Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Detailed Action
Claims 21-23,25,27-32,34-38 and 40-44 are currently pending.
Request for Continued Examination
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/13/2026 has been entered.
Response to Amendment
This action is in response to the RCE filled on 2/13/2026. The amendment has been entered. Claims 21, 30, and 40 have been amended, and claims 1-20,24,26,33, and 39 have been cancelled previously. Claims 21-23,25,27-32,34-38 and 40-44 are pending, with claims 21, 30 and 40 being independent in the instant application.
Response to Arguments
Applicant's Arguments/Remarks filed on 2/13/2026 on page 9-14 regarding 35 U.S.C. 103 rejections have been fully considered but found unpersuasive in view of the amended claims and presented Arguments/Remarks by the Applicant.
Applicants stated on page 11 (2nd para) in Arguments/Remarks: “the office does not point to any teaches related to probabilistic modeling of object existence uncertainty across multiple flights or maintaining such uncertainty metrics in a stored three- dimensional representation.”
Examiner respectfully disagrees with this argument/remark. In response to applicant’s argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies i.e., "probabilistic modeling of object existence uncertainty" is not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. There is no claim limitation/language "probabilistic modeling of object existence uncertainty" recited in the claim(s) 21. Therefore, the argument/remark above is unpersuasive.
Applicants stated on page 12 (2nd para) in Arguments/Remarks: “The office alleges that Levy discloses assigning a flight risk score to a zone or region, but a zone-level score differs materially from a risk confidence score computed for individual portions of a flight path. However, Applicant does not believe that the Office demonstrates where Levy discloses subdividing a flight path into discrete portions and computing separate risk confidence scores for each portion based on object-specific existence accuracy or boundary uncertainty.”
Examiner respectfully disagrees with the argument/remark above. In response to applicant’s argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies i.e., “assigning a flight risk score to a zone or region, subdividing a flight path into discrete portions and computing separate risk confidence scores for each portion based on object-specific existence accuracy or boundary uncertainty” is not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. There is no claim limitation/language “assigning a flight risk score to a zone or region, subdividing a flight path into discrete portions and computing separate risk confidence scores for each portion based on object-specific existence accuracy or boundary uncertainty” recited in the 21. The argument “assigning a flight risk score to a zone or region, subdividing a flight path into discrete portions” is completely a new scope which has not been claimed earlier and also not present in current amendments of claim 1. Rather, Applicants claimed “determining a risk confidence score for individual portions of a flight path based on object existence accuracies” in claim 21. Therefore, the argument/remark above is unpersuasive.
Applicants stated on page 13 (1st para) in Arguments/Remarks: “Applicant does not believe that the Office demonstrates where the cited art discloses modifying a planned flight path based on pre-flight risk confidence scores derived from object existence accuracies and boundary uncertainty.”
Examiner respectfully disagrees with the argument/remark above. In response to applicant’s argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies i.e., “modifying a planned flight path based on pre-flight risk confidence scores derived from object existence accuracies and boundary uncertainty There is no claim limitation/language “modifying a planned flight path based on pre-flight risk confidence scores derived from object existence accuracies and boundary uncertainty” recited in the claim 21. The argument “modifying a planned flight path based on pre-flight risk confidence scores derived from object existence accuracies and boundary uncertainty” is completely a new scope which has not been claimed earlier and also not present in current amendments of claim 1. Rather, Applicants claimed “override the flight path based on the determined predicted risk so that the predicted risk is lowered as the unmanned aerial vehicle travels along the path” in claim 21. Therefore, the argument/remark above is unpersuasive. Accordingly, a new ground of rejections is necessitated by Applicant's claim amendments. Therefore, the previous rejection regarding 35 U.S.C. 103 are being amended in this current office action. (See analysis below Claim Rejections-35 U.S.C. 103).
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution.
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 (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.
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 non-obviousness.
7. Claims 21-23, 27-32, 34,36-38 and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Levy et al. (Pub. No.US2016/0140851A1) (hereinafter Levy) (submitted in IDS dated 8/26/2021), in view of Herwitz (Patent No. US7706979B1), and further in view of Liu et al. (Pub. No. US2016/0070265A1).
Regarding claim 21, Levy teaches a system comprising: an unmanned aerial vehicle; a representation component; one or more physical processors configured by machine readable instructions to: (Levy disclosed in page 3 para [0049]: “The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.” In page 4 para [0062]: “Drone navigation system 1102 includes a drone module … Examples of drones 1106 include autonomous unmanned vehicles, which may include the ability to be manually remotely controlled by a pilot, …”. In page 6 para [0090]: “Zones containing static object(s) may be assigned a static score. The static score may represent the risk of flight through the zone, to keep the drone away from the static object. … The score may represent a low risk of flight at 100 meters and further away from the buildings, …”. This disclosure reads limitation “representation component” (e.g., static score represents the risk of flight through the zone (to keep the drone away from the static object); score represents a low risk of flight at 100 meters).
Levy teaches obtain a previously stored three-dimensional representation of user-selected a location, and reflecting a presence of objects; (Levy disclosed in page 5 para [0069-0070]: “a real time monitoring station 122 in communication with control server 1108 enables human operators within the control center to view current and/or planned status, issue commands, and/or override automatic functions. Station 122 may include a display and/or user interface. The method of FIG. 2 generates a three dimensional flight risk map. The map may cover the environmental air space available for drone flying. The map includes zones, each zone associated with risk of flying the drone through the respective zone.” In para [0073-0075]: “At 202, data to generate the geographical airspace is received. Data may be locally stored, and/or obtained by accessing one or more remote servers. … Topography data describing the terrain of the area designated to the respective central control server, for example, a county, a city, and/or certain dimensions on the group. The topography data includes, for example, data of mountains, canyons, hills, Valleys, and rives. The topography data may be obtained from a topography database 101, …”.
The disclosure above “a three-dimensional flight risk map generated in FIG. 2 where the map covers the environmental air space available for drone flying. The map includes zones, each zone associated with risk of flying the drone” corresponds to the claim limitation “obtain a three-dimensional representation of a user-selected location, which is previously stored as “topography data obtained from a topography database”. Further, the topography data includes, data of mountains, canyons, hills, Valleys, and rives, thus teaches the claim limitation “reflecting a presence of objects.”).
Levy teaches the three-dimensional representation being derived from a plurality of depth maps of the user-selected location generated during previous unmanned aerial vehicle flights, (Levy disclosed in page 3 para [0040-0041]: “An aspect of some embodiments of the present invention relates to systems and methods for safe navigation of a drone through a geographical air space, the navigation of the drone based on flight through regions of the geographical air space designated as having an acceptable flight risk. A flight risk map associates a representation of the geographical air space with local flight risk scores based on the risk of flying the drone through the local air space. … The systems and/or methods prevent or reduce the risk of drones crashing into other objects (e.g., planes, other drones, buildings, hills, and power lines) by proactively preventing from the drones from flying above certain areas using the flight risk map ... A new flight plan is dynamically generated based on the dynamically updated flight risk map, to prevent the drone from flying through local air spaces with dynamically changed risk profiles.” In page 5 para [0070]: “The method of FIG. 2 generates a three dimensional flight risk map. The map may cover the environmental air space available for drone flying. The map includes zones, each zone associated with risk of flying the drone through the respective zone.”
It has been discussed above that 3D depth map as “risk map” is derived/generated during previous unmanned aerial vehicle flights, because “a new flight plan is dynamically generated based on the dynamically updated flight risk map, to prevent the drone from flying through local air spaces with dynamically changed risk profiles” as discussed above).
Even Levy teaches the claimed element “representation component” (e.g., static score represents the risk of flight through the zone, corresponds to “representation component” disclosed in page 6 para [0090]), however, Levy doesn’t explicitly teach the limitation “the representation component enables a user to move and/or scroll the three-dimensional representation including displaying different portions of the visual information of the three-dimensional representation and an allow a user to zoom in and/or out of a particular location within the three- dimensional representation to display more or less detail to the user.”
wherein Herwitz teaches the representation component enables a user to move and/or scroll the three-dimensional representation including displaying different portions of the visual information of the three-dimensional representation and an allow a user to zoom in and/or out of a particular location within the three- dimensional representation to display more or less detail to the user. (Examiner notes that the claim language includes two optional embodiments, a first embodiment “a user to move”, or" a second embodiment “a user to scroll”. Since "and/or" is interpreted as at least one of, only one of the two embodiments need to be taught by the reference.
Herwitz disclosed in col. 7 lines 49-58: “The system is comprised of an expanded map library of georeferenced 2D and 3D background maps. The system allows the UAV pilot to shift back-and-forth between different background map types … while the system is in operation. A selectable zoom factor associated with the graphic displays provides the UAV pilot with different fields of view (e.g., ranging from 10 to 100 nautical miles in diameter). Optionally, the zoom factor monotonically increases as a function of the relative velocity magnitude …”. In col. 8 lines 3-12: “In the 3D graphic display and in the nadir view plane of the 2D graphic display, GPS time and the calendar date are shown as UAV and AV data are being displayed. These time-date stamps remain embedded in the system log files that store the UAV and AV data from each UAV flight. UAV and AV data stored in the system log files are available for replaying and for the analysis of past UAV missions. The replays are run at different rates (faster or slower), and paused for closer viewing and still-frame storage.”
The disclosure above “system allows the UAV pilot to shift back-and-forth between different background map types” teaches the claim limitation “enable a user to move the three-dimensional representation including displaying different portions of the three-dimensional representation” and “3D background map” corresponds to “representation component”. Further, the disclosure above “A selectable zoom factor associated with the graphic displays provides the UAV pilot with different fields of view e.g., ranging from 10 to 100 nautical miles in diameter” teaches the limitation “the ability to zoom in and/or out of a particular location within the three-dimensional representation”. The disclosure “UAV and AV data stored in the system log files are available for replaying and the replays run at different rates, and paused for closer viewing” corresponds to the limitation “representation to display more or less detail to the user”. Therefore, Herwitz teaches the whole limitation).
Levy and Herwitz are analogous art because they are related in using environmental maps to allow to make collision-free flights in autonomous unmanned vehicles. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Levy and Herwitz before him or her, to modify the providing or viewing the continuous anti-collision monitoring or a contingency plan (emergency instructions) for each drone of Levy, to include displaying the 3D background map in graphic displays provides the UAV pilot with different fields of view (i.e., to have better display) using selectable zoom factor of Herwitz. The suggestion/motivation for doing so would have been obvious by Herwitz because “The invention is a sense-and-avoid display system that obtains position and/or Velocity data for an unmanned aerial vehicle (UAV) and other aerial vehicles (AVs). The UAV and AV data are used to compute and display: present and projected future positions of UAV and other AVs; time and distance of closest approach between UAV and an AV; and recommended change in UAV's flight path and/or flight speed for conflict avoidance. This information is provided to the UAV pilot in 2D and 3D graphic displays.” (Herwitz disclosed in ‘Abstract’).
Neither Levy nor Herwitz teaches the limitations “a sensor configured to generate an output signal including sensor control information that conveys visual information, including objects, within a field of view of the unmanned aerial vehicle; reflecting object existence accuracies for each individual object wherein the object existence accuracies provide information about accuracy of existence of boundaries of each of the individual object; obtain a flight path for a future unmanned aerial flight of the unmanned aerial vehicle within the previously stored three-dimensional representation of the user-selected location; associate the sensor control information with the flight path; determine predicted risk for individual portions of the flight path based upon risk parameters and the sensor control information; override the flight path based on the determined predicted risk so that as the unmanned aerial vehicle travels along the individual portions of the flight path, the predicted risk of the unmanned aerial vehicle is lowered; generate a notification that indicates the individual portions of the flight path have a higher predicted risk than other individual portions of the flight path; predict a new flight path for the unmanned aerial vehicle based upon a position of the unmanned aerial vehicle and user-selected points within the three-dimensional representation; determine an updated predicted risk based upon the objects at or near the position of the unmanned aerial vehicle; and autonomously fly the unmanned aerial vehicle along the new flight path;”
Liu teaches a sensor configured to generate an output signal including sensor control information that conveys visual information, including objects, within a field of view of the unmanned aerial vehicle; (Liu disclosed in page 9 para [0100]: “In step 360, first sensing data is received from one or more vision sensors, the first sensing data including depth information for the environment. … The vision sensors can be carried by the UAV, such as by a UAV vehicle body. In embodiments where multiple vision sensors are used, each sensor can be located on a different portion of the UAV, and the disparity between the image data collected by each sensor can be used to provide depth information for the environment. Depth information can be used herein to refer to information regarding distances of one or more objects from the UAV and/or sensor. In embodiments where a single vision sensor is used, depth information can be obtained by capturing image data for a plurality of different positions and orientations of the vision sensor, and then using suitable image analysis techniques (e.g., structure from motion) to reconstruct the depth information.”
The disclosures above “vision sensors can be carried by the UAV; the image data collected by each sensor can be used to provide depth information for the environment and depth information can be used to refer to information regarding distances of one or more objects from the UAV and/or sensor” correspond to claim limitation “sensor configured to generate an output signal including sensor control information that conveys visual information, including objects, within a field of view of the unmanned aerial vehicle”).
Liu teaches reflecting object existence accuracies for each individual object, wherein the object existence accuracies provide information about accuracy of existence of boundaries of each of the individual object; (Examiner would construe the claim element “object existence accuracies” relates to “uncertainty may exist” as per Specification para [0033], is possibly an uncertainty or confidence bound.
Liu disclosed in page 12 para [0118]: “In step 540, the environmental map is used to detect one or more obstacles situated in the portion of the environment. … In some embodiments, if the environmental map is an occupancy grid map, obstacles can be identified by detecting volumes in the occupancy grid map that are continuously occupied. The obstacle detection results may provide information regarding the position, orientation, size, proximity, and/or type of each obstacle, as well as corresponding confidence information for the result. The map can be analyzed to identify obstacles that pose a collision risk to the UAV (e.g., are positioned along or near the flight path”).
Liu teaches obtain a flight path for a future unmanned aerial flight of the unmanned aerial vehicle within the previously stored three-dimensional representation of the user-selected location; (Liu disclosed in page 10 para [0103]: “The depth information associated with each set of sensing data can be spatially aligned and combined (e.g., using suitable sensor fusion methods such as Kalman filtering) in order to generate a map including depth information (e.g., a 3D environmental representation such as an occupancy grid map), …”. Further, it has been discussed in page 11 para [0109] that the UAV can be navigated based on obstacle occupancy information represented in the final environmental map in order to avoid collisions. The UAV can be navigated by a user, by an automated control system. Any person having skills in the art would understand that 3D environmental map/grid map corresponds to “three-dimensional representation” of flight path for UAV is obtained by user-selected location.
In page 12 para [0118-0119]: “In step 540, the environmental map is used to detect one or more obstacles situated in the portion of the environment. … In step 550, a second signal is generated using the environmental map to cause to UAV to navigate so as to avoid the one or more obstacles. The environmental map can be analyzed in order to determine the location of the one or more obstacles relative to the UAV, as well as the location of any unobstructed spaces through which the UAV can move in order to avoid colliding with the obstacle. The second signal can thus provide appropriate control signals (e.g., for the propulsion system of the UAV) to cause the UAV to navigate through the unobstructed spaces. In embodiments where the UAV is navigated according to a flight path, the flight path can be modified based on the environmental map so as to avoid the one or more obstacles and the UAV can be navigated according to the modified flight path.”
Therefore, the above disclosures teach the whole limitation).
Liu teaches associate the sensor control information with the flight path; (Liu disclosed in page 15 para [0146]: “In step 924, the current position of the UAV is determined using any of the approaches described herein, such as using multi-sensor fusion and/or environmental mapping. … The flight path can determined based on any suitable criteria. For example, the flight path can be configured to avoid environmental obstacles. … For instance, the flight path can specify a series of positions and/or orientations for the UAV, and suitable control signals can be generated and transmitted to the UAV propulsion systems so as to cause the UAV to assume the specified position and/or orientations.).
Liu teaches determine predicted risk for individual portions of the flight path based upon risk parameters and the sensor control information; (Liu disclosed in page 11 para [0109]: “In step 450, the first and second environmental maps are combined, thereby generating a final environmental map including occupancy information for the environment. … Subsequently, the UAV can be navigated within the environment based on the final environmental map. For instance, the UAV can be navigated based at least in part on obstacle occupancy information represented in the final environmental map in order to avoid collisions. The UAV can be navigated by a user, by an automated control system,” In page 15 para [0146]: “In step 924, the current position of the UAV is determined using any of the approaches described herein, such as using multi-sensor fusion and/or environmental mapping. … The flight path can determined based on any suitable criteria. For example, the flight path can be configured to avoid environmental obstacles. … For instance, the flight path can specify a series of positions and/or orientations for the UAV, and suitable control signals can be generated and transmitted to the UAV propulsion systems so as to cause the UAV to assume the specified position and/or orientations.”).
Liu teaches override the flight path based on the determined predicted risk so that as the unmanned aerial vehicle travels along the individual portions of the flight path, the predicted risk of the unmanned aerial vehicle is lowered; (Liu disclosed in page 13 para [0126-0127]: “In some embodiments, once the UAV has received an instruction to auto-return, the UAV can use the environmental map to determine its current location, the spatial relationship between the current and initial locations, and/or the locations of any environmental obstacles. Based on the obstacle occupancy information in the environmental map, the UAV can then determine an appropriate path (e.g., a flight path) to navigate from the current location to the initial location. Various approaches can be used to determine a suitable path for the UAV. For example, the path can be determined using an environmental map such as a topology map, … the path can be configured to avoid one or more environmental obstacles that may obstruct the flight of the UAV. Alternatively, or in combination, the path may be the shortest path between the current and initial locations. … a plurality of potential paths can be generated, and one path selected from the plurality based on suitable criteria (e.g., minimizing total flight time, … minimizing number of obstacles encountered, maintaining a predetermined distance from obstacles, … etc.). Flight path information for the UAV (e.g., previously traveled paths, potential paths, selected paths) may be included in the environmental map. … In some embodiments, as the UAV is navigating along the path, it may detect one or more obstacles along the path that obstruct its flight. In such situations, the path can be modified to avoid the obstacle so as to permit the UAV to continue navigating to the initial location, …”.
The disclosure above “based on the obstacle occupancy information in the environmental map, the UAV can then determine an appropriate path e.g., a flight path to navigate from the current location to the initial location; the path can be determined using an environmental map such as a topology map; the path can be configured to avoid one or more environmental obstacles that may obstruct the flight of the UAV” correspond to claim limitation “override the flight path based on the determined predicted risk so that as the unmanned aerial vehicle travels along the individual portions of the flight path”. Further, the disclosure “a plurality of potential paths can be generated, and one path selected from the plurality based on suitable criteria e.g., minimizing number of obstacles encountered, maintaining a predetermined distance from obstacles” correspond to claim limitation “the predicted risk of the unmanned aerial vehicle is lowered”).
Liu teaches generate a notification that indicates the individual portions of the flight path have a higher predicted risk than other individual portions of the flight path; (Liu disclosed in page 12 para [0118]: “the environmental map is used to detect one or more obstacles situated in the portion of the environment. … Suitable machine learning algorithms can be implemented to perform obstacle detection. In some embodiments, if the environmental map is an occupancy grid map, obstacles can be identified by detecting volumes in the occupancy grid map that are continuously occupied. … The map can be analyzed to identify obstacles that pose a collision risk to the UAV (e.g., are positioned along or near the flight path).” In page 12-13 para [0125]: “an environmental map representative of at least a portion of the environment is generated based on the sensing data. … The resultant map can be provided in any suitable format and may include information pertaining to obstacle occupancy (e.g., an obstacle grid map).”
The disclosure above “the environmental map is used to detect one or more obstacles situated in the portion of the environment; the environmental map is an occupancy grid map; obstacles can be identified by detecting volumes in the occupancy grid map that are continuously occupied” corresponds to the limitation “the individual portions of the flight path have a higher predicted risk than other individual portions of the flight path”. Further, the disclosure “an environmental map representative of a portion of the environment is generated based on the sensing data; the resultant map provided include information pertaining to obstacle occupancy (e.g., an obstacle grid map” corresponds to claim limitation “generate notification that indicates the predicted risk for individual portions of the flight path”).
Liu teaches predict a new flight path for the unmanned aerial vehicle based upon a position of the unmanned aerial vehicle and user-selected points within the three-dimensional representation; (Liu disclosed in page 15 para [0146]: “In step 924, the current position of the UAV is determined using any of the approaches described herein, such as using multi-sensor fusion and/or environmental mapping. In step 926, the topology map position corresponding to the current UAV position is determined, thereby locating the UAV relative to the environmental information represented in the topology map. Based on the topology map, a flight path from the current position to the target position is determined in step 926. … For example, the flight path can be configured to avoid environmental obstacles. As another example, the flight path can be configured to minimize the total distance traveled by the UAV to reach the target position. Subsequently, in step 928, the UAV flight controls are adjusted based on the determined flight path in order to cause the UAV to move along the flight path. For instance, the flight path can specify a series of positions and/or orientations for the UAV, and suitable control signals can be generated and transmitted to the UAV propulsion systems …”. This disclosure corresponds to claim limitation “predict a new flight path for the unmanned aerial vehicle based upon a position of the unmanned aerial vehicle”.
In page 10 para [0103]: “The depth information associated with each set of sensing data can be spatially aligned and combined (e.g., using suitable sensor fusion methods such as Kalman filtering) in order to generate a map including depth information (e.g., a 3D environmental representation such as an occupancy grid map), …”. Further, it has been discussed in page 11 para [0109] that the UAV can be navigated based on obstacle occupancy information represented in the final environmental map in order to avoid collisions. The UAV can be navigated by a user, by an automated control system. Therefore, any person having skills in the art would understand that 3D environmental map/grid map corresponds to “three-dimensional representation” of flight path for UAV is predicted by user-selected points (positions)).
Liu teaches determine an updated predicted risk based upon the objects at or near the position of the unmanned aerial vehicle; (Liu disclosed in page 15 para [0146-0147]: “in step 928, the UAV flight controls are adjusted based on the determined flight path in order to cause the UAV to move along the flight path. For instance, the flight path can specify a series of positions and/or orientations for the UAV, and suitable control signals can be generated and transmitted to the UAV propulsion systems so as to cause the UAV to assume the specified position and/or orientations. As the UAV moves towards the target position, it can detect obstacles and perform obstacle avoidance maneuvers as appropriate, as depicted in steps 930 and 932. … Optionally, obstacle detection and/or avoidance can be based on the generated topology map. For example, once an obstacle has been identified, the environmental information represented in the topology map can be used to determine suitable modifications to the flight path that avert potential collisions.”).
and Liu teaches autonomously fly the unmanned aerial vehicle along the new flight path; (Liu disclosed in page 12 para [0119-0120]: “In embodiments where the UAV is navigated according to a flight path, the flight path can be modified based on the environmental map so as to avoid the one or more obstacles and the UAV can be navigated according to the modified flight path. The flight path modifications can direct the UAV to traverse only through unobstructed spaces. For instance, the flight path can be modified so as to cause the UAV to fly around the obstacle (e.g., above, below, or to the side), fly away from the obstacle, or maintain a specified distance from the obstacle. In situations where multiple modified flight paths are possible, a preferred flight path can be selected based on any suitable criteria, … In another exemplary application of multi-sensor fusion for UAV operation, the embodiments presented herein can be implemented as part of an “auto-return functionality, in which the UAV will automatically navigate from the current location to a “home” location under certain circumstances. … The home location can be determined automatically or specified by the user.”).
Levy, Herwitz, and Liu are analogous art because they are related in using environmental maps to allow to make collision-free flights in autonomous unmanned vehicles. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Levy, Herwitz and Liu before him or her, to modify the risk parameters related to the flight safety/confidence score based on the presence of object along the flight path of Levy, to include determining predicted risk for the flight path based on position of UAV and sensor control information Liu, in order to achieve the result or claimed invention “autonomously fly the UAV along the new flight path”. The suggestion/motivation for doing so would have been obvious by Liu because “In some embodiments, a plurality of potential paths can be generated, and one path selected from the plurality based on suitable criteria (e.g., minimizing total flight time, minimizing total flight distance, minimizing energy expenditure, minimizing number of obstacles encountered, maintaining a predetermined distance from obstacles, maintaining a predetermined altitude range, etc.). Flight path information for the UAV (e.g., previously traveled paths, potential paths, selected paths) may be included in the environmental map.” (Liu disclosed in page 13 para [0126]).
Regarding claim 22, Levy, Herwitz and Liu teach the system of claim 21, wherein Levy teaches the three-dimensional representation is derived from depth maps of the user-selected location generated during previous unmanned aerial flights. (Levy disclosed in page 3 para [0040-0041]: “An aspect of some embodiments of the present invention relates to systems and methods for safe navigation of a drone through a geographical air space, the navigation of the drone based on flight through regions of the geographical air space designated as having an acceptable flight risk. A flight risk map associates a representation of the geographical air space with local flight risk scores based on the risk of flying the drone through the local air space. … The systems and/or methods prevent or reduce the risk of drones crashing into other objects (e.g., planes, other drones, buildings, hills, and power lines) by proactively preventing from the drones from flying above certain areas using the flight risk map ... A new flight plan is dynamically generated based on the dynamically updated flight risk map, to prevent the drone from flying through local air spaces with dynamically changed risk profiles.” In page 5 para [0070]: “The method of FIG. 2 generates a three dimensional flight risk map. The map may cover the environmental air space available for drone flying. The map includes zones, each zone associated with risk of flying the drone through the respective zone.”
It has been discussed above that 3D depth map as “risk map” is derived/generated during previous unmanned aerial vehicle flights, because “a new flight plan is dynamically generated based on the dynamically updated flight risk map, to prevent the drone from flying through local air spaces with dynamically changed risk profiles” as discussed above).
Regarding claim 23, Levy, Herwitz and Liu teach the system of claim 21, however, Levy, and Herwitz do not explicitly teach the limitation “the object existence accuracies provide the information about the accuracy of existence of each individual object within the user-selected location includes information about accuracy of boundaries of each individual object within the user-selected location”.
wherein Liu teaches the object existence accuracies provide the information about the accuracy of existence of each individual object within the user-selected location includes information about accuracy of boundaries of each individual object within the user-selected location. (Liu disclosed in page 12 para [0118]: “In step 540, the environmental map is used to detect one or more obstacles situated in the portion of the environment. … In some embodiments, if the environmental map is an occupancy grid map, obstacles can be identified by detecting volumes in the occupancy grid map that are continuously occupied. The obstacle detection results may provide information regarding the position, orientation, size, proximity, and/or type of each obstacle, as well as corresponding confidence information for the result. The map can be analyzed to identify obstacles that pose a collision risk to the UAV (e.g., are positioned along or near the flight path”
In page 10 para [0103]: “The depth information associated with each set of sensing data can be spatially aligned and combined (e.g., using suitable sensor fusion methods such as Kalman filtering) in order to generate a map including depth information (e.g., a 3D environmental representation such as an occupancy grid map), …”. Further, it has been discussed in page 11 para [0109] that the UAV can be navigated based on obstacle occupancy information represented in the final environmental map in order to avoid collisions. The UAV can be navigated by a user, by an automated control system. Any person having skills in the art would understand that “three-dimensional representation” of flight path for UAV is obtained by user-selected location).
Levy, Herwitz, and Liu are analogous art because they are related in using environmental maps to allow to make collision-free flights in autonomous unmanned vehicles. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Levy, Herwitz and Liu before him or her, to modify the risk parameters related to the flight safety/confidence score based on the presence of object along the flight path of Levy, to include determining predicted risk for the flight path based on position of UAV and sensor control information Liu, in order to achieve the result or claimed invention “autonomously fly the UAV along the new flight path”. The suggestion/motivation for doing so would have been obvious by Liu because “In some embodiments, a plurality of potential paths can be generated, and one path selected from the plurality based on suitable criteria (e.g., minimizing total flight time, minimizing total flight distance, minimizing energy expenditure, minimizing number of obstacles encountered, maintaining a predetermined distance from obstacles, maintaining a predetermined altitude range, etc.). Flight path information for the UAV (e.g., previously traveled paths, potential paths, selected paths) may be included in the environmental map.” (Liu disclosed in page 13 para [0126]).
Regarding claim 27, Levy, Herwitz and Liu teach the system of claim 21, wherein Levy teaches the predicted risk for a given portion of the flight path reflects likelihood of experiencing a collision with one or more of the objects at or near the given portion of the flight path. (Levy disclosed in page 4 para [0057-0058]: “Reference is now made to FIG. 1, which is a schematic diagram depicting a drone navigation system … The drone navigation system navigates a drone through a geographical air space containing high fly risk zones, by navigating the drone through low fly risk zones. The high and low flight risks may be predefined, for example, by the drone operator, based on local policy, and/or based on government policy. Low risk zones may be defined based on an acceptable risk of the drone crashing and/or causing damage to other property. High risk zones may be defined based on an unacceptable risk of the drone crashing and/or causing damage to other property. The low and high risk may be defined based on the properties of the air zone relative, ...”. The disclosure above “drone navigation system navigates a drone through a geographical air space containing high fly risk zones, by navigating the drone through low fly risk zones. The high and low flight risks may be predefined based on local policy, and/or based on government policy. The low and high risk may be defined based on the properties of the air zone relative” corresponds to the claim limitation “predicted risk for a given portion of the flight path reflects experiencing a collision with one or more of the objects at or near the given portion of the flight path”).
Regarding claim 28, Levy, Herwitz and Liu teach the system of claim 21, wherein Levy teaches the object existence accuracies reflect a higher object existence accuracy for stationary objects and a lower object existence accuracy for moving objects. (Levy disclosed in page 6 para [0087]: “Zones containing one or more infrastructure objects impermeable to flight, and/or having land terrain impermeable to flight may be assigned a score representing that flying through the zone is impossible, for example, an impossible flight score.” In para [0089]: “Zones containing dynamic object(s) may be assigned a dynamic score representing the risk of flight through the zone when the drone is flying through the zone or expected to fly through the zone. For example, zone 1120 is known to host a hot air balloon festival during a certain week of the year. The score for zone 1120 may be dynamically changed to high risk when the festival is on, and to low risk when the festival is not running. In another example, zone 1122, which used to be open land, is undergoing construction. The score for zone 1122 may be changed from low risk to high risk.” Therefore, the disclosure above “The score for zone 1120 may be dynamically changed” reflects certainty/a high object existence for the impermeable object and a changing/lower existence accuracy for the moving balloons).
Regarding claim 29, Levy, Herwitz and Liu teach the system of claim 21, wherein Levy teaches the one or more physical processors are further configured by machine readable instructions to: track a position of the unmanned aerial vehicle during an unmanned aerial flight; (Levy disclosed in page 9 para [0140]: “Reference is now made to FIG. 6, which is a computer implemented method for monitoring flight of a drone through a geographical airspace based on a flight risk map, … The method monitors flight data of the drone within the geographical air space. The flight data is compared against a flight route and/or the flight risk map, to determine whether the drone is flying within, or predicted to fly into a restricted or a high risk flight zone. The method of FIG. 6 may be implemented by control server 1108, for example, by drone monitoring module 304.” The drone monitoring module works in a computer system using one or more physical processors to track a position of an unmanned aerial vehicle (e.g., based on a flight risk map, the flight data is compared against a flight route and/or the flight risk map, to determine or predict if the drone is flying within, or to fly into a restricted or a high risk flight zone)).
and Levy teaches determine an updated predicted risk based upon the tracked position of the unmanned aerial vehicle, wherein the updated predicted risk reflects a likelihood of experiencing a collision with one or more objects at or near the tracked position of the unmanned aerial vehicle. (Levy disclosed in page 9 para [0143-0145]: “At 606, the current and/or near future estimated location of the drone in the air space is evaluated. The current and/or estimated location of the drone may be compared with the proposed flight plan, to determine when the drone is located or estimated to be located outside of the proposed flight plan path. The current and/or estimated location of the drone may be compared with the proposed flight plan, to determine when the drone is located within, or estimated to fly into a certain zone having an unacceptable risk level, for example, above a threshold, an impassable zone, a high risk zone, and/or a restricted zone … The comparison with the flight risk map may be made, for example, in a case when the flight risk map has been updated based on dynamic conditions, without yet updating the corresponding flight plan. It is noted that the drone may obey the server approved flight plan, yet still find itself in an unacceptable zone. For example, the flight risk of the zone the drone is flying within (or expected to reach) may be dynamically adjusted from low risk to high risk, such as due to an unexpected presence of a police helicopter chasing a suspect, sudden onset of a storm, and a terrorist attack.” The disclosure above “the flight risk of the zone the drone is flying within (or expected to reach) may be dynamically adjusted from low risk to high risk” corresponds to the claim limitation “determine an updated predicted risk based upon the tracked position of the unmanned aerial vehicle”. Further the disclosure “an unacceptable zone, for example an unexpected presence of a police helicopter chasing a suspect, sudden onset of a storm, and a terrorist attack” corresponds to claim limitation “experiencing a collision with one or more objects at or near the tracked position of the unmanned aerial vehicle”).
Regarding Claim 30, the same ground of rejection is made as discussed in claim 21 for substantially similar rationale, therefore claim 30 is rejected under 35 U.S.C. 103 as being unpatentable over Levy, Herwitz and Liu as discussed above for substantially similar rationale.
In addition, Levy teaches obtaining a previously stored three-dimensional representation of user-selected a location, reflecting a presence of objects; (Levy disclosed in page 5 para [0069-0070]: “a real time monitoring station 122 in communication with control server 1108 enables human operators within the control center to view current and/or planned status, issue commands, and/or override automatic functions. Station 122 may include a display and/or user interface. The method of FIG. 2 generates a three dimensional flight risk map. The map may cover the environmental air space available for drone flying. The map includes zones, each zone associated with risk of flying the drone through the respective zone.” In para [0073-0075]: “At 202, data to generate the geographical airspace is received. Data may be locally stored, and/or obtained by accessing one or more remote servers. … Topography data describing the terrain of the area designated to the respective central control server, for example, a county, a city, and/or certain dimensions on the group. The topography data includes, for example, data of mountains, canyons, hills, Valleys, and rives. The topography data may be obtained from a topography database 101, …”.
The disclosure above “a three-dimensional flight risk map generated in FIG. 2 where the map covers the environmental air space available for drone flying. The map includes zones, each zone associated with risk of flying the drone” corresponds to the claim limitation “obtain a three-dimensional representation of a user-selected location, which is previously stored as “topography data obtained from a topography database”. Further, the topography data includes, data of mountains, canyons, hills, Valleys, and rives, thus teaches the claim limitation “reflecting a presence of objects.”).
Levy teaches the three-dimensional representation being derived from a plurality of depth maps of the user-selected location generated during previous unmanned aerial vehicle flights, (Levy disclosed in page 3 para [0040-0041]: “An aspect of some embodiments of the present invention relates to systems and methods for safe navigation of a drone through a geographical air space, the navigation of the drone based on flight through regions of the geographical air space designated as having an acceptable flight risk. A flight risk map associates a representation of the geographical air space with local flight risk scores based on the risk of flying the drone through the local air space. … The systems and/or methods prevent or reduce the risk of drones crashing into other objects (e.g., planes, other drones, buildings, hills, and power lines) by proactively preventing from the drones from flying above certain areas using the flight risk map ... A new flight plan is dynamically generated based on the dynamically updated flight risk map, to prevent the drone from flying through local air spaces with dynamically changed risk profiles.” In page 5 para [0070]: “The method of FIG. 2 generates a three dimensional flight risk map. The map may cover the environmental air space available for drone flying. The map includes zones, each zone associated with risk of flying the drone through the respective zone.”
It has been discussed above that 3D depth map as “risk map” is derived/generated during previous unmanned aerial vehicle flights, because “a new flight plan is dynamically generated based on the dynamically updated flight risk map, to prevent the drone from flying through local air spaces with dynamically changed risk profiles” as discussed above).
However, the prior arts Levy and Herwitz do not explicitly teach the limitations “reflecting object existence accuracies for each individual object, wherein the object existence accuracies provide information about accuracy of existence of boundaries of each of the individual object; obtain a flight path for a future unmanned aerial flight of the unmanned aerial vehicle within the previously stored three-dimensional representation of the user-selected location;
Liu teaches reflecting object existence accuracies for each individual object, wherein the object existence accuracies provide information about accuracy of existence of boundaries of each of the individual object; (Liu disclosed in page 12 para [0118]: “In step 540, the environmental map is used to detect one or more obstacles situated in the portion of the environment. … In some embodiments, if the environmental map is an occupancy grid map, obstacles can be identified by detecting volumes in the occupancy grid map that are continuously occupied. The obstacle detection results may provide information regarding the position, orientation, size, proximity, and/or type of each obstacle, as well as corresponding confidence information for the result. The map can be analyzed to identify obstacles that pose a collision risk to the UAV (e.g., are positioned along or near the flight path”).
Liu teaches obtaining a user-created flight path for a future unmanned aerial flight of the unmanned aerial vehicle, within the previously stored three-dimensional representation of the user-selected location; (Liu disclosed in page 10 para [0103]: “The depth information associated with each set of sensing data can be spatially aligned and combined (e.g., using suitable sensor fusion methods such as Kalman filtering) in order to generate a map including depth information (e.g., a 3D environmental representation such as an occupancy grid map), …”. Further, it has been discussed in page 11 para [0109] that the UAV can be navigated based on obstacle occupancy information represented in the final environmental map in order to avoid collisions. The UAV can be navigated by a user, by an automated control system. Any person having skills in the art would understand that 3D environmental map/grid map corresponds to “three-dimensional representation” of flight path for UAV is obtained by user-selected location.
In page 12 para [0118-0119]: “In step 540, the environmental map is used to detect one or more obstacles situated in the portion of the environment. … In step 550, a second signal is generated using the environmental map to cause to UAV to navigate so as to avoid the one or more obstacles. The environmental map can be analyzed in order to determine the location of the one or more obstacles relative to the UAV, as well as the location of any unobstructed spaces through which the UAV can move in order to avoid colliding with the obstacle. The second signal can thus provide appropriate control signals (e.g., for the propulsion system of the UAV) to cause the UAV to navigate through the unobstructed spaces. In embodiments where the UAV is navigated according to a flight path, the flight path can be modified based on the environmental map so as to avoid the one or more obstacles and the UAV can be navigated according to the modified flight path.”
Therefore, the above disclosures teach the whole limitation).
Levy, Herwitz, and Liu are analogous art because they are related in using environmental maps to allow to make collision-free flights in autonomous unmanned vehicles. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Levy, Herwitz and Liu before him or her, to modify the risk parameters related to the flight safety/confidence score based on the presence of object along the flight path of Levy, to include determining predicted risk for the flight path based on position of UAV and sensor control information Liu, in order to achieve the result or claimed invention “autonomously fly the UAV along the new flight path”. The suggestion/motivation for doing so would have been obvious by Liu because “In some embodiments, a plurality of potential paths can be generated, and one path selected from the plurality based on suitable criteria (e.g., minimizing total flight time, minimizing total flight distance, minimizing energy expenditure, minimizing number of obstacles encountered, maintaining a predetermined distance from obstacles, maintaining a predetermined altitude range, etc.). Flight path information for the UAV (e.g., previously traveled paths, potential paths, selected paths) may be included in the environmental map.” (Liu disclosed in page 13 para [0126]).
Regarding claims 31,32,36 and 37, Levy, Herwitz and Liu teach the method of claim 30, are incorporating the rejections of claims 22,23,27 and 28 respectively, because claims 31,32,36 and 37 have substantially similar claim language as claims 22,23,27 and 28, therefore claims 31,32, 36 and 37 are rejected under 35 U.S.C. 103 as being unpatentable over Levy, Herwitz and Liu as discussed above for substantially similar rationale.
Regarding claim 34, Levy, Herwitz and Liu teach the method of claim 30, wherein Levy teaches the claim element “each of the risk confidence scores” (Levy disclosed in page 8 para [0128]: “when the drone proposes the flight plan, one or more risk scores are calculated for the flight plan based on the flight risk map. For example, a total risk score may be calculated based on scores of respective zones the drone is proposing to fly through. In another example, each zone the drone is proposing to fly through is individually evaluated, to identify zones where the drone is not allowed to fly through (e.g., high risk zone, restricted zone, physical barrier zone).” Here, the evaluated proposed zone such as “high risk zone, restricted zone, physical barrier zone” corresponds to the claim element “risk confidence score or level” related to the risk parameter).
However, the prior arts Levy and Herwitz do not explicitly teach the limitation “risk confidence represent a likelihood of the unmanned aerial vehicle to collide with each corresponding individual object within the three-dimensional representation of the user-selected location”.
Liu teaches risk confidence represent a likelihood of the unmanned aerial vehicle to collide with each corresponding individual object within the three-dimensional representation of the user-selected location. (Liu disclosed in page 12 para [0118]: “In step 540, the environmental map is used to detect one or more obstacles situated in the portion of the environment. ... Optionally, suitable machine learning algorithms can be implemented to perform obstacle detection. In some embodiments, if the environmental map is an occupancy grid map, obstacles can be identified by detecting volumes in the occupancy grid map that are continuously occupied. The obstacle detection results may provide information regarding the position, orientation, size, proximity, and/or type of each obstacle, as well as corresponding confidence information for the result. The map can be analyzed to identify obstacles that pose a collision risk to the UAV (e.g., are positioned along or near the flight path). Liu discussed about “3D grid map” in page 8 para [0091]. The disclosure above “The obstacle detection results provide information regarding the position, orientation, size, proximity, and/or type of each obstacle, also corresponding confidence information for the result. The map can be analyzed to identify obstacles that pose a collision risk to the UAV (e.g., are positioned along or near the flight path)” corresponds to the claim limitation “risk confidence represents an unmanned aerial vehicle to collide with each corresponding individual object within the 3D representation of the location.”).
Levy, Herwitz and Liu are analogous art because they are related in using environmental maps to allow to make collision-free flights in autonomous unmanned vehicles. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Levy, Herwitz and Liu before him or her, to modify the risk parameters related to the flight safety/confidence score based on the presence of object along the flight path of Levy, to include determining predicted risk for the flight path based on position of UAV and sensor control information Liu, in order to achieve the result or claimed invention “autonomously fly the UAV along the new flight path”. The suggestion/motivation for doing so would have been obvious by Liu because “In some embodiments, a plurality of potential paths can be generated, and one path selected from the plurality based on suitable criteria (e.g., minimizing total flight time, minimizing total flight distance, minimizing energy expenditure, minimizing number of obstacles encountered, maintaining a predetermined distance from obstacles, maintaining a predetermined altitude range, etc.). Flight path information for the UAV (e.g., previously traveled paths, potential paths, selected paths) may be included in the environmental map.” (Liu disclosed in page 13 para [0126]).
Regarding claim 38, Levy, Herwitz and Liu teach the method of claim 30, Levy teaches tracking a position of an unmanned aerial vehicle during an unmanned aerial flight; (Levy disclosed in page 9 para [0140]: “Reference is now made to FIG. 6, which is a computer implemented method for monitoring flight of a drone through a geographical airspace based on a flight risk map, … The method monitors flight data of the drone within the geographical air space. The flight data is compared against a flight route and/or the flight risk map, to determine whether the drone is flying within, or predicted to fly into a restricted or a high risk flight zone. The method of FIG. 6 may be implemented by control server 1108, for example, by drone monitoring module 304.” The drone monitoring module works in a computer system using one or more physical processors to track a position of an unmanned aerial vehicle (e.g., based on a flight risk map, the flight data is compared against a flight route and/or the flight risk map, to determine or predict if the drone is flying within, or to fly into a restricted or a high risk flight zone)).
and Levy teaches determining an updated predicted risk based upon the tracked position of the unmanned aerial vehicle, wherein the updated predicted risk reflects a likelihood of experiencing a collision with one or more objects at or near the tracked position of the unmanned aerial vehicle; (Levy disclosed in page 9 para [0143-0145]: “At 606, the current and/or near future estimated location of the drone in the air space is evaluated. The current and/or estimated location of the drone may be compared with the proposed flight plan, to determine when the drone is located or estimated to be located outside of the proposed flight plan path. The current and/or estimated location of the drone may be compared with the proposed flight plan, to determine when the drone is located within, or estimated to fly into a certain zone having an unacceptable risk level, for example, above a threshold, an impassable zone, a high risk zone, and/or a restricted zone … The comparison with the flight risk map may be made, for example, in a case when the flight risk map has been updated based on dynamic conditions, without yet updating the corresponding flight plan. It is noted that the drone may obey the server approved flight plan, yet still find itself in an unacceptable zone. For example, the flight risk of the zone the drone is flying within (or expected to reach) may be dynamically adjusted from low risk to high risk, such as due to an unexpected presence of a police helicopter chasing a suspect, sudden onset of a storm, and a terrorist attack.” The disclosure above “the flight risk of the zone the drone is flying within (or expected to reach) may be dynamically adjusted from low risk to high risk” corresponds to the claim limitation “determine an updated predicted risk based upon the tracked position of the unmanned aerial vehicle”. Further the disclosure “an unacceptable zone, for example an unexpected presence of a police helicopter chasing a suspect, sudden onset of a storm, and a terrorist attack” corresponds to claim limitation “experiencing a collision with one or more objects at or near the tracked position of the unmanned aerial vehicle”).
However, Levy and Herwitz do not explicitly teach the claim limitation “a first portion of the user-created flight path for which risk is determined is a point on the user-created flight path”.
wherein Liu teaches a first portion of the user-created flight path for which risk is determined is a point on the user-created flight path. (Under BRI and conventional meaning in the art, Examiner would construe the claim element “point” as “position”.
Liu disclosed in page 5 para [0068]: “The UAVs described herein can be operated completely autonomously (e.g., by a suitable computing system such as an onboard controller), semi-autonomously, or manually (e.g., by a human user). The UAV can receive commands from a suitable entity (e.g., human user or autonomous control system) and respond to such commands by performing one or more actions.” In page 12 para [0118]: “In step 540, the environmental map is used to detect one or more obstacles situated in the portion of the environment. Obstacle detection from map information can be performed using various strategies such as by feature extraction or pattern recognition techniques. Optionally, suitable machine learning algorithms can be implemented to perform obstacle detection. In some embodiments, if the environmental map is an occupancy grid map, obstacles can be identified by detecting volumes in the occupancy grid map that are continuously occupied. The obstacle detection results may provide information regarding the position, orientation, size, proximity, and/or type of each obstacle, as well as corresponding confidence information for the result.” The disclosure above “obstacle detection results may provide information regarding the position of each obstacle” corresponds to the claim limitation “risk is determined is a point on the user-created flight path”. The first portion of the user-created flight path is determined by the human user (above disclosure para [0068]), where UAV can receive commands from a suitable entity such as human user or autonomous control system. The environmental map (Fig. 5 element 540) is used to detect one or more obstacles/risk situated in the portion (corresponds to first portion of flight path) of the environment).
Levy, Herwitz and Liu are analogous art because they are related in using environmental maps to allow to make collision-free flights in autonomous unmanned vehicles. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Levy, Herwitz and Liu before him or her, to modify the risk parameters related to the flight safety/confidence score based on the presence of object along the flight path of Levy, to include determining predicted risk for the flight path based on position of UAV and sensor control information Liu, in order to achieve the result or claimed invention “autonomously fly the UAV along the new flight path”. The suggestion/motivation for doing so would have been obvious by Liu because “In some embodiments, a plurality of potential paths can be generated, and one path selected from the plurality based on suitable criteria (e.g., minimizing total flight time, minimizing total flight distance, minimizing energy expenditure, minimizing number of obstacles encountered, maintaining a predetermined distance from obstacles, maintaining a predetermined altitude range, etc.). Flight path information for the UAV (e.g., previously traveled paths, potential paths, selected paths) may be included in the environmental map.” (Liu disclosed in page 13 para [0126]).
Regarding Claim 40, the same ground of rejection is made as discussed in claims 21 and 30 for substantially similar rationale, therefore claim 40 is rejected under 35 U.S.C. 103 as being unpatentable over Levy, Herwitz and Liu as discussed above for substantially similar rationale. In addition, claim 40 recites following limitations:
Levy teaches a non-transitory computer-readable storage medium comprising stored instructions that, (Levy disclosed in page 3 para [0049-0050]: “The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device.”).
Levy teaches when executed, causes at least one processor to: obtain a previously stored three-dimensional representation of a user-selected location, reflect a presence of objects (Levy disclosed in page 5 para [0069-0070]: “a real time monitoring station 122 in communication with control server 1108 enables human operators within the control center to view current and/or planned status, issue commands, and/or override automatic functions. Station 122 may include a display and/or user interface. The method of FIG. 2 generates a three dimensional flight risk map. The map may cover the environmental air space available for drone flying. The map includes zones, each zone associated with risk of flying the drone through the respective zone.” In para [0073-0075]: “At 202, data to generate the geographical airspace is received. Data may be locally stored, and/or obtained by accessing one or more remote servers. … Topography data describing the terrain of the area designated to the respective central control server, for example, a county, a city, and/or certain dimensions on the group. The topography data includes, for example, data of mountains, canyons, hills, Valleys, and rives. The topography data may be obtained from a topography database 101, …”.
The disclosure above “a three-dimensional flight risk map generated in FIG. 2 where the map covers the environmental air space available for drone flying. The map includes zones, each zone associated with risk of flying the drone” corresponds to the claim limitation “obtain a three-dimensional representation of a user-selected location, which is previously stored as “topography data obtained from a topography database”. Further, the topography data includes, data of mountains, canyons, hills, Valleys, and rives, thus teaches the claim limitation “reflecting a presence of objects.”).
Levy teaches the three-dimensional representation being derived from a plurality of depth maps of the user-selected location generated during previous unmanned aerial vehicle flights, (Levy disclosed in page 3 para [0040-0041]: “An aspect of some embodiments of the present invention relates to systems and methods for safe navigation of a drone through a geographical air space, the navigation of the drone based on flight through regions of the geographical air space designated as having an acceptable flight risk. A flight risk map associates a representation of the geographical air space with local flight risk scores based on the risk of flying the drone through the local air space. … The systems and/or methods prevent or reduce the risk of drones crashing into other objects (e.g., planes, other drones, buildings, hills, and power lines) by proactively preventing from the drones from flying above certain areas using the flight risk map ... A new flight plan is dynamically generated based on the dynamically updated flight risk map, to prevent the drone from flying through local air spaces with dynamically changed risk profiles.” In page 5 para [0070]: “The method of FIG. 2 generates a three dimensional flight risk map. The map may cover the environmental air space available for drone flying. The map includes zones, each zone associated with risk of flying the drone through the respective zone.”
It has been discussed above that 3D depth map as “risk map” is derived/generated during previous unmanned aerial vehicle flights, because “a new flight plan is dynamically generated based on the dynamically updated flight risk map, to prevent the drone from flying through local air spaces with dynamically changed risk profiles” as discussed above).
However, Levy and Herwitz do not explicitly teach the limitation “reflect object existence accuracies for each individual object wherein the object existence accuracies provide information about accuracy of existence of boundaries of each of the individual object; obtain a flight path for a future unmanned aerial flight of the unmanned aerial vehicle within the previously stored three-dimensional representation of the user-selected location; override the flight path based on the determined predicted risk so that as the unmanned aerial vehicle travels along the individual portions of the flight path, the predicted risk of the unmanned aerial vehicle is lowered with regard to the boundaries of each of the individual objects;”
Liu teaches reflect object existence accuracies for each individual object, wherein the object existence accuracies provide information about accuracy of existence of boundaries of each of the individual object; (Liu disclosed in page 12 para [0118]: “In step 540, the environmental map is used to detect one or more obstacles situated in the portion of the environment. … In some embodiments, if the environmental map is an occupancy grid map, obstacles can be identified by detecting volumes in the occupancy grid map that are continuously occupied. The obstacle detection results may provide information regarding the position, orientation, size, proximity, and/or type of each obstacle, as well as corresponding confidence information for the result. The map can be analyzed to identify obstacles that pose a collision risk to the UAV (e.g., are positioned along or near the flight path”).
Liu teaches override the flight path based on the determined predicted risk so that as the unmanned aerial vehicle travels along the individual portions of the flight path, the predicted risk of the unmanned aerial vehicle is lowered with regard to the boundaries of each of the individual objects; (Liu disclosed in page 13 para [0126-0127]: “In some embodiments, once the UAV has received an instruction to auto-return, the UAV can use the environmental map to determine its current location, the spatial relationship between the current and initial locations, and/or the locations of any environmental obstacles. Based on the obstacle occupancy information in the environmental map, the UAV can then determine an appropriate path (e.g., a flight path) to navigate from the current location to the initial location. Various approaches can be used to determine a suitable path for the UAV. For example, the path can be determined using an environmental map such as a topology map, … the path can be configured to avoid one or more environmental obstacles that may obstruct the flight of the UAV. Alternatively, or in combination, the path may be the shortest path between the current and initial locations. … a plurality of potential paths can be generated, and one path selected from the plurality based on suitable criteria (e.g., minimizing total flight time, … minimizing number of obstacles encountered, maintaining a predetermined distance from obstacles, … etc.). Flight path information for the UAV (e.g., previously traveled paths, potential paths, selected paths) may be included in the environmental map. … In some embodiments, as the UAV is navigating along the path, it may detect one or more obstacles along the path that obstruct its flight. In such situations, the path can be modified to avoid the obstacle so as to permit the UAV to continue navigating to the initial location, …”.
The disclosure above “based on the obstacle occupancy information in the environmental map, the UAV can then determine an appropriate path e.g., a flight path to navigate from the current location to the initial location; the path can be determined using an environmental map such as a topology map; the path can be configured to avoid one or more environmental obstacles that may obstruct the flight of the UAV; the path can be modified to avoid the obstacle so as to permit the UAV to continue navigating to the initial location” correspond to claim limitation “override the flight path based on the determined predicted risk so that as the unmanned aerial vehicle travels along the individual portions of the flight path with regard to the boundaries of each of the individual objects”. Further, the disclosure “a plurality of potential paths can be generated, and one path selected from the plurality based on suitable criteria e.g., minimizing number of obstacles encountered, maintaining a predetermined distance from obstacles” correspond to claim limitation “the predicted risk of the unmanned aerial vehicle is lowered”).
Levy, Herwitz and Liu are analogous art because they are related in using environmental maps to allow to make collision-free flights in autonomous unmanned vehicles. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Levy, Herwitz and Liu before him or her, to modify the risk parameters related to the flight safety/confidence score based on the presence of object along the flight path of Levy, to include determining predicted risk for the flight path based on position of UAV and sensor control information Liu, in order to achieve the result or claimed invention “autonomously fly the UAV along the new flight path”. The suggestion/motivation for doing so would have been obvious by Liu because “In some embodiments, a plurality of potential paths can be generated, and one path selected from the plurality based on suitable criteria (e.g., minimizing total flight time, minimizing total flight distance, minimizing energy expenditure, minimizing number of obstacles encountered, maintaining a predetermined distance from obstacles, maintaining a predetermined altitude range, etc.). Flight path information for the UAV (e.g., previously traveled paths, potential paths, selected paths) may be included in the environmental map.” (Liu disclosed in page 13 para [0126]).
Claims 25,35, 41 and 42 are rejected under 35 U.S.C. 103 as being unpatentable over Levy, Herwitz, Liu, and further in view of Sills et al. (Patent No. US9594372B1).
Regarding claim 25, Levy, Herwitz and Liu teach the system of claim 21, wherein Levy teaches the claim element “each of the risk confidence scores” (Levy disclosed in page 8 para [0128]: “when the drone proposes the flight plan, one or more risk scores are calculated for the flight plan based on the flight risk map. For example, a total risk score may be calculated based on scores of respective zones the drone is proposing to fly through. In another example, each zone the drone is proposing to fly through is individually evaluated, to identify zones where the drone is not allowed to fly through (e.g., high risk zone, restricted zone, physical barrier zone).” Here, the evaluated proposed zone such as “high risk zone, restricted zone, physical barrier zone” corresponds to the claim element “risk confidence score or level” related to the risk parameter).
and Levy teaches the risk parameters include a distance between the unmanned aerial vehicle along the individual portions of the flight path and each individual object within the three-dimensional representation (Levy disclosed in page 5 para [0070]: “The method of FIG. 2 generates a three dimensional flight risk map. The map may covers the environmental air space available for drone flying. The map includes zones, each zone associated with risk of flying the drone through the respective zone.” In page 6 para [0090]: “Zones containing static object(s) may be assigned a static score. The static score may represent the risk of flight through the zone, to keep the drone away from the static object. For example, the risk of flight may increase closer to the object. For example, zone 1124 contains residential buildings. The score may represent a low risk of flight at 100 meters and further away from the buildings, increasing …”. Further, in page 9 para [0132]: “At 510, the flight plan proposed by the drone and/or each flight plan generated by the server are evaluated. … The approval or rejection may be performed by comparing the total calculated risk of flight to a predefined total risk threshold. When multiple flight plans are proposed, a subset of one or more flight routes with acceptable total flight risk is designated. The approval or rejection may be performed by checking whether a portion (or all) of the flight plan involves flying through certain zones. For example, a flight plan that flies only through low risk zones is approved.”
The disclosure above “the flight safety score is designated from multiple available flight safety scores, based on the presence of at least one existing object type within the respective volume” corresponds the claim element “risk parameters” for a flight. Further, the disclosure “drones are not allowed to fly within 100 meters of high-rise buildings” corresponds the claim limitation “distance between an unmanned aerial vehicle along the individual portions of the flight path and each individual object”).
However, Levy and Herwitz do not explicitly teach the limitation “risk confidence represent a likelihood of the unmanned aerial vehicle to collide with each corresponding individual object within the three-dimensional representation of the user-selected location”.
Liu teaches risk confidence represent a likelihood of the unmanned aerial vehicle to collide with each corresponding individual object within the three-dimensional representation of the user-selected location. (Liu disclosed in page 12 para [0118]: “In step 540, the environmental map is used to detect one or more obstacles situated in the portion of the environment. ... Optionally, suitable machine learning algorithms can be implemented to perform obstacle detection. In some embodiments, if the environmental map is an occupancy grid map, obstacles can be identified by detecting volumes in the occupancy grid map that are continuously occupied. The obstacle detection results may provide information regarding the position, orientation, size, proximity, and/or type of each obstacle, as well as corresponding confidence information for the result. The map can be analyzed to identify obstacles that pose a collision risk to the UAV (e.g., are positioned along or near the flight path). Liu discussed about “3D grid map” in page 8 para [0091]. The disclosure above “The obstacle detection results provide information regarding the position, orientation, size, proximity, and/or type of each obstacle, also corresponding confidence information for the result. The map can be analyzed to identify obstacles that pose a collision risk to the UAV (e.g., are positioned along or near the flight path)” corresponds to the claim limitation “risk confidence represents an unmanned aerial vehicle to collide with each corresponding individual object within the 3D representation of the location.”).
Levy, Herwitz, and Liu are analogous art because they are related in using environmental maps to allow to make collision-free flights in autonomous unmanned vehicles. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Levy, Herwitz and Liu before him or her, to modify the risk parameters related to the flight safety/confidence score based on the presence of object along the flight path of Levy, to include determining predicted risk for the flight path based on position of UAV and sensor control information Liu, in order to achieve the result or claimed invention “autonomously fly the UAV along the new flight path”. The suggestion/motivation for doing so would have been obvious by Liu because “In some embodiments, a plurality of potential paths can be generated, and one path selected from the plurality based on suitable criteria (e.g., minimizing total flight time, minimizing total flight distance, minimizing energy expenditure, minimizing number of obstacles encountered, maintaining a predetermined distance from obstacles, maintaining a predetermined altitude range, etc.). Flight path information for the UAV (e.g., previously traveled paths, potential paths, selected paths) may be included in the environmental map.” (Liu disclosed in page 13 para [0126]).
However, Levy, Herwitz and Liu do not explicitly teach the limitation “risk parameters include previous collision records of previous unmanned aerial vehicles colliding with an object within the user-selected location”.
and Sills teaches risk parameters include previous collision records of previous unmanned aerial vehicles colliding with an object within the user-selected location. (Sills disclosed in col. 1 lines 39-46: “a control system that is programmatically arranged to assist a user by facilitating feedback processes based on information about the environment that the control system receives from an aerial vehicle, such as from an unmanned aerial vehicle (UAV) … the control system may determine a particular assistance mode that is associated with an account (e.g., a user-account set up for the user).”. In col. 27 lines 3-12: “the control system may determine that this preceding environment information indicates twenty previous collisions with obstacles in the environment and may also determine that the indicated number of collisions exceeds a threshold number of collisions (e.g., fifteen). And in response to determining that the indicated number of collisions exceeds the threshold number of collisions, the control system may determine that the above-mentioned “visual obstacle detection” assistance mode should provide feedback periodically at periodic increments often seconds.” In column 26, lines 7-10, discussed the “navigation assistance mode”, which provides feedback more often in the higher-collision area, (see column 27, lines 5-12), and thus influences the risk prediction, i.e., should be used as risk parameters related to claim limitation “previous collision records of previous unmanned aerial vehicles colliding with an object within the location” (e.g., preceding environment information indicates twenty previous collisions with obstacles in the environment and also determined the indicated number of collisions exceeds a threshold number of collisions (e.g., fifteen)).
Levy, Herwitz, Liu and Sills are analogous art because they are related to have collision-free flights in autonomous unmanned vehicles. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Levy, Herwitz, Liu and Sills before him or her, to modify risk confidence related to the UAV to collide with each corresponding individual object of Liu, to include risk parameters include previous collision records of previous UAV colliding with an object of Sills. The suggestion/motivation for doing so would have been obvious by Sills because “a control system, a particular assistance mode associated with an account, where the particular assistance mode specifies (i) particular operations for an aerial vehicle to carry out in order to obtain sensor data providing environment information corresponding to a location associated with the account and (ii) feedback processes to provide feedback, via a feedback system associated with the account, that corresponds to respective environment information.” (Sills disclosed in col. 2 lines 5-14).
Regarding claim 35, Levy, Herwitz and Liu teach the method of claim 30, wherein Levy teaches the risk parameters include a distance between an unmanned aerial vehicle along the individual portions of the user-created flight path and each individual object within the three-dimensional representation (Levy disclosed in page 5 para [0070]: “The method of FIG. 2 generates a three dimensional flight risk map. The map may covers the environmental air space available for drone flying. The map includes zones, each zone associated with risk of flying the drone through the respective zone.” In page 6 para [0090]: “Zones containing static object(s) may be assigned a static score. The static score may represent the risk of flight through the zone, to keep the drone away from the static object. For example, the risk of flight may increase closer to the object. For example, zone 1124 contains residential buildings. The score may represent a low risk of flight at 100 meters and further away from the buildings, increasing …”. Further, in page 9 para [0132]: “At 510, the flight plan proposed by the drone and/or each flight plan generated by the server are evaluated. … The approval or rejection may be performed by comparing the total calculated risk of flight to a predefined total risk threshold. When multiple flight plans are proposed, a subset of one or more flight routes with acceptable total flight risk is designated. The approval or rejection may be performed by checking whether a portion (or all) of the flight plan involves flying through certain zones. For example, a flight plan that flies only through low risk zones is approved.”
The disclosure above “the flight safety score is designated from multiple available flight safety scores, based on the presence of at least one existing object type within the respective volume” corresponds the claim element “risk parameters” for a flight. Further, the disclosure “drones are not allowed to fly within 100 meters of high-rise buildings” corresponds the claim limitation “distance between an unmanned aerial vehicle along the individual portions of the flight path and each individual object”).
However, Levy, Herwitz, and Liu do not explicitly teach the limitation “risk parameters include previous collision records of previous unmanned aerial vehicles colliding with an object within the location.
and Sills teaches risk parameters include previous collision records of previous unmanned aerial vehicles colliding with an object within the location. (Sills disclosed in col. 27 lines 3-12: “the control system may determine that this preceding environment information indicates twenty previous collisions with obstacles in the environment and may also determine that the indicated number of collisions exceeds a threshold number of collisions (e.g., fifteen). And in response to determining that the indicated number of collisions exceeds the threshold number of collisions, the control system may determine that the above-mentioned “visual obstacle detection” assistance mode should provide feedback periodically at periodic increments often seconds.” In column 26, lines 7-10, discussed the “navigation assistance mode”, which provides feedback more often in the higher-collision area, (see column 27, lines 5-12), and thus influences the risk prediction, i.e., should be used as risk parameters related to claim limitation “previous collision records of previous unmanned aerial vehicles colliding with an object within the location” (e.g., preceding environment information indicates twenty previous collisions with obstacles in the environment and also determined the indicated number of collisions exceeds a threshold number of collisions (e.g., fifteen)).
Levy, Herwitz, Liu and Sills are analogous art because they are related to have collision-free flights in autonomous unmanned vehicles. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Levy, Herwitz, Liu and Sills before him or her, to modify risk confidence related to the UAV to collide with each corresponding individual object of Liu, to include risk parameters include previous collision records of previous UAV colliding with an object of Sills. The suggestion/motivation for doing so would have been obvious by Sills because “a control system, a particular assistance mode associated with an account, where the particular assistance mode specifies (i) particular operations for an aerial vehicle to carry out in order to obtain sensor data providing environment information corresponding to a location associated with the account and (ii) feedback processes to provide feedback, via a feedback system associated with the account, that corresponds to respective environment information.” (Sills disclosed in col. 2 lines 5-14).
Regarding claim 41, Levy, Herwitz and Liu teach the system of claim 21, further comprising: Levy teaches display within the three- dimensional representations to indicate a direction to move the unmanned aerial vehicle relative to the flight path of the unmanned aerial vehicle to decrease a likelihood of a collision; (Levy disclosed in page 5 para [0064]: “Control center 1108 performs one or more of the following functions: ensures that drones are certified to fly, automatically approves flight plans according to current policy and/or to specific urban limitations (based on a flight map), generates air routes that avoid obstacles (based on the flight map), monitors drones are flying within allowed air spaces (optionally based on enforcement of flight regulations), navigates drones through the allowed airspaces, … provides the drones continuous anti-collision monitoring and/or control, and/or provides each drone with a contingency plan and/or emergency instructions …”. In page 5 para [0069-0070]: “FIG. 3B is an example of a certain system design to implement control server 1108. Optionally, a real time monitoring station 122 in communication with control server 1108 enables human operators within the control center to view current and/or planned status, issue commands, and/or override automatic functions. Station 122 may include a display and/or user interface. The method of FIG. 2 generates a three dimensional flight risk map. The map may covers the environmental air space available for drone flying. The map includes zones, each zone associated with risk of flying the drone through the respective zone.”
The disclosure “air routes that avoid obstacles (based on the flight map) is generated, drones are monitored flying within allowed air spaces and further the drones are provided with continuous anti-collision monitoring or control, and each drone is provided with a contingency plan or emergency instructions” corresponds to the claim limitation “indicate a direction to move an unmanned aerial vehicle relative to the unmanned aerial flight to decrease a likelihood of a collision”. A real time monitoring station in communication with control server that enables human operators to view current and/or planned status, issue commands, or override automatic functions, i.e., direction to move an unmanned aerial vehicle/flight is displayed in user interface (or monitoring station) within the 3D representation (e.g., FIG. 2 generates a three-dimensional flight risk map). Further, the disclosure above “map includes zones cover the environmental air space available for drone flying” corresponds to claim element “flight path of the unmanned aerial vehicle”)).
However, Levy, Herwitz and Liu do not explicitly teach the limitation “display notifications, based upon the determined predicted risk, to indicate a direction to move the unmanned aerial vehicle”.
Sills teaches display notifications, based upon the determined predicted risk, to indicate a direction to move the unmanned aerial vehicle (Sills disclosed in col. 26 lines 14-24: “the control system may arrange the navigation mode to specify a respective feedback process to provide feedback indicative of one or more navigation instructions, … By way of example, the feedback may include projection from a projector of the feedback system. These projections may include projections of arrows each indicating an instructed direction of movement and perhaps may also include projections to point out obstacles and/or points of interest in the environment.” In col. 26 lines 45-52: “the control system may determine that the preceding environment information is representative of a threshold high number of obstacles being encountered in the environment and may responsively determine that a “visual obstacle detection” assistance mode should be used, which is arranged to provide visual feedback to points out all obstacles that an aerial vehicle detect within a one mile radius of the location …”. This disclosure provides display notification corresponding to collision or obstacle e.g., projections of arrows each indicating an instructed direction of movement and also include projections to point out obstacles; a “visual obstacle detection” assistance mode should be used, to provide visual feedback to points out all obstacles that an aerial vehicle detect within a one-mile radius of the location).
Further, in col. 30 lines 6-16: “the control system may determine that a particular obstacle is positioned at or in the vicinity of the location associated with the account. In doing so, the control system may determine a potential future contact with the particular obstacle, such as a potential future collision between the particular obstacle and a user wearing the associated feedback system for instance. As such, the control system may determine a corresponding feedback process to initiate feedback that indicates the potential future contact, so as to ultimately help the user avoid the future contact for instance.” In col. 30 lines 31-40: “the feedback may be arranged to indicate the distance between the particular obstacle and the location associated with the account. As an example, the control system may determine a feedback process to initiate vibrational feedback at a vibrational intensity corresponding to the determined distance.” The predicted risk or obstacle encountered in this scenario to indicate a direction to move an unmanned aerial vehicle (e.g., control system may determine a corresponding feedback process to initiate feedback that indicates the potential future contact and feedback process to initiate vibrational feedback at a vibrational intensity corresponding to the determined distance)).
and wherein Sills teaches the indication is a graphical notification that includes an arrow that points to the direction to move the unmanned aerial vehicle. (Sills disclosed in col. 34 lines 31-42: “the visual feedback information may include a visual symbol that is representative of an instruction specifying a movement towards a particular destination. For example, the visual symbol could be an arrow that specifies a direction towards which a user should move to ultimately arrive at the particular destination. In yet another case, the visual feedback information may include a visual symbol that is representative of a particular obstacle in an environment corresponding to the location associated with the account. For example, the visual symbol could be a callout bubble that points at the particular obstacle and labels the particular obstacle with a certain word or phrase.”).
Levy, Herwitz, Liu and Sills are analogous art because they are related to have collision-free flights in autonomous unmanned vehicles. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Levy, Herwitz, Liu and Sills before him or her, to modify risk confidence related to the UAV to collide with each corresponding individual object of Liu, to include risk parameters include previous collision records of previous UAV colliding with an object of Sills. The suggestion/motivation for doing so would have been obvious by Sills because “a control system, a particular assistance mode associated with an account, where the particular assistance mode specifies (i) particular operations for an aerial vehicle to carry out in order to obtain sensor data providing environment information corresponding to a location associated with the account and (ii) feedback processes to provide feedback, via a feedback system associated with the account, that corresponds to respective environment information.” (Sills disclosed in col. 2 lines 5-14).
Regarding claim 42, Levy, Herwitz and Liu teach the non-transitory computer-readable storage medium of claim 40, is incorporating the rejections of claim 41, because claim 42 has substantially similar claim language as claim 41, therefore claim 42 is rejected under 35 U.S.C. 103 as being unpatentable over Levy, Herwitz, Liu and further in view of Sills as discussed above for substantially similar rationale.
Claims 43 and 44 are rejected under 35 U.S.C. 103 as being unpatentable over Levy, Herwitz, Liu and Sills and further in view of Stark et al. (Patent No. US9102406B2).
Regarding claim 43, Levy, Herwitz, Liu and Sills teach the non-transitory computer-readable storage medium of claim 42, further Levy teaches providing risk based upon a current travel speed of the unmanned aerial vehicle (Levy disclosed in page 8 para [0127-0128]: “drone flight performance details are accessed or received. The details may include static drone parameters, for example, drone weight, maximum flight speed, and maximum altitude. The details may include real time in-flight performance, … current flight speed, current altitude, current flight direction. Optionally, at 506, when the drone proposes the flight plan, one or more risk scores are calculated for the flight plan based on the flight risk map. For example, a total risk score may be calculated based on scores of respective zones the drone is proposing to fly through.” The details related to real time in-flight performance includes current flight speed, current altitude etc. are considered while calculating the total risk score based on scores of respective zones the drone is proposing to fly through).
However, Levy, Herwitz, Liu and Sills do not explicitly teach the limitation “providing a risk confidence score of the unmanned aerial vehicle hitting an object along the flight path at a future time based upon the flight path of the unmanned aerial vehicle”.
Stark teaches providing a risk confidence score of the unmanned aerial vehicle hitting an object along the flight path at a future time based upon the flight path of the unmanned aerial vehicle. (Stark disclosed in col. 11 lines 12-24: “the control method and system taught herein combines centralized, control … with smart UAVs to more effectively provide flock-type movement of the UAVs. … while attempting to respond to environmental conditions such as changing wind or the unexpected presence of another UAV within or near to their safety window (or safe operating envelope surrounding each UAV such as a sphere of several-to-many feet such as 10 to 30 feet or the like in which no other UAV typically will travel to avoid collisions).” The risk confidence score of the unmanned aerial vehicle, provided in this disclosure is 10 to 30 feet, that an UAV might face or can collide with unexpected presence of another UAV within or near to their safety window (i.e., unmanned aerial vehicle hitting an object along the flight path at a future time)).
Levy, Herwitz, Liu, Sills and Stark are analogous art because they are related to have collision-free flights in autonomous unmanned vehicles. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Levy, Herwitz, Liu, Sills and Stark before him or her, to modify providing risk score based on the travel speed of unmanned aerial vehicle of Levy, to include providing/obtaining risk confidence score of the unmanned aerial vehicle at a future time based upon the flight path of Stark. The suggestion/motivation for doing so would have been obvious by Stark because “the GUI may show properly operating and positioned UAVs in green, UAVs that are off course or out of position a safe amount in yellow, and UAVs outside of a safe envelope in red. The red/unsafe UAVs may be handled automatically or manually to cause them to enter a safe mode of operation. The ground control system (GCS) configured to evaluate collision issues and execute collision avoidance commands to preserve show quality (i.e., flight performance) in degrading weather conditions.” (Stark disclosed in col. 12 lines 15-31).
Regarding claim 44, Levy, Herwitz, Liu, Sills and Stark teach the non-transitory computer-readable storage medium of claim 43, however, Levy, Herwitz, Liu and Sills do not explicitly teach the limitation “updating the predicted risk for a tracked position of the unmanned aerial vehicle if a user manually overrides the flight path”.
further Stark teaches updating the predicted risk for a tracked position of the unmanned aerial vehicle if a user manually overrides the flight path. (Stark disclosed in col. 12 lines 5-14: “Generally, the GCS monitors for safe operations of the UAVs as discussed with reference to FIG. 3, but an operator may take steps to manually override a particular one of the many UAVs to provide better control of that UAV. … A warning may be provided in a GUI that the UAV is trending off course or is outside an accepted tolerance for reaching its next way point.” This disclosure teaches the limitation “a user manually overrides the flight path”.
In col. 13 lines 29-44: “Each UAV may use its local control module to operate on an ongoing basis to detect when another UAV comes within a predefined distance from the UAV such as within a sphere of 10 to 30 feet or the like. The first UAV to detect such a condition (or both UAVs if a tie) generates a collision warning message and transmits this message to the offending/nearby UAV to alter its course or present position to move out of the first UAVs air space. … The evasion may be taken for a preset time period and then the UAV may return to following its flight plan (e.g., recalculate a course to the next way point from its new present location or the like).” The predicted risk in above disclosure is when one UAV comes within a predefined distance from another UAV within a sphere of 10 to 30 feet, then the first UAV or both can detect such a condition and alter its course or present position to move out from each other’s air space; further flight plan is changed e.g., recalculate a course to the next way point from its new present location (i.e., the predicted risk for a tracked position of the unmanned aerial vehicle is updated/modified)).
Levy, Herwitz, Liu, Sills and Stark are analogous art because they are related to have collision-free flights in autonomous unmanned vehicles. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Levy, Herwitz, Liu, Sills and Stark before him or her, to modify providing risk score based on the travel speed of unmanned aerial vehicle of Levy, to include providing/obtaining risk confidence score of the unmanned aerial vehicle at a future time based upon the flight path of Stark. The suggestion/motivation for doing so would have been obvious by Stark because “the GUI may show properly operating and positioned UAVs in green, UAVs that are off course or out of position a safe amount in yellow, and UAVs outside of a safe envelope in red. The red/unsafe UAVs may be handled automatically or manually to cause them to enter a safe mode of operation. The ground control system (GCS) configured to evaluate collision issues and execute collision avoidance commands to preserve show quality (i.e., flight performance) in degrading weather conditions.” (Stark disclosed in col. 12 lines 15-31).
Conclusion
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Hammond et al. (Pub. No. US2016/0291593A1) disclosed systems and methods for scanning environments and tracking unmanned aerial vehicles within the scanned environments. The method can further include forming a computer-based map of the region with the points and uS1ng the rangefinder and a camera to locate the UAV as it moves in the region. The location of the UAV can be compared with locations on the computer-based map and, based upon the comparison, the method can include transmitting guidance information to the UAV. In a further particular embodiment, two-dimensional imaging data is used in addition to the rangefinder data to provide color information to points in the region. The particular importance to maintain a set distance from an object, (a) to prevent collisions with the object, and/or (b) to keep the object within the target range of a sensor aboard the UAV. Additionally, the information can include the speed of the UAV, the flight dynamics and capabilities of the UAV, based on this information, and an input from the user (e.g., the user can click on a feature or object, and select an offset distance), the system can automatically generate a flight path.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NUPUR DEBNATH whose telephone number is (571)272-8161. The examiner can normally be reached M-F 8:00 am -4:30 pm.
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/NUPUR DEBNATH/Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186