DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed 07/08/2026 is being entered. Claims 1, 11, and 20 are amended. Claims 1-20 are pending, and rejected as detailed below. This action is final as necessitated by amendment.
Response to Arguments
Claim Rejections under 35 U.S.C. §103
Arguments:
Applicant argues that Deyle, whether taken alone or in combination with the other cited references, fails to teach or suggest at least the above recited features of amended independent Claim 1. Specifically, Applicant submits that Deyle fails to teach or suggest at least that the extensible object models are configured to mask the spatial data "such that the spatial data is generated to comprise first data representing a first asset that the current user is authorized to access according to the asset permissions data and to exclude second data representing a second asset that the current user is not authorized to access according to the asset permissions data" as recited by independent Claim 1. The Office Action alleges that Deyle's privacy mode teaches masking spatial data based on asset permissions data and access control data, relying on paragraph [0210] of Deyle. See Office Action. However, Deyle's privacy mode is a blanket, event-triggered suppression mechanism in which "data collected and security operations performed by the robot are not communicated to an external entity...unless one or more security criteria are triggered." See Deyle, paragraph [0210]. This is an all-or-nothing switch that withholds all collected data from all external entities unless a security trigger occurs. Deyle does not disclose selectively generating spatial data to comprise first data representing a first asset that a current user is authorized to access while simultaneously excluding second data representing a second asset that the current user is not authorized to access. There is no concept in Deyle of user-specific, per-asset inclusion or exclusion of spatial data based on asset permissions data. Accordingly, Deyle fails to teach or suggest the amended features of Claim 1.
Applicant also argues that the other cited references fail to cure the deficiencies of Deyle, and indeed, were not cited for these propositions. Accordingly, for at least the various reasons set forth above, Applicant respectfully submits that the cited references fail to disclose, teach, or suggest each and every element of independent Claim 1 as amended. Therefore, the amended independent Claim 1 is allowable. Applicant respectfully requests that the rejection of independent Claim 1 be withdrawn, and that independent Claim 1 be allowed.
Independent Claims 11 and 20 recite similar features to independent Claim 1. Thus, for the same or similar reasons as those described above in conjunction with amended independent Claim 1, Applicant submits that Claims 11 and 20 are also not taught or suggested by the cited references. Applicant respectfully requests that the rejection of independent Claims 1, 11, and 20 be withdrawn and that these claims be allowed.
Applicant also argues other claims are dependent from one of the independent claims discussed above and are patentable for at least the same reasons. Applicant, therefore, respectfully submits that the rejections herewith are overcome and request that the rejections be withdrawn.
Response:
Applicant’s arguments, as amended herein, with respect to the rejections of claims 1, 11, and 20 under 35 U.S.C. §103 have been fully considered and not persuasive. More specifically, applicant argument of all-or-nothing switch that withholds all collected data from all external entities unless a security trigger occurs is incorrect as Deyle [0210] discloses “In some embodiments, the robot is configured to blur faces within captured images or videos to preserve the privacy of the individuals associated with the blurred faces. Such facial blurring may be performed by the robot on the captured images and videos only when the images or video are transmitted to an external entity. The robot can also beneficially blur computer monitors, documents, sensitive data or instruments, or any other objects or information as needed to satisfy security restrictions.”. In other words, when collected data is accessed by authorized personals, the authorized personals have access to some of the collected data (such as a body of an individual that requires preserving the privacy, clothing items of an individual, and other non-sensitive data). However, the authorized personals don’t have access to some of the collected data (such as the face of an individual that requires preserving the privacy, identification cards of an individual, and other sensitive data). For example, when there is a suspicious activity, the security personals are allowed to observe the transmitted data so that the severity of the current situation can be determined. However, sensitive data (facial data, documents, physical identifications, etc.) of an individual/item that requires preserving the privacy can be blurred to protect the identity.
Applicant’s arguments with respect to the rejections of corresponding dependent claims of independent claims 1, 11, and 20 under 35 U.S.C. §103 have been fully considered and not persuasive as the independent claims are rejected based on the combination Deyle, Faustino, and Klingensmith.
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 for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Deyle (US 20200050206 A1), and further in view of FAUSTINO (US 20210325189 A1) and Klingensmith (US 20240192695 A1).
Regarding claim 1, Deyle teaches (Currently Amended) An apparatus comprising at least one processor and at least one non-transitory memory comprising program code stored thereon, wherein the at least one non-transitory memory and the program code are configured to, with the at least one processor (Deyle, at least one para. 0407; “Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented with a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described.”), cause the apparatus to at least:
receive navigation selection input indicating a selected portion of one or more operational systems at a premises (Deyle, at least one para. 0058; “The remote access interface can include a display for displaying information related to one or more components of FIG. 2, an input mechanism for receiving interactions from a user of the remote access interface, and a communicate interface enabling the remote access interface to communicate via the network 200. It should be noted that in some embodiments, the remote access interface can be implemented within hardware located remotely from the central system, the robots, or the other components of FIG. 2, for instance within a different building or on a different floor from the other components of FIG. 2.”) for performance of one or more service operations (Deyle, at least one para. 0219; “As noted above, the robot 100 can perform operations in addition to security operations. For instance, the robot can be located within an entrance or doorway and greet people as they enter or leave an area. The robot can request janitorial service in response to detect a mess within a proximity of the robot, and can act in self-defense in the event that someone tries to tamper with the robot or with another security system or infrastructure system.”);
generate spatial data representing the selected portion of the one or more operational systems based at least in part on the navigation selection input (Deyle, at least one para. 0120; “The semantic mapping system 736 is configured to generate or update a semantic map associated with a location or setting in which the robot 100 is located. For instance, the semantic mapping system can generate a map associated with a patrol route through a building floor as the robot moves through the space. The location of obstructions, and paths within the building floor can be detected by the scanners 726 and recorded onto the semantic map.”) (Deyle, at least one para. 0215; “In emergency situations, the robot can enable an external entity (such as an operator of the robot) to communicate with individuals within a proximity of the robot, for instance to assist with evacuation, to hold or disable elevators, to unlike exit doors, to provide information to response personnel, to sound alarms, to map out a location of a fire, to identify exit routes, to obstruct or locate unauthorized or dangerous individuals, and the like.”), wherein the one or more extensible object models are configured to mask the spatial data based on at least asset permissions data and access control data associated with a current user (Deyle, at least one para. 0210; “The robot 100 can perform one or more additional security operations, either independently of or in concert with one or more of the security operations described above. In one embodiment, the robot can operate in a privacy mode, where data collected and security operations performed by the robot are not communicated to an external entity (to preserve the privacy of individuals in proximity to the robot, or to reduce the likelihood that such data is intercepted during transmission) unless one or more security criteria are triggered (such as the detection of suspicious activity, the identification of an unauthorized individual, and the like). Operating in such a privacy mode further beneficially enables the robot to operate in proximity to sensitive data or objects (such as secret lab equipment or confidential/classified information) without a risk of such sensitive data or objects being discovered by unauthorized entities within access to the robot or the central system 210.”), such that the spatial data is generated to comprise first data representing a first asset that the current user is authorized to access according to the asset permissions data and to exclude second data representing a second asset that the current user is not authorized to access according to the asset permissions data (Deyle, at least one para. 0210; “In some embodiments, the robot is configured to blur faces within captured images or videos to preserve the privacy of the individuals associated with the blurred faces. Such facial blurring may be performed by the robot on the captured images and videos only when the images or video are transmitted to an external entity. The robot can also beneficially blur computer monitors, documents, sensitive data or instruments, or any other objects or information as needed to satisfy security restrictions.”, In other words, when collected data is accessed by authorized personals, the authorized personals have access to some of the collected data (such as a body of an individual that requires preserving the privacy, clothing items of an individual, and other non-sensitive data). However, the authorized personals don’t have access to some of the collected data (such as the face of an individual that requires preserving the privacy, identification cards of an individual, and other sensitive data). For example, when there is a suspicious activity, the security personals are allowed to observe the transmitted data so that the severity of the current situation can be determined. However, sensitive data (facial data, documents, physical identifications, etc.) of an individual/item that requires preserving the privacy can be blurred to protect the identity.);
transmit the spatial data to one or more service devices of different types, including at least one mobile computing device (Deyle, at least one para. 0128; “It should also be noted that the robot 100 includes component necessary to communicatively couple and control the components of the robot, including but not limited to: on-board computers, controllers, and processors; electric circuitry (e.g., motor drivers); computer memory; storage media (e.g., non-transitory computer-readable storage mediums, such as flash memory, hard drives, and the like); communication buses; cooling or heat dissipation systems; and the like.”) and at least one autonomous mobile device (Deyle, at least one para. 0164; “It should be noted that a robot and a hardware system can perform security operations as described herein autonomously or without the explicit instructions or involvement of a human (such as a security personnel 250 or an operator of the robot), beneficially reducing the amount of human interaction required to perform the security operations.”), each associated with the one or more operational systems (Deyle, at least one para. 0105; “The navigation system 710 can move the robot 100 in response to receiving navigation instructions, for instance from a user of the central system 210, from a security personnel 250, or from another robot. In some embodiments, the navigation system moves the robot as part of a patrol, routine, or security protocol. Navigation instructions can include an end location and can determine a route from a current location of the robot to the end location, for instance by detecting obstacles and/or paths from the current location to the end location, by selecting a path based on the detected obstacles and paths, and by moving the robot along the selected path until the robot arrives at the end location. In some embodiments, the navigation instructions can include a path, an ordered set of locations, an objective (e.g., “patrol the 4th floor”), or a map, and the navigation system can move the robot based on the navigation instructions.”); and
cause the one or more service devices to perform one or more navigation operations with respect to the premises and the one or more operational systems (Deyle, at least one para. 0181; “In implementing the security policy, the central system 210 can instruct one or more robots 100 to perform a task (such as patrol a route or intercept an individual that isn't authorized to be in a particular location), can instruct security cameras to change viewpoint and/or capture video of a location associated with a potential violation of a security policy, can request sensor data detected by one or more infrastructure systems 220 or security systems 230, and the like.”)
Even though Deyle teaches “semantic mapping”, Deyle does not explicitly teach that generate spatial data representing the selected portion of the one or more operational systems based at least in part on the navigation selection input and on one or more extensible object models comprising at least graph-based representations of assets, spatial features,
based at least in part on the spatial data
However, FAUSTINO, in the same field of endeavor (FAUSTINO, at least one para. 0013; “FIG. 1 illustrates an example of a network architecture for locating a user device in an indoor environment based at least on a building topology model. The network 100 includes one or more user devices 102(1) and 102(2). The user devices 102(1) and 102(2) may be smartphones, mobile devices, personal digital assistants (PDAs), or other electronic devices having a wireless communication function, that are capable of receiving input, processing the input, and generating output data.”) teaches generate spatial data representing the selected portion of the one or more operational systems based at least in part on the navigation selection input (FAUSTINO, at least one para. 0028; “The location of the individual IoT devices 206-222 may correspond to 3D coordinates within the boundaries of the building 200. For instance, the first IoT device 206 may be located at (X.sub.1, Y1, Z.sub.1), the second IoT device 208 may be located at (X.sub.2, Y2, Z.sub.2), the third IoT device 210 may be located at (X.sub.3, Y3, Z.sub.3), and so on. The 3D coordinates of the IoT devices 206-222 may be used to generate one or more building geometries 224 via a location server.”) and on one or more extensible object models comprising (FAUSTINO, at least one para. 00258; “As the IoT devices 212 and 214 on the second floor and the IoT devices 206-210 on the top floor are located, the building geometry may be updated, or a new building geometry may be generated. The updated building geometry or the new building geometry may indicate that the building 200 may include multiple levels. Accordingly, the building geometries and other spatial data from each floor of the building 200 may be layered to determine the building topology model 202 that represents the building 200.”);
based at least in part on the spatial data (FAUSTINO, at least one para. 0028; “The location of the individual IoT devices 206-222 may correspond to 3D coordinates within the boundaries of the building 200. For instance, the first IoT device 206 may be located at (X.sub.1, Y1, Z.sub.1), the second IoT device 208 may be located at (X.sub.2, Y2, Z.sub.2), the third IoT device 210 may be located at (X.sub.3, Y3, Z.sub.3), and so on.”)
Deyle and FAUSTINO are both considered to be analogous to the claimed invention because both of them are in the same field as generating spatial data representing a device as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine one or more navigation input selection of Deyle with teaching of FAUSTINO in relation to the spatial data gathering by known methods with no changes in their respective functions, and the combination would have yielded predictable results. One of the ordinary skill in the art would have been motivated to make this modification so that topology model that represents a building can be accurately generated or updated (FAUSTINO; 0028).
Even though the combination of Deyle and FAUSTINO teaches “semantic mapping” and “spatial features”, the combination of Deyle and FAUSTINO does not explicitly teach that generate spatial data representing the selected portion of the one or more operational systems based at least in part on the navigation selection input and on one or more extensible object models comprising at least graph-based representations of assets,
However, Klingensmith, in the same field of endeavor (Klingensmith, at least one para. 0083; “Generally described, autonomous and semi-autonomous robots can utilize mapping, localization, and navigation systems to map an environment utilizing sensor data obtained by the robots. Further, the robots can utilize the systems to perform navigation and/or localization in the environment and build navigation graphs that identify route data.”) teaches generate spatial data representing the selected portion of the one or more operational systems based at least in part on the navigation selection input and on one or more extensible object models comprising at least graph-based representations of assets (Klingensmith, at least one para. 0121; “The high-level navigation module 220 can obtain (e.g., from the remote system 160 or the remote controller 10 or the topology component 250) and/or generate a series of route waypoints (As shown in FIGS. 3A and 3B) on the graph map 222 for a navigation route 212 that plots a path around large and/or static obstacles from a start location (e.g., the current location of the robot 100) to a destination. Route edges may connect corresponding pairs of adjacent route waypoints. In some examples, the route edges record geometric transforms between route waypoints based on odometry data (e.g., odometry data from motion sensors or image sensors to determine a change in the robot's position over time). The route waypoints 310 and the route edges 312 may be representative of the navigation route 212 for the robot 100 to follow from a start location to a destination location.”),
The combination of Deyle, FAUSTINO, and Klingensmith are considered to be analogous to the claimed invention because all of them are in the same field as generating spatial data representing a device as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine one or more navigation input selection of Deyle with teaching of Klingensmith in relation to the spatial data gathering by known methods with no changes in their respective functions, and the combination would have yielded predictable results. One of the ordinary skill in the art would have been motivated to make this modification so that topology model can represent accurate path and able to avoid obstacles (Klingensmith; 0125).
Regarding claim 2, The combination of Deyle, FAUSTINO, and Klingensmith teaches the limitations of claim 1, upon which the instant claim depends, as discussed supra. Further, Deyle teaches (Original) The apparatus of claim 1, wherein the one or more service devices include a mobile computing device (Deyle, at least one para. 0128; “It should also be noted that the robot 100 includes component necessary to communicatively couple and control the components of the robot, including but not limited to: on-board computers”) operated by a technician associated with the one or more operational systems (Deyle, at least one para. 0054; “One or more security personnel 250 (for instance, one or more robot operators) can control or monitor the robots, and can adjust a robot deployment as needed”), and
the one or more navigation operations include generating and presenting a blueprint based at least in part on the spatial data (Deyle, at least one para. 0274; “In some embodiments, a floor plan for a building floor can be generated based on a semantic map generated by the robot 100. Likewise, a semantic map can be generated by the robot based on a building floor plan. As used herein, a floor plan can include a SLAM map.”),
the blueprint comprising a graphical depiction of a layout of an area of the premises (Deyle, at least one para. 0224; “the SLAM map 1700 illustrates a partial mapping of a building floor. The map includes an area 1702 that is navigable by a robot 100.”) containing the selected portion of the one or more operational systems with graphical elements representing assets of the one or more operational systems included in the selected portion of the one or more operational systems (Deyle, at least one para. 0270; “In some embodiments, the semantic map can highlight portions of the semantic map corresponding to identification information. For example, the portion of the map corresponding to the conference room 1906 can be highlighted a particular color, for instance a color corresponding to conference rooms”),
wherein positions of the graphical elements with respect to the graphical depiction of the layout of the area of the premises are based at least in part on the spatial data (Deyle, at least one para. 0267; “FIG. 18 illustrates the generation of a 3D semantic map by a robot”).
Regarding claim 3, The combination of Deyle, FAUSTINO, and Klingensmith teaches the limitations of claim 2, upon which the instant claim depends, as discussed supra. Further, Deyle teaches (Original) The apparatus of claim 2, wherein the at least one non-transitory memory and the program code are configured to, with the at least one processor, further cause the apparatus to at least: retrieve service specification data associated with the assets (Deyle, at least one para. 0179; “In some embodiments, the central system can coordinate between multiple maps of the same location, for instance by updating older maps to include the location of objects that newer maps indicated have moved, or by incorporating types of information present in a first map but not a second map into the second map (for instance, the location of windows, whether a door is locked or unlocked, or the location of security cameras).”); and
present graphical elements representing the service specification data for each of the assets as part of the blueprint.
Deyle does not explicitly teach that present graphical elements representing the service specification data for each of the assets as part of the blueprint.
However, FAUSTINO, in the same field of endeavor (FAUSTINO, at least one para. 0013; “FIG. 1 illustrates an example of a network architecture for locating a user device in an indoor environment based at least on a building topology model. The network 100 includes one or more user devices 102(1) and 102(2). The user devices 102(1) and 102(2) may be smartphones, mobile devices, personal digital assistants (PDAs), or other electronic devices having a wireless communication function, that are capable of receiving input, processing the input, and generating output data.”) present graphical elements representing the service specification data for each of the assets as part of the blueprint (FAUSTINO, at least one para. 0030; “For example, the building topology model 202 may show defined interior hallways, rooms, spaces, and other features that may be shown on building blueprints or floor plans.”).
The combination of Deyle, FAUSTINO, and Klingensmith are considered to be analogous to the claimed invention because both of them are in the same field as generating spatial data representing a device as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine one or more navigation input selection of Deyle with teaching of FAUSTINO in relation to the spatial data gathering by known methods with no changes in their respective functions, and the combination would have yielded predictable results. One of the ordinary skill in the art would have been motivated to make this modification so that various architectural or structural features of structurally identical buildings can be shown within the corresponding maps (FAUSTINO; 0030).
Regarding claim 4, The combination of Deyle, FAUSTINO, and Klingensmith teaches the limitations of claim 1, upon which the instant claim depends, as discussed supra. Further, Deyle teaches (Original) The apparatus of claim 1, wherein the one or more service devices include an autonomous mobile device (Deyle, at least one para. 0340; “In some embodiments, the robot 100 can select a route (for instance, based on historical routes, building information, security information, and the like) and navigate the route autonomously, without human operator input or intervention.”) configured to perform service on assets of the one or more operational systems (Deyle, at least one para. 0211; “When the robot detects suspicious activity or when one or more security criteria are triggered while operating in darkness, the robot can activate a spotlight of the robot to highlight an object associated with the detected activity, can activate the buildings lights (e.g., by communicating with the central system 210 to turn on the lights for a particular area associated with the detected activity).”), and
the one or more navigation operations include the autonomous mobile device automatically moving through the premises to assets of the one or more operational systems included in the selected portion of the one or more operational systems based at least in part on the spatial data (Deyle, at least one para. 0211; “When the robot detects suspicious activity or when one or more security criteria are triggered while operating in darkness, the robot can activate a spotlight of the robot to highlight an object associated with the detected activity, can activate the buildings lights (e.g., by communicating with the central system 210 to turn on the lights for a particular area associated with the detected activity)., wherein the while operating in darkness can be seen as the autonomous mobile device automatically moving through the premises”).
Regarding claim 5, The combination of Deyle, FAUSTINO, and Klingensmith teaches the limitations of claim 1, upon which the instant claim depends, as discussed supra. Further, Deyle teaches (Previously Presented) The apparatus of claim 1, wherein the one or more navigation operations include generating and presenting navigation data via the one or more service devices associated with the premises based at least in part on the spatial data (Deyle, at least one para. 0051; “In some embodiments, the robot can be deployed within a building, for instance on one or more floors or portions of floors of a building, can be deployed outside (for instance, in a parking lot), or can be deployed any other suitable location.”).
Regarding claim 6, The combination of Deyle, FAUSTINO, and Klingensmith teaches the limitations of claim 1, upon which the instant claim depends, as discussed supra. Further, Deyle teaches (Original) The apparatus of claim 1, wherein the at least one non-transitory memory and the program code are configured to, with the at least one processor, further cause the apparatus to at least: present a navigation selection interface configured to receive the navigation selection input (Deyle, at least one para. 0058; “The remote access interface can include a display for displaying information related to one or more components of FIG. 2, an input mechanism for receiving interactions from a user of the remote access interface”); and
receive, via the navigation selection interface, the navigation selection input (Deyle, at least one para. 0058; “a communicate interface enabling the remote access interface to communicate via the network 200.”).
Regarding claim 7, The combination of Deyle, FAUSTINO, and Klingensmith teaches the limitations of claim 6, upon which the instant claim depends, as discussed supra. Further, Deyle teaches (Original) The apparatus of claim 6, wherein the navigation selection interface comprises one or more interactable elements configured to receive input indicative of selections of selected regions, sites, zones, and/or floors of the premises (Deyle, at least one para. 0054; “One or more security personnel 250 (for instance, one or more robot operators) can control or monitor the robots, and can adjust a robot deployment as needed (for instance, by allocating additional robots to a building floor on which a security violation is detected).”), (Deyle, at least one para. 0065; “The infrastructure interface 316 is configured to enable the central system 210 (or a user of the central system) to interact with one or more infrastructure systems 220 via the communication interface 310. For instance, the infrastructure interface can lock one or more doors within a building.”).
Regarding claim 8, The combination of Deyle, FAUSTINO, and Klingensmith teaches the limitations of claim 1, upon which the instant claim depends, as discussed supra. Further, Deyle teaches (Previously Presented) The apparatus of claim 1, wherein the at least one non-transitory memory and the program code are configured to, with the at least one processor, further cause the apparatus to at least: retrieve the asset permissions data associated with a current user and the selected portion of the one or more operational systems (Deyle, at least one para. 0202; “In some embodiments, a user can use the security interface to change the patrol route of a robot by selecting among a set of patrol routes, or by manually drawing a patrol route on the local map”); and
cause the one or more service devices to perform the one or more navigation operations based at least in part on the asset permissions data (Deyle, at least one para. 0202; “upon which, a set of navigation instructions through a building portion corresponding to the drawn patrol route will be sent to the robot, and the robot can begin patrolling the drawn patrol route based on the navigation instructions”).
Regarding claim 9, The combination of Deyle, FAUSTINO, and Klingensmith teaches the limitations of claim 1, upon which the instant claim depends, as discussed supra. Further, Deyle teaches (Previously Presented) The apparatus of claim 1, wherein the at least one non-transitory memory and the program code are configured to, with the at least one processor, further cause the apparatus to at least: retrieve from one or more access control systems for the premises access control data associated with a current user and one or more areas of the premises containing the selected portion of the one or more operational systems (Deyle, at least one para. 0208; “A user of the security interface can also configure security policies using the security interface. For instance, a user can select security policy components, including one or more of a location, a time range for which the security policy will be active, one or more categories of individuals to whom the security policy applies (e.g., employees, managers, staff, visitors, etc.), permitted access credentials, security policy exceptions, actions taken in the event of security policy violations (e.g., notify security, revoke user credentials, lock nearby doors, etc.), and the like.”); and
cause the one or more service devices to perform the one or more navigation operations based at least in part on the asset permissions data (Deyle, at least one para. 0208; “The security policy configured by the user of the security interface can be stored and subsequently implemented, for instance by patrolling robots”).
Regarding claim 10, The combination of Deyle, FAUSTINO, and Klingensmith teaches the limitations of claim 1, upon which the instant claim depends, as discussed supra. Further, Deyle teaches (Previously Presented) The apparatus of claim 1, wherein the spatial data comprises at least:
asset data identifying and/or characterizing assets included in the selected portion of the one or more operational systems and/or functional relationships between the identified assets (Deyle, at least one para. 0222; “As noted above, the semantic mapping system 736 of the robot 100 can generate or update a semantic map associated with a location or setting in which the robot is located. As used herein, a “semantic map” refers to a map that includes one or more objects, entities, or individuals, and that includes a meaning, description, identity, or status of the identified objects, entities, or individuals. Semantic maps generated by the semantic mapping system can be stored locally by the robot, or can be uploaded to the central system 210 for storage in the semantic maps storage module 342.”).
Deyle does not explicitly teach that premises layout data for the premises, location data indicating a location with respect to the premises of the selected portion of the one or more operational system,
position data indicating positions of assets included in the selected portion of the one or more operational systems with respect to a layout of the premises and other assets, and
However, Klingensmith, in the same field of endeavor (Klingensmith, at least one para. 0083; “Generally described, autonomous and semi-autonomous robots can utilize mapping, localization, and navigation systems to map an environment utilizing sensor data obtained by the robots. Further, the robots can utilize the systems to perform navigation and/or localization in the environment and build navigation graphs that identify route data.”) teaches premises layout data for the premises, location data indicating a location with respect to the premises of the selected portion of the one or more operational system (Klingensmith, at least one para. 0084; “The present disclosure relates to the generation of a transformed virtual representation of the sensor data obtained by the robot (which can include a transformed navigation graph (e.g., transformed route data)) such that the transformed data visually aligns with a site model (e.g., image data) of a site (e.g., environment) using a computing system.”),
position data indicating positions of assets included in the selected portion of the one or more operational systems with respect to a layout of the premises and other assets (Klingensmith, at least one para. 0177; “The site model may identify a plurality of obstacles in the site of the robot. The plurality of obstacles may be areas within the site where the robot 100 may not traverse, may adjust navigation behavior prior to traversing, etc. based on determining the area is an obstacle. The plurality of obstacles may include static obstacles and/or dynamic obstacles. For example, the site model may identify one or more wall(s), stair(s), door(s), object(s), mover(s), etc. In some embodiments, the site model may identify obstacles that are affixed to, positioned on, etc. another obstacle. For example, the site model may identify an obstacle placed on a stair.”), and
The combination of Deyle, FAUSTINO, and Klingensmith are considered to be analogous to the claimed invention because both of them are in the same field as generating spatial data representing a device as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the spatial data of Deyle with teaching of Klingensmith in relation to the spatial data to generate the map with no changes in their respective functions, and the combination would have yielded predictable results. Furthermore, one of the ordinary skill in the art would have been motivated to make this modification so that the exact location of the robot can be identified based on the location data (Klingensmith; 0175).
Regarding claim 11, Deyle teaches (Currently Amended) A computer-implemented method (Deyle, at least one para. 0407; “Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented with a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described.”) comprising:
receiving navigation selection input indicating a selected portion of one or more operational systems at a premises (Deyle, at least one para. 0058; “The remote access interface can include a display for displaying information related to one or more components of FIG. 2, an input mechanism for receiving interactions from a user of the remote access interface, and a communicate interface enabling the remote access interface to communicate via the network 200. It should be noted that in some embodiments, the remote access interface can be implemented within hardware located remotely from the central system, the robots, or the other components of FIG. 2, for instance within a different building or on a different floor from the other components of FIG. 2.”) for performance of one or more service operations (Deyle, at least one para. 0219; “As noted above, the robot 100 can perform operations in addition to security operations. For instance, the robot can be located within an entrance or doorway and greet people as they enter or leave an area. The robot can request janitorial service in response to detect a mess within a proximity of the robot, and can act in self-defense in the event that someone tries to tamper with the robot or with another security system or infrastructure system.”);
generating spatial data representing the selected portion of the one or more operational systems based at least in part on the navigation selection input (Deyle, at least one para. 0120; “The semantic mapping system 736 is configured to generate or update a semantic map associated with a location or setting in which the robot 100 is located. For instance, the semantic mapping system can generate a map associated with a patrol route through a building floor as the robot moves through the space. The location of obstructions, and paths within the building floor can be detected by the scanners 726 and recorded onto the semantic map.”) (Deyle, at least one para. 0215; “In emergency situations, the robot can enable an external entity (such as an operator of the robot) to communicate with individuals within a proximity of the robot, for instance to assist with evacuation, to hold or disable elevators, to unlike exit doors, to provide information to response personnel, to sound alarms, to map out a location of a fire, to identify exit routes, to obstruct or locate unauthorized or dangerous individuals, and the like.”), wherein the one or more extensible object models are configured to mask the spatial data based on at least asset permissions data and access control data associated with a current user (Deyle, at least one para. 0210; “The robot 100 can perform one or more additional security operations, either independently of or in concert with one or more of the security operations described above. In one embodiment, the robot can operate in a privacy mode, where data collected and security operations performed by the robot are not communicated to an external entity (to preserve the privacy of individuals in proximity to the robot, or to reduce the likelihood that such data is intercepted during transmission) unless one or more security criteria are triggered (such as the detection of suspicious activity, the identification of an unauthorized individual, and the like). Operating in such a privacy mode further beneficially enables the robot to operate in proximity to sensitive data or objects (such as secret lab equipment or confidential/classified information) without a risk of such sensitive data or objects being discovered by unauthorized entities within access to the robot or the central system 210.”) , such that the spatial data is generated to comprise first data representing a first asset that the current user is authorized to access according to the asset permissions data and to exclude second data representing a second asset that the current user is not authorized to access according to the asset permissions data (Deyle, at least one para. 0210; “In some embodiments, the robot is configured to blur faces within captured images or videos to preserve the privacy of the individuals associated with the blurred faces. Such facial blurring may be performed by the robot on the captured images and videos only when the images or video are transmitted to an external entity. The robot can also beneficially blur computer monitors, documents, sensitive data or instruments, or any other objects or information as needed to satisfy security restrictions.”, In other words, when collected data is accessed by authorized personals, the authorized personals have access to some of the collected data (such as a body of an individual that requires preserving the privacy, clothing items of an individual, and other non-sensitive data). However, the authorized personals don’t have access to some of the collected data (such as the face of an individual that requires preserving the privacy, identification cards of an individual, and other sensitive data). For example, when there is a suspicious activity, the security personals are allowed to observe the transmitted data so that the severity of the current situation can be determined. However, sensitive data (facial data, documents, physical identifications, etc.) of an individual/item that requires preserving the privacy can be blurred to protect the identity.);
transmitting the spatial data to one or more service devices of different types, including at least one mobile computing device (Deyle, at least one para. 0128; “It should also be noted that the robot 100 includes component necessary to communicatively couple and control the components of the robot, including but not limited to: on-board computers, controllers, and processors; electric circuitry (e.g., motor drivers); computer memory; storage media (e.g., non-transitory computer-readable storage mediums, such as flash memory, hard drives, and the like); communication buses; cooling or heat dissipation systems; and the like.”) and at least one autonomous mobile device (Deyle, at least one para. 0164; “It should be noted that a robot and a hardware system can perform security operations as described herein autonomously or without the explicit instructions or involvement of a human (such as a security personnel 250 or an operator of the robot), beneficially reducing the amount of human interaction required to perform the security operations.”), each associated with the one or more operational systems (Deyle, at least one para. 0105; “The navigation system 710 can move the robot 100 in response to receiving navigation instructions, for instance from a user of the central system 210, from a security personnel 250, or from another robot. In some embodiments, the navigation system moves the robot as part of a patrol, routine, or security protocol. Navigation instructions can include an end location and can determine a route from a current location of the robot to the end location, for instance by detecting obstacles and/or paths from the current location to the end location, by selecting a path based on the detected obstacles and paths, and by moving the robot along the selected path until the robot arrives at the end location. In some embodiments, the navigation instructions can include a path, an ordered set of locations, an objective (e.g., “patrol the 4th floor”), or a map, and the navigation system can move the robot based on the navigation instructions.”); and
causing the one or more service devices to perform one or more navigation operations with respect to the premises and the one or more operational systems (Deyle, at least one para. 0181; “In implementing the security policy, the central system 210 can instruct one or more robots 100 to perform a task (such as patrol a route or intercept an individual that isn't authorized to be in a particular location), can instruct security cameras to change viewpoint and/or capture video of a location associated with a potential violation of a security policy, can request sensor data detected by one or more infrastructure systems 220 or security systems 230, and the like.”)
Even though Deyle teaches “semantic mapping”, Deyle does not explicitly teach that generating spatial data representing the selected portion of the one or more operational systems based at least in part on the navigation selection input and on one or more extensible object models comprising at least graph-based representations of assets, spatial features,
based at least in part on the spatial data
However, FAUSTINO, in the same field of endeavor (FAUSTINO, at least one para. 0013; “FIG. 1 illustrates an example of a network architecture for locating a user device in an indoor environment based at least on a building topology model. The network 100 includes one or more user devices 102(1) and 102(2). The user devices 102(1) and 102(2) may be smartphones, mobile devices, personal digital assistants (PDAs), or other electronic devices having a wireless communication function, that are capable of receiving input, processing the input, and generating output data.”) teaches generating spatial data representing the selected portion of the one or more operational systems based at least in part on the navigation selection input (FAUSTINO, at least one para. 0028; “The location of the individual IoT devices 206-222 may correspond to 3D coordinates within the boundaries of the building 200. For instance, the first IoT device 206 may be located at (X.sub.1, Y1, Z.sub.1), the second IoT device 208 may be located at (X.sub.2, Y2, Z.sub.2), the third IoT device 210 may be located at (X.sub.3, Y3, Z.sub.3), and so on. The 3D coordinates of the IoT devices 206-222 may be used to generate one or more building geometries 224 via a location server.”) and on one or more extensible object models comprising (FAUSTINO, at least one para. 00258; “As the IoT devices 212 and 214 on the second floor and the IoT devices 206-210 on the top floor are located, the building geometry may be updated, or a new building geometry may be generated. The updated building geometry or the new building geometry may indicate that the building 200 may include multiple levels. Accordingly, the building geometries and other spatial data from each floor of the building 200 may be layered to determine the building topology model 202 that represents the building 200.”);
based at least in part on the spatial data (FAUSTINO, at least one para. 0028; “The location of the individual IoT devices 206-222 may correspond to 3D coordinates within the boundaries of the building 200. For instance, the first IoT device 206 may be located at (X.sub.1, Y1, Z.sub.1), the second IoT device 208 may be located at (X.sub.2, Y2, Z.sub.2), the third IoT device 210 may be located at (X.sub.3, Y3, Z.sub.3), and so on.”)
Deyle and FAUSTINO are both considered to be analogous to the claimed invention because both of them are in the same field as generating spatial data representing a device as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine one or more navigation input selection of Deyle with teaching of FAUSTINO in relation to the spatial data gathering by known methods with no changes in their respective functions, and the combination would have yielded predictable results. One of the ordinary skill in the art would have been motivated to make this modification so that the topology model that represents a building can be accurately generated or updated (FAUSTINO; 0028).
Even though the combination of Deyle and FAUSTINO teaches “semantic mapping” and “spatial features”, the combination of Deyle and FAUSTINO does not explicitly teach that generating spatial data representing the selected portion of the one or more operational systems based at least in part on the navigation selection input and on one or more extensible object models comprising at least graph-based representations of assets,
However, Klingensmith, in the same field of endeavor (Klingensmith, at least one para. 0083; “Generally described, autonomous and semi-autonomous robots can utilize mapping, localization, and navigation systems to map an environment utilizing sensor data obtained by the robots. Further, the robots can utilize the systems to perform navigation and/or localization in the environment and build navigation graphs that identify route data.”) teaches generating spatial data representing the selected portion of the one or more operational systems based at least in part on the navigation selection input and on one or more extensible object models comprising at least graph-based representations of assets (Klingensmith, at least one para. 0121; “The high-level navigation module 220 can obtain (e.g., from the remote system 160 or the remote controller 10 or the topology component 250) and/or generate a series of route waypoints (As shown in FIGS. 3A and 3B) on the graph map 222 for a navigation route 212 that plots a path around large and/or static obstacles from a start location (e.g., the current location of the robot 100) to a destination. Route edges may connect corresponding pairs of adjacent route waypoints. In some examples, the route edges record geometric transforms between route waypoints based on odometry data (e.g., odometry data from motion sensors or image sensors to determine a change in the robot's position over time). The route waypoints 310 and the route edges 312 may be representative of the navigation route 212 for the robot 100 to follow from a start location to a destination location.”),
The combination of Deyle, FAUSTINO, and Klingensmith are considered to be analogous to the claimed invention because all of them are in the same field as generating spatial data representing a device as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine one or more navigation input selection of Deyle with teaching of Klingensmith in relation to the spatial data gathering by known methods with no changes in their respective functions, and the combination would have yielded predictable results. One of the ordinary skill in the art would have been motivated to make this modification so that topology model can represent accurate path and able to avoid obstacles (Klingensmith; 0125).
Regarding claim 12, The combination of Deyle, FAUSTINO, and Klingensmith teaches the limitations of claim 11, upon which the instant claim depends, as discussed supra. Further, Deyle teaches (Original) The method of claim 11, wherein the one or more service devices include a mobile computing device (Deyle, at least one para. 0128; “It should also be noted that the robot 100 includes component necessary to communicatively couple and control the components of the robot, including but not limited to: on-board computers”) operated by a technician associated with the one or more operational systems (Deyle, at least one para. 0054; “One or more security personnel 250 (for instance, one or more robot operators) can control or monitor the robots, and can adjust a robot deployment as needed”), and
the one or more navigation operations include generating and presenting a blueprint based at least in part on the spatial data (Deyle, at least one para. 0274; “In some embodiments, a floor plan for a building floor can be generated based on a semantic map generated by the robot 100. Likewise, a semantic map can be generated by the robot based on a building floor plan. As used herein, a floor plan can include a SLAM map.”),
containing the selected portion of the one or more operational systems with graphical elements representing assets of the one or more operational systems included in the selected portion of the one or more operational systems
the blueprint comprising a graphical depiction of a layout of an area of the premises (Deyle, at least one para. 0224; “the SLAM map 1700 illustrates a partial mapping of a building floor. The map includes an area 1702 that is navigable by a robot 100.”) containing the selected portion of the one or more operational systems with graphical elements representing assets of the one or more operational systems included in the selected portion of the one or more operational systems (Deyle, at least one para. 0270; “In some embodiments, the semantic map can highlight portions of the semantic map corresponding to identification information. For example, the portion of the map corresponding to the conference room 1906 can be highlighted a particular color, for instance a color corresponding to conference rooms”),
wherein positions of the graphical elements with respect to the graphical depiction of the layout of the area of the premises are based at least in part on the spatial data (Deyle, at least one para. 0267; “FIG. 18 illustrates the generation of a 3D semantic map by a robot”).
Regarding claim 13, The combination of Deyle, FAUSTINO, and Klingensmith teaches the limitations of claim 12, upon which the instant claim depends, as discussed supra. Further, Deyle teaches (Original) The method of claim 12, further comprising: retrieving service specification data associated with the assets (Deyle, at least one para. 0179; “In some embodiments, the central system can coordinate between multiple maps of the same location, for instance by updating older maps to include the location of objects that newer maps indicated have moved, or by incorporating types of information present in a first map but not a second map into the second map (for instance, the location of windows, whether a door is locked or unlocked, or the location of security cameras).”); and
presenting the service specification data for each of the assets as part of the blueprint.
Deyle does not explicitly teach that presenting the service specification data for each of the assets as part of the blueprint.
However, FAUSTINO, in the same field of endeavor (FAUSTINO, at least one para. 0013; “FIG. 1 illustrates an example of a network architecture for locating a user device in an indoor environment based at least on a building topology model. The network 100 includes one or more user devices 102(1) and 102(2). The user devices 102(1) and 102(2) may be smartphones, mobile devices, personal digital assistants (PDAs), or other electronic devices having a wireless communication function, that are capable of receiving input, processing the input, and generating output data.”) presenting the service specification data for each of the assets as part of the blueprint (FAUSTINO, at least one para. 0030; “For example, the building topology model 202 may show defined interior hallways, rooms, spaces, and other features that may be shown on building blueprints or floor plans.”).
The combination of Deyle, FAUSTINO, and Klingensmith are considered to be analogous to the claimed invention because both of them are in the same field as generating spatial data representing a device as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine one or more navigation input selection of Deyle with teaching of FAUSTINO in relation to the spatial data gathering by known methods with no changes in their respective functions, and the combination would have yielded predictable results. One of the ordinary skill in the art would have been motivated to make this modification so that various architectural or structural features of structurally identical buildings can be shown within the corresponding maps (FAUSTINO; 0030).
Regarding claim 14, The combination of Deyle, FAUSTINO, and Klingensmith teaches the limitations of claim 11, upon which the instant claim depends, as discussed supra. Further, Deyle teaches (Original) The method of claim 11, wherein the one or more service devices include an autonomous mobile device (Deyle, at least one para. 0340; “In some embodiments, the robot 100 can select a route (for instance, based on historical routes, building information, security information, and the like) and navigate the route autonomously, without human operator input or intervention.”) configured to perform service on assets of the one or more operational systems (Deyle, at least one para. 0211; “When the robot detects suspicious activity or when one or more security criteria are triggered while operating in darkness, the robot can activate a spotlight of the robot to highlight an object associated with the detected activity, can activate the buildings lights (e.g., by communicating with the central system 210 to turn on the lights for a particular area associated with the detected activity).”), and
the one or more navigation operations include the autonomous mobile device automatically moving through the premises to assets of the one or more operational systems included in the selected portion of the one or more operational systems based at least in part on the spatial data (Deyle, at least one para. 0211; “When the robot detects suspicious activity or when one or more security criteria are triggered while operating in darkness, the robot can activate a spotlight of the robot to highlight an object associated with the detected activity, can activate the buildings lights (e.g., by communicating with the central system 210 to turn on the lights for a particular area associated with the detected activity)., wherein the while operating in darkness can be seen as the autonomous mobile device automatically moving through the premises”).
Regarding claim 15, The combination of Deyle, FAUSTINO, and Klingensmith teaches the limitations of claim 11, upon which the instant claim depends, as discussed supra. Further, Deyle teaches (Previously Presented) The method of claim 11, wherein the one or more navigation operations include generating and presenting navigation data via the one or more service devices associated with the premises based at least in part on the spatial data (Deyle, at least one para. 0051; “In some embodiments, the robot can be deployed within a building, for instance on one or more floors or portions of floors of a building, can be deployed outside (for instance, in a parking lot), or can be deployed any other suitable location.”).
Regarding claim 16, The combination of Deyle, FAUSTINO, and Klingensmith teaches the limitations of claim 11, upon which the instant claim depends, as discussed supra. Further, Deyle teaches (Original) The method of claim 11, further comprising: presenting a navigation selection interface configured to receive the navigation selection input (Deyle, at least one para. 0058; “The remote access interface can include a display for displaying information related to one or more components of FIG. 2, an input mechanism for receiving interactions from a user of the remote access interface”); and
receiving, via the navigation selection interface, the navigation selection input (Deyle, at least one para. 0058; “a communicate interface enabling the remote access interface to communicate via the network 200.”).
Regarding claim 17, The combination of Deyle, FAUSTINO, and Klingensmith teaches the limitations of claim 16, upon which the instant claim depends, as discussed supra. Further, Deyle teaches (Original) The method of claim 16, wherein the navigation selection interface comprises one or more interactable elements configured to receive input indicative of selections of selected regions, sites, zones, and/or floors of the premises (Deyle, at least one para. 0054; “One or more security personnel 250 (for instance, one or more robot operators) can control or monitor the robots, and can adjust a robot deployment as needed (for instance, by allocating additional robots to a building floor on which a security violation is detected).”), (Deyle, at least one para. 0065; “The infrastructure interface 316 is configured to enable the central system 210 (or a user of the central system) to interact with one or more infrastructure systems 220 via the communication interface 310. For instance, the infrastructure interface can lock one or more doors within a building.”).
Regarding claim 18, The combination of Deyle, FAUSTINO, and Klingensmith teaches the limitations of claim 11, upon which the instant claim depends, as discussed supra. Further, Deyle teaches (Previously Presented) The method of claim 11, further comprising: retrieving the asset permissions data associated with a current user and the selected portion of the one or more operational systems (Deyle, at least one para. 0202; “In some embodiments, a user can use the security interface to change the patrol route of a robot by selecting among a set of patrol routes, or by manually drawing a patrol route on the local map”); and
causing the one or more service devices to perform the one or more navigation operations based at least in part on the asset permissions data (Deyle, at least one para. 0202; “upon which, a set of navigation instructions through a building portion corresponding to the drawn patrol route will be sent to the robot, and the robot can begin patrolling the drawn patrol route based on the navigation instructions”).
Regarding claim 19, The combination of Deyle, FAUSTINO, and Klingensmith teaches the limitations of claim 11, upon which the instant claim depends, as discussed supra. Further, Deyle teaches (Previously Presented) The method of claim 11, further comprising: retrieving from one or more access control systems for the premises access control data associated with a current user and one or more areas of the premises containing the selected portion of the one or more operational systems (Deyle, at least one para. 0208; “A user of the security interface can also configure security policies using the security interface. For instance, a user can select security policy components, including one or more of a location, a time range for which the security policy will be active, one or more categories of individuals to whom the security policy applies (e.g., employees, managers, staff, visitors, etc.), permitted access credentials, security policy exceptions, actions taken in the event of security policy violations (e.g., notify security, revoke user credentials, lock nearby doors, etc.), and the like.”); and
causing the one or more service devices to perform the one or more navigation operations based at least in part on the asset permissions data (Deyle, at least one para. 0208; “The security policy configured by the user of the security interface can be stored and subsequently implemented, for instance by patrolling robots”).
Regarding claim 20, Deyle teaches (Currently Amended) A computer program product comprising at least one non-transitory computer- readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising an executable portion configured to (Deyle, at least one para. 0407; “Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented with a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described.”):
receive navigation selection input indicating a selected portion of one or more operational systems at a premises (Deyle, at least one para. 0058; “The remote access interface can include a display for displaying information related to one or more components of FIG. 2, an input mechanism for receiving interactions from a user of the remote access interface, and a communicate interface enabling the remote access interface to communicate via the network 200. It should be noted that in some embodiments, the remote access interface can be implemented within hardware located remotely from the central system, the robots, or the other components of FIG. 2, for instance within a different building or on a different floor from the other components of FIG. 2.”) for performance of one or more service operations (Deyle, at least one para. 0219; “As noted above, the robot 100 can perform operations in addition to security operations. For instance, the robot can be located within an entrance or doorway and greet people as they enter or leave an area. The robot can request janitorial service in response to detect a mess within a proximity of the robot, and can act in self-defense in the event that someone tries to tamper with the robot or with another security system or infrastructure system.”);
generate spatial data representing the selected portion of the one or more operational systems based at least in part on the navigation selection input (Deyle, at least one para. 0120; “The semantic mapping system 736 is configured to generate or update a semantic map associated with a location or setting in which the robot 100 is located. For instance, the semantic mapping system can generate a map associated with a patrol route through a building floor as the robot moves through the space. The location of obstructions, and paths within the building floor can be detected by the scanners 726 and recorded onto the semantic map.”) (Deyle, at least one para. 0215; “In emergency situations, the robot can enable an external entity (such as an operator of the robot) to communicate with individuals within a proximity of the robot, for instance to assist with evacuation, to hold or disable elevators, to unlike exit doors, to provide information to response personnel, to sound alarms, to map out a location of a fire, to identify exit routes, to obstruct or locate unauthorized or dangerous individuals, and the like.”), wherein the one or more extensible object models are configured to mask the spatial data based on at least asset permissions data and access control data associated with a current user (Deyle, at least one para. 0210; “The robot 100 can perform one or more additional security operations, either independently of or in concert with one or more of the security operations described above. In one embodiment, the robot can operate in a privacy mode, where data collected and security operations performed by the robot are not communicated to an external entity (to preserve the privacy of individuals in proximity to the robot, or to reduce the likelihood that such data is intercepted during transmission) unless one or more security criteria are triggered (such as the detection of suspicious activity, the identification of an unauthorized individual, and the like). Operating in such a privacy mode further beneficially enables the robot to operate in proximity to sensitive data or objects (such as secret lab equipment or confidential/classified information) without a risk of such sensitive data or objects being discovered by unauthorized entities within access to the robot or the central system 210.”), such that the spatial data is generated to comprise first data representing a first asset that the current user is authorized to access according to the asset permissions data and to exclude second data representing a second asset that the current user is not authorized to access according to the asset permissions data (Deyle, at least one para. 0210; “In some embodiments, the robot is configured to blur faces within captured images or videos to preserve the privacy of the individuals associated with the blurred faces. Such facial blurring may be performed by the robot on the captured images and videos only when the images or video are transmitted to an external entity. The robot can also beneficially blur computer monitors, documents, sensitive data or instruments, or any other objects or information as needed to satisfy security restrictions.”, In other words, when collected data is accessed by authorized personals, the authorized personals have access to some of the collected data (such as a body of an individual that requires preserving the privacy, clothing items of an individual, and other non-sensitive data). However, the authorized personals don’t have access to some of the collected data (such as the face of an individual that requires preserving the privacy, identification cards of an individual, and other sensitive data). For example, when there is a suspicious activity, the security personals are allowed to observe the transmitted data so that the severity of the current situation can be determined. However, sensitive data (facial data, documents, physical identifications, etc.) of an individual/item that requires preserving the privacy can be blurred to protect the identity.);
transmit the spatial data to one or more service devices of different types, including at least one mobile computing device (Deyle, at least one para. 0128; “It should also be noted that the robot 100 includes component necessary to communicatively couple and control the components of the robot, including but not limited to: on-board computers, controllers, and processors; electric circuitry (e.g., motor drivers); computer memory; storage media (e.g., non-transitory computer-readable storage mediums, such as flash memory, hard drives, and the like); communication buses; cooling or heat dissipation systems; and the like.”) and at least one autonomous mobile device (Deyle, at least one para. 0164; “It should be noted that a robot and a hardware system can perform security operations as described herein autonomously or without the explicit instructions or involvement of a human (such as a security personnel 250 or an operator of the robot), beneficially reducing the amount of human interaction required to perform the security operations.”), each associated with the one or more operational systems (Deyle, at least one para. 0105; “The navigation system 710 can move the robot 100 in response to receiving navigation instructions, for instance from a user of the central system 210, from a security personnel 250, or from another robot. In some embodiments, the navigation system moves the robot as part of a patrol, routine, or security protocol. Navigation instructions can include an end location and can determine a route from a current location of the robot to the end location, for instance by detecting obstacles and/or paths from the current location to the end location, by selecting a path based on the detected obstacles and paths, and by moving the robot along the selected path until the robot arrives at the end location. In some embodiments, the navigation instructions can include a path, an ordered set of locations, an objective (e.g., “patrol the 4th floor”), or a map, and the navigation system can move the robot based on the navigation instructions.”); and
cause the one or more service devices to perform one or more navigation operations with respect to the premises and the one or more operational systems (Deyle, at least one para. 0181; “In implementing the security policy, the central system 210 can instruct one or more robots 100 to perform a task (such as patrol a route or intercept an individual that isn't authorized to be in a particular location), can instruct security cameras to change viewpoint and/or capture video of a location associated with a potential violation of a security policy, can request sensor data detected by one or more infrastructure systems 220 or security systems 230, and the like.”)
Even though Deyle teaches “semantic mapping”, Deyle does not explicitly teach that generate spatial data representing the selected portion of the one or more operational systems based at least in part on the navigation selection input and on one or more extensible object models comprising at least graph-based representations of assets, spatial features,
based at least in part on the spatial data
However, FAUSTINO, in the same field of endeavor (FAUSTINO, at least one para. 0013; “FIG. 1 illustrates an example of a network architecture for locating a user device in an indoor environment based at least on a building topology model. The network 100 includes one or more user devices 102(1) and 102(2). The user devices 102(1) and 102(2) may be smartphones, mobile devices, personal digital assistants (PDAs), or other electronic devices having a wireless communication function, that are capable of receiving input, processing the input, and generating output data.”) teaches generate spatial data representing the selected portion of the one or more operational systems based at least in part on the navigation selection input (FAUSTINO, at least one para. 0028; “The location of the individual IoT devices 206-222 may correspond to 3D coordinates within the boundaries of the building 200. For instance, the first IoT device 206 may be located at (X.sub.1, Y1, Z.sub.1), the second IoT device 208 may be located at (X.sub.2, Y2, Z.sub.2), the third IoT device 210 may be located at (X.sub.3, Y3, Z.sub.3), and so on. The 3D coordinates of the IoT devices 206-222 may be used to generate one or more building geometries 224 via a location server.”) and on one or more extensible object models comprising (FAUSTINO, at least one para. 00258; “As the IoT devices 212 and 214 on the second floor and the IoT devices 206-210 on the top floor are located, the building geometry may be updated, or a new building geometry may be generated. The updated building geometry or the new building geometry may indicate that the building 200 may include multiple levels. Accordingly, the building geometries and other spatial data from each floor of the building 200 may be layered to determine the building topology model 202 that represents the building 200.”);
based at least in part on the spatial data (FAUSTINO, at least one para. 0028; “The location of the individual IoT devices 206-222 may correspond to 3D coordinates within the boundaries of the building 200. For instance, the first IoT device 206 may be located at (X.sub.1, Y1, Z.sub.1), the second IoT device 208 may be located at (X.sub.2, Y2, Z.sub.2), the third IoT device 210 may be located at (X.sub.3, Y3, Z.sub.3), and so on.”)
Deyle and FAUSTINO are both considered to be analogous to the claimed invention because both of them are in the same field as generating spatial data representing a device as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine one or more navigation input selection of Deyle with teaching of FAUSTINO in relation to the spatial data gathering by known methods with no changes in their respective functions, and the combination would have yielded predictable results. One of the ordinary skill in the art would have been motivated to make this modification so that topology model that represents a building can be accurately generated or updated (FAUSTINO; 0028).
Even though the combination of Deyle and FAUSTINO teaches “semantic mapping” and “spatial features”, the combination of Deyle and FAUSTINO does not explicitly teach that generate spatial data representing the selected portion of the one or more operational systems based at least in part on the navigation selection input and on one or more extensible object models comprising at least graph-based representations of assets,
However, Klingensmith, in the same field of endeavor (Klingensmith, at least one para. 0083; “Generally described, autonomous and semi-autonomous robots can utilize mapping, localization, and navigation systems to map an environment utilizing sensor data obtained by the robots. Further, the robots can utilize the systems to perform navigation and/or localization in the environment and build navigation graphs that identify route data.”) teaches generate spatial data representing the selected portion of the one or more operational systems based at least in part on the navigation selection input and on one or more extensible object models comprising at least graph-based representations of assets (Klingensmith, at least one para. 0121; “The high-level navigation module 220 can obtain (e.g., from the remote system 160 or the remote controller 10 or the topology component 250) and/or generate a series of route waypoints (As shown in FIGS. 3A and 3B) on the graph map 222 for a navigation route 212 that plots a path around large and/or static obstacles from a start location (e.g., the current location of the robot 100) to a destination. Route edges may connect corresponding pairs of adjacent route waypoints. In some examples, the route edges record geometric transforms between route waypoints based on odometry data (e.g., odometry data from motion sensors or image sensors to determine a change in the robot's position over time). The route waypoints 310 and the route edges 312 may be representative of the navigation route 212 for the robot 100 to follow from a start location to a destination location.”),
The combination of Deyle, FAUSTINO, and Klingensmith are considered to be analogous to the claimed invention because all of them are in the same field as generating spatial data representing a device as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine one or more navigation input selection of Deyle with teaching of Klingensmith in relation to the spatial data gathering by known methods with no changes in their respective functions, and the combination would have yielded predictable results. One of the ordinary skill in the art would have been motivated to make this modification so that topology model can represent accurate path and able to avoid obstacles (Klingensmith; 0125).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/U.P.C./ Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665
