurality of navigation devices for traversing through the enclosed environment, wherein the respective machine-readable movement path that is transmitted is tailored for the corresponding navigation device (See at least Saboo FIG. 3 and Paragraphs 75-76 “At block 302, the method involves causing one or more robotic devices of the plurality to perform a task comprising one or more task phases. Herein, a “task” refers to an operation that is assigned to at least one entity for that entity or entities to perform. Within example embodiments, such a task is assigned to the at least one entity by a system that monitors, governs, or otherwise manages the at least one entity in order to facilitate a performance of the task by the at least one entity. Within examples, performance of the task may be completed by at least two different types of robotic devices working in coordination to manipulate at least one object. A task (and associated task phases) involving a manipulation of an object or objects may take various forms. For example, a task may involve delivering objects directly from a starting location to a target location, or possibly transporting objects from a starting location to one or more intermediate locations before being finally delivered to the target location, handing off the objects from one robotic device to another at each intermediate location.”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Rakshit in view of Li to include detecting the plurality of navigation devices in the enclosed environment; and transmitting, by the centralized computer, a respective machine-readable movement path to a corresponding navigation device of the detected plurality of navigation devices for traversing through the enclosed environment, wherein the respective machine-readable movement path that is transmitted is tailored for the corresponding navigation device, as taught by Saboo as disclosed above, in order to ensure accurate vehicle movement (Saboo Paragraph 4 “In one aspect, the present application describes a method. The method may involve determining a map of a plurality of robotic devices within a physical environment, where the map includes predicted future locations of at least some of the plurality of robotic devices.”) . 07-21-aia AIA Claim s 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Rakshit (US 20210109544 A1) (“Rakshit”) in view of Li (An algorithm for safe navigation of mobile robots by a sensor network in dynamic cluttered industrial environments) (“Li”) (Attached) further in view of Che (US 20230255429 A1) (“Che”) . With respect to claim 8, and similarly claim 17, Rakshit in view of Li fail to explicitly disclose determining, by the centralized computer, a specification of the corresponding navigation device traversing through the enclosed environment; and tailoring, by the centralized computer, the respective machine-readable movement path transmitted to the corresponding navigation device based on the determined specification of the corresponding navigation device. Che, however, teaches determining, by the centralized computer, a specification of the corresponding navigation device traversing through the enclosed environment; and tailoring, by the centralized computer, the respective machine-readable movement path transmitted to the corresponding navigation device based on the determined specification of the corresponding navigation device (See at least Che Paragraphs 67-69 “Step 14, the robot is controlled to clean the target right angle corner point. In this embodiment of the disclosure, the robot is controlled to clean the target right angle corner point includes: position information of the target right angle corner point in the environmental map is acquired; a target location is determined according to the position information and a shape parameter of the robot; and the robot is controlled to move to the target location, so as to clean the target right angle corner point. As an example, a position 2 times the body length of the robot from the right angle corner point is used as the target location. Then, the robot is controlled to move to the target location, and can clean, according to a right-handed rotation cleaning mode, an area in which the right angle corner point is located. As shown in FIG. 2 , for example, if a white area is located in a first quadrant, a cleaning robot is moved to the target location, and then adjusts an angle to cause the robot to face the corner, so as to perform the cleaning operation.”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Rakshit in view of Li to include determining, by the centralized computer, a specification of the corresponding navigation device traversing through the enclosed environment; and tailoring, by the centralized computer, the respective machine-readable movement path transmitted to the corresponding navigation device based on the determined specification of the corresponding navigation device, as taught by Che as disclosed above, in order to ensure safe vehicle movement (Che Paragraph 2 “The disclosure relates to the field of intelligent robot control, in particular, to a method and an apparatus for controlling a robot, an electronic device, and a storage medium.”). Conclusion 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to IBRAHIM ABDOALATIF ALSOMAIRY whose telephone number is (571)272-5653. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /IBRAHIM ABDOALATIF ALSOMAIRY/Examiner, Art Unit 3667 /KENNETH J MALKOWSKI/Primary Examiner, Art Unit 3667 Application/Control Number: 18/495,843 Page 2 Art Unit: 3667 Application/Control Number: 18/495,843 Page 3 Art Unit: 3667 Application/Control Number: 18/495,843 Page 4 Art Unit: 3667 Application/Control Number: 18/495,843 Page 5 Art Unit: 3667 Application/Control Number: 18/495,843 Page 6 Art Unit: 3667 Application/Control Number: 18/495,843 Page 7 Art Unit: 3667 Application/Control Number: 18/495,843 Page 8 Art Unit: 3667 Application/Control Number: 18/495,843 Page 9 Art Unit: 3667 Application/Control Number: 18/495,843 Page 10 Art Unit: 3667 Application/Control Number: 18/495,843 Page 11 Art Unit: 3667 Application/Control Number: 18/495,843 Page 12 Art Unit: 3667 Application/Control Number: 18/495,843 Page 13 Art Unit: 3667 Application/Control Number: 18/495,843 Page 14 Art Unit: 3667 Application/Control Number: 18/495,843 Page 15 Art Unit: 3667 Application/Control Number: 18/495,843 Page 16 Art Unit: 3667 Application/Control Number: 18/495,843 Page 17 Art Unit: 3667 Application/Control Number: 18/495,843 Page 18 Art Unit: 3667 Application/Control Number: 18/495,843 Page 19 Art Unit: 3667 Application/Control Number: 18/495,843 Page 20 Art Unit: 3667 Application/Control Number: 18/495,843 Page 21 Art Unit: 3667 Application/Control Number: 18/495,843 Page 22 Art Unit: 3667 Application/Control Number: 18/495,843 Page 23 Art Unit: 3667 Application/Control Number: 18/495,843 Page 24 Art Unit: 3667 Application/Control Number: 18/495,843 Page 25 Art Unit: 3667 Application/Control Number: 18/495,843 Page 26 Art Unit: 3667 Application/Control Number: 18/495,843 Page 27 Art Unit: 3667 Application/Control Number: 18/495,843 Page 28 Art Unit: 3667 Application/Control Number: 18/495,843 Page 29 Art Unit: 3667 Application/Control Number: 18/495,843 Page 30 Art Unit: 3667 Application/Control Number: 18/495,843 Page 31 Art Unit: 3667 Application/Control Number: 18/495,843 Page 32 Art Unit: 3667 Application/Control Number: 18/495,843 Page 33 Art Unit: 3667 Application/Control Number: 18/495,843 Page 34 Art Unit: 3667 Application/Control Number: 18/495,843 Page 35 Art Unit: 3667 Application/Control Number: 18/495,843 Page 36 Art Unit: 3667 Application/Control Number: 18/495,843 Page 37 Art Unit: 3667 Application/Control Number: 18/495,843 Page 38 Art Unit: 3667 Application/Control Number: 18/495,843 Page 39 Art Unit: 3667 Application/Control Number: 18/495,843 Page 40 Art Unit: 3667
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