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 .
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.
Claim(s) 1-5, 8, 10-11, 14, 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sengupta (EP4047435), in view of Ditiberner (US 2018/0292374).
Claim 1, A method of operation of an unmanned autonomous vehicle, the method comprising: generating a map of a facility at least partially based on data collected by the unmanned autonomous vehicle; autonomously maneuvering the unmanned autonomous vehicle about the facility to perform inspections of assets of the facility based at least in part on the generated map; and using the unmanned autonomous vehicle to survey the facility for one or more abnormalities while autonomously maneuvering the unmanned autonomous vehicle about the facility.
"generating a map of a facility at least partially based on data collected by the unmanned autonomous vehicle" is taught by SENGUPTA. Paragraph [0013] discloses: "processing the captured information about the detected one or more anomalies to generate map information representing the detected one or more anomalies along with at least a partial map of the facility indicating access to the site of the detected one or more anomalies therein." Paragraph [0027] further discloses: "map-building module configured to process the captured information about the detected one or more anomalies to generate map information representing the detected one or more anomalies along with at least a partial map of the facility indicating access to the site of the detected one or more anomalies therein." SENGUPTA therefore teaches generating map information of the facility using information collected by the autonomous vehicle.
"autonomously maneuvering the unmanned autonomous vehicle about the facility to perform inspections of assets of the facility based at least in part on the generated map" is taught by SENGUPTA. Paragraph [0004] discloses: "navigating an autonomous vehicle to the site of the detected one or more anomalies in the facility." Paragraph [0011] further discloses: "navigating the autonomous vehicle to the site of the detected one or more anomalies in the facility based on the determined current location of the autonomous vehicle in the facility and the determined location information of the site of the detected one or more anomalies in the facility, by implementing Visual Simultaneous Localization and Mapping (VSLAM) technique." Paragraph [0015] additionally discloses: "implementing the autonomous vehicle to monitor the facility." These disclosures teach autonomous navigation within the facility using mapping and localization information while carrying out inspection and monitoring operations.
"using the unmanned autonomous vehicle to survey the facility for one or more abnormalities while autonomously maneuvering the unmanned autonomous vehicle about the facility" is taught by SENGUPTA. Paragraph [0004] discloses: "monitoring the facility to detect one or more anomalies therein." Paragraph [0016] similarly discloses: "The detection module is configured to monitor the facility to detect one or more anomalies therein." Paragraph [0029] further discloses: "The autonomous vehicle comprises a detection module. The detection module is configured to monitor the facility to detect one or more anomalies therein." , thus makes it obvious to surveying or monitoring the facility for abnormalities while the autonomous vehicle operates throughout the facility and within the knowledge of ordinary skilled artisan.
Claim 2, The method of claim 1, comprising: detecting, via the unmanned autonomous vehicle, an anomaly within the facility; autonomously maneuvering the unmanned autonomous vehicle toward the detected anomaly; and performing, via the unmanned autonomous vehicle, an intelligent inspection to investigate the detected anomaly.
"detecting, via the unmanned autonomous vehicle, an anomaly within the facility" is taught by SENGUPTA. Paragraph [0004] discloses: "monitoring the facility to detect one or more anomalies therein." Paragraph [0016] similarly discloses: "The detection module is configured to monitor the facility to detect one or more anomalies therein." These disclosures teach detecting an anomaly within the facility.
"autonomously maneuvering the unmanned autonomous vehicle toward the detected anomaly" is taught by SENGUPTA. Paragraph [0004] discloses: "navigating an autonomous vehicle to the site of the detected one or more anomalies in the facility." Paragraph [0029] similarly discloses: "a navigation module configured to navigate the autonomous vehicle to the site of the detected one or more anomalies in the facility." These disclosures teach autonomous movement toward the detected anomaly.
"performing, via the unmanned autonomous vehicle, an intelligent inspection to investigate the detected anomaly" is taught by SENGUPTA. Paragraph [0004] discloses: "capturing, by the autonomous vehicle, information about the detected one or more anomalies in the facility at the site of the facility." Paragraph [0096] further discloses: "This robot now navigates through the entire area to understand the scene and builds a map with all relevant information." These disclosures teach investigating the anomaly after reaching the anomaly site.
Claim 3, The method of claim 2, comprising generating and transmitting a report about the surveyed facility and/or the detected anomaly.
"generating ... a report about the surveyed facility and/or the detected anomaly" is taught by SENGUPTA. Paragraph [0013] discloses: "processing the captured information about the detected one or more anomalies to generate map information representing the detected one or more anomalies along with at least a partial map of the facility." Paragraph [0096] further discloses: "The map information representing the changed environment is sent to the rescue teams." These disclosures teach generating information representing the anomaly and facility condition.
"transmitting a report about the surveyed facility and/or the detected anomaly" is taught by SENGUPTA. Paragraph [0004] discloses: "communicating the captured information about the detected one or more anomalies." Paragraph [0014] discloses: "relaying the generated map information via one or more of: web portal, FTP server, web service, email." These disclosures teach transmitting the generated anomaly information.
Claim 4, The method of claim 2, wherein the detected anomaly comprises a gas leak, a liquid leak, an equipment malfunction, or some combination thereof.
"the detected anomaly comprises ... an equipment malfunction" is taught by SENGUPTA. Paragraph [0036] discloses: "the anomaly could be something small, such as a machine breakdown in the facility which could affect production efficiency thereof." This disclosure teaches an equipment malfunction.
"the detected anomaly comprises a gas leak" is not expressly taught by SENGUPTA.
DITIBERNER discloses in the Abstract: "Methods, systems and computer program products for detecting gas leaks using a drone are provided." Figure 7 further discloses: "Identify one or more regions with potential gas leaks in the geographical area" and "Fly the drone along the secondary flight path and capture a plurality of gas concentration samples." These disclosures teach detection and investigation of gas leaks using an unmanned vehicle.
It would have been obvious to modify the anomaly-management system of SENGUPTA with the gas-leak detection techniques of DITIBERNER because gas leaks are a known type of industrial anomaly and incorporating known gas sensing and gas-leak localization techniques would improve the anomaly-detection capabilities of the autonomous inspection vehicle.
Claim 5, The method of claim 1, wherein surveying the facility for the one or more abnormalities comprises detecting one or more actual anomalies that have occurred within the facility.
"detecting one or more actual anomalies that have occurred within the facility" is taught by SENGUPTA. Paragraph [0004] discloses: "monitoring the facility to detect one or more anomalies therein." Paragraph [0036] discloses: "the anomaly could be something small, such as a machine breakdown ... or large such as an accident like fire or explosion." These disclosures teach detecting actual existing anomalies within the facility.
Claim 8, The method of claim 1, comprising: receiving, via the unmanned autonomous vehicle, an initial map of the facility; updating, via the unmanned autonomous vehicle, the initial map of the facility based on data collected by the unmanned autonomous vehicle; and autonomously maneuvering the unmanned autonomous vehicle about the facility to perform inspections of assets of the facility based at least in part on the updated map.
"receiving ... an initial map of the facility" is taught by SENGUPTA. Paragraph [0085] discloses that the tracking unit receives "map information of the facility."
"updating ... the initial map of the facility based on data collected by the unmanned autonomous vehicle" is taught by SENGUPTA. Paragraph [0096] discloses: "This robot now navigates through the entire area to understand the scene and builds a map with all relevant information." Paragraph [0013] further discloses generation of map information from captured information.
"autonomously maneuvering ... based at least in part on the updated map" is taught by SENGUPTA. Paragraph [0011] discloses VSLAM-based navigation and Paragraph [0025] similarly discloses navigation using VSLAM.
Claim 10, The method of claim 1, wherein inspection of the assets comprises inspecting gas sensors of the facility for potential gas leaks.
SENGUPTA does not expressly teach inspecting gas sensors.
DITIBERNER teaches gas-leak detection using airborne sensors. Paragraph [0034] discloses: "the drone flies along a flight path ... and collects data regarding the presence of gas" and "taking periodic measurements of the concentration of the gas." Paragraph [0035] further discloses identifying regions having higher than expected concentrations of gas and collecting additional data.
It would have been obvious to utilize the autonomous facility-inspection platform of SENGUPTA to inspect facility gas-monitoring assets using the gas-detection techniques taught by DITIBERNER in order to improve facility safety and leak detection.
Claim 11, A method for identifying an anomaly in a processing facility, comprising: receiving an instruction to initiate an inspection mission, wherein the inspection mission is associated with one or more tasks to be performed by an unmanned autonomous vehicle; mounting a payload to the unmanned autonomous vehicle, wherein the payload is configured to capture data associated with the processing facility; directing the unmanned autonomous vehicle along a pre-defined path to capture data associated with the processing facility; receiving data indicative of one or more environmental conditions present at the processing facility; determining one or more optimal data capture locations based on the one or more environmental conditions; capturing additional data at the one or more optimal data capture locations; and identifying the anomaly based on the additional data captured at the one or more optimal data capture locations.
"receiving an instruction to initiate an inspection mission, wherein the inspection mission is associated with one or more tasks to be performed by an unmanned autonomous vehicle" is taught by SENGUPTA. Paragraph [0004] discloses autonomous anomaly-management operations including monitoring, anomaly detection, navigation, and information collection. The autonomous vehicle is deployed to perform anomaly investigation tasks within the facility.
"mounting a payload to the unmanned autonomous vehicle, wherein the payload is configured to capture data associated with the processing facility" is substantially taught by SENGUPTA. Paragraph [0017] discloses a perception module comprising a microphone configured to capture sound signals in the facility. Paragraph [0029] further discloses a perception module configured to capture information about detected anomalies. These teachings correspond to sensors carried by the autonomous vehicle for collecting facility data.
"directing the unmanned autonomous vehicle along a pre-defined path to capture data associated with the processing facility" is taught by SENGUPTA. Paragraph [0011] discloses navigation using VSLAM. Paragraph [0025] similarly discloses navigating the autonomous vehicle to a destination based on location information and VSLAM. These disclosures teach directing the vehicle along a navigation path to collect information.
"receiving data indicative of one or more environmental conditions present at the processing facility" is not expressly taught by SENGUPTA.
DITIBERNER teaches receiving environmental-condition data. Figure 6 discloses: "Create, using the processor, a gas dispersion model for the geographic area for modeling wind speed around objects in the three-dimensional model of the geographical area." The gas measurements and wind-speed information constitute environmental-condition data used during anomaly investigation.
"determining one or more optimal data capture locations based on the one or more environmental conditions" is not expressly taught by SENGUPTA.
DITIBERNER teaches this limitation. Figure 7 discloses: "Identify one or more regions with potential gas leaks in the geographical area" and "Create one or more secondary flight plans for the one or more regions with potential gas leaks based on the three-dimensional model of the geographical area." The identified regions and secondary flight plans correspond to optimal data-capture locations selected based upon environmental conditions and collected measurements.
"capturing additional data at the one or more optimal data capture locations" is not expressly taught by SENGUPTA.
DITIBERNER teaches this limitation. Figure 7 discloses: "Fly the drone along the secondary flight path and capture a plurality of gas concentration samples."
"identifying the anomaly based on the additional data captured at the one or more optimal data capture locations" is taught by the combination. SENGUPTA teaches anomaly detection and investigation, while DITIBERNER teaches collecting additional gas-concentration measurements at targeted locations to identify gas leaks, It would have been obvious to modify the anomaly-management system of SENGUPTA to employ the environmental-condition-based secondary inspection and data collection techniques of DITIBERNER because targeted follow-up inspection of suspected anomaly regions improves anomaly localization accuracy and provides more detailed information regarding the anomaly source.
Claim 14, The method of claim 11, wherein the anomaly comprises a gas leak, a liquid leak, an equipment malfunction, or some combination thereof.
"equipment malfunction" is taught by SENGUPTA. Paragraph [0036] discloses: "the anomaly could be something small, such as a machine breakdown in the facility."
"gas leak" is not expressly taught by SENGUPTA.
DITIBERNER teaches gas-leak detection. The Abstract discloses: "Methods, systems and computer program products for detecting gas leaks using a drone are provided." Figure 7 further discloses identifying regions having potential gas leaks and collecting gas-concentration measurements, It would have been obvious to incorporate gas-leak detection techniques into the anomaly-management platform of SENGUPTA because gas leaks are a known industrial anomaly requiring autonomous detection and investigation.
Claim 18, A method, comprising: receiving, via a processor, instructions to perform an inspection of an asset; receiving, via the processor, data comprising: a location of the asset; one or more possible routes between a current location of an unmanned autonomous vehicle and the location of the asset; and an indication of one or more possible obstructions along the one or more possible route or traffic data along the one or more possible routes; selecting, via the processor, a particular route of the one or more possible routes; autonomously navigating, via the processor, the unmanned autonomous vehicle along the selected particular route to the asset; inspecting the asset via one or more on-board sensors of the unmanned autonomous vehicle; and navigating, via the processor, the unmanned autonomous vehicle along the selected particular route to an end of the selected particular route.
"receiving ... a location of the asset" is taught by SENGUPTA. Paragraph [0004] discloses determining location information of the anomaly site.
"one or more possible routes" is taught by SENGUPTA through path planning and navigation. Paragraph [0011] discloses VSLAM navigation.
"an indication of one or more possible obstructions" is taught by SENGUPTA through obstacle avoidance functionality associated with autonomous navigation.
"selecting ... a particular route" and "autonomously navigating ... to the asset" are taught by SENGUPTA through navigation to the anomaly site using location information and VSLAM.
"inspecting the asset via one or more on-board sensors" is taught by SENGUPTA through use of the perception module to capture information at the site, thus would have been obvious to ordinary skilled artisan.
Claim 19, The method of claim 18, comprising: detecting, via the one or more on-board sensors of the unmanned autonomous vehicle, an obstruction along the selected particular route; providing, to an edge device, route data and data associated with the obstruction detected by the one or more on-board sensors of the unmanned autonomous vehicle; receiving, from the edge device, one or more alternative routes; selecting a particular alternative route of the one or more alternative routes; and navigating the unmanned autonomous vehicle along the selected particular alternative route.
SENGUPTA teaches obstacle detection and path adjustment.
However, SENGUPTA does not expressly teach transmitting obstruction information to an edge device and receiving alternative routes from the edge device.
DITIBERNER teaches communication between the drone and an external processing system that processes collected information. The drone communicates with a processing system to support mission operations, It would have been obvious to employ an external processing device to assist route replanning because remote processing of navigation information was a known technique for improving autonomous vehicle navigation efficiency.
Claim 20, The method of claim 18, comprising: detecting, via the one or more on-board sensors of the unmanned autonomous vehicle, an obstruction along the selected particular route; selecting, via the unmanned autonomous vehicle, a particular alternative route from one or more alternative routes based at least in part on data associated with the obstruction detected by the one or more on-board sensors of the unmanned autonomous vehicle; and navigating the unmanned autonomous vehicle along the selected particular alternative route.
SENGUPTA teaches autonomous obstacle avoidance and path replanning. The navigation functionality determines paths and adjusts navigation based upon detected conditions. The autonomous vehicle uses VSLAM navigation and obstacle avoidance while navigating through the facility.
DITIBERNER further teaches dynamically modifying flight paths and generating secondary flight plans based upon detected conditions and collected measurements, It would have been obvious to modify the route selected by SENGUPTA in response to detected obstacles using known dynamic route-adjustment techniques such as those taught by DITIBERNER because doing so predictably improves autonomous navigation reliability and mission completion.
Allowable Subject Matter
Claims 15-17 are allowed.
Claims 6-7, 9, 12-13 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MASUD AHMED whose telephone number is (571)270-1315. The examiner can normally be reached M-F 9:00-8:30 PM PST with IFP.
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MASUD . AHMED
Primary Examiner
Art Unit 3657A
/MASUD AHMED/Primary Examiner, Art Unit 3657
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