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 June 2nd, 2026 has been entered. Claims 1-20 are pending in the application. Applicant’s amendments to the Claims 1-20 have overcome the rejections previously set forth in the Non-Final Office Action mailed May 14th 2026. A second search has been performed to address the material amended in the aforementioned claims. Newly found references Levanti (US 11836420 B1), Pilot Institute (NPL: Drone Use Cases in the Oil and Gas Industry), Pensec (NPL: Smart Anomaly Detection and Monitoring of Industry 4.0 by Drones), Huang (US 20220043441 A1), Iynoolkhan (US 11012526 B1), Dittberner (US 20180292374 A1), Agerstam (US 20190138423 A1) and Gan (US 20220221374 A1) were used for the amended claim limitations.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
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.
Claims 1, 2, 4, 5, 6, 7, 11, 12, 13, 15, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Dasgupta (US 20180158197 A1) in view of Levanti (US 11836420 B1; see attachment for paragraph numbers) and Pilot Institute (NPL: Drone Use Cases in the Oil and Gas Industry).
Regarding claim 1:
Dasgupta teaches:
A system comprising:
one or more sensors configured to obtain three-dimensional (3D) data in a facility (Dasgupta: images of the physical environment captured by the onboard visual sensors are processed to extract semantic information about detected objects, Abstract); and
a processor communicatively coupled to the one or more sensors, wherein the processor is configured to execute instruction (Dasgupta: in some embodiments, the navigation system 120 and associated subsystems, may be implemented as instructions stored in memory and executable by one or more processors [0027]) to:
generate a 3D model of the facility based on a first 3D data received from the one or more sensors at a first time (Dasgupta: data received from sensors onboard UAV 100 can be processed to generate a 3D map of the surrounding physical environment [0089]);
receive a second 3D data from the one or more sensors at a second time after the first time (Dasgupta: These depth estimates can then be used to continually update a generated 3D model of the physical environment taking into account motion estimates for the image capture device (i.e., UAV 100) through the physical environment. [0089]);
Dasgupta fails to teach:
determine an event based on the second 3D data and the 3D model, wherein the event comprises one or more of an anomaly, a change in an actuator position of an equipment, or a change in the equipment in response to an unauthorized tampering, vibration, or weather;
output a notification via an electronic device based on the event.
Levanti teaches:
output a notification via an electronic device based on the event (Levanti: If analysis of the 3D model and the location of a person (based on the location device) indicates that the person has entered a restricted area and the person is not authorized for the restricted area, then the geo-fencing application may generate an alarm message and send the alarm message to a security guard (e.g., to a display device, as an email, text message, etc.) or to a monitoring application that processes the alarm message to inform security personnel, paragraph (84)).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Levanti with Dasgupta. Outputting a notification via an electronic device based on the event, as in Levanti, would benefit the Dasgupta teachings by enhancing security of confidential items.
Dasgupta in view of Levanti fails to explicitly teach:
determine an event based on the second 3D data and the 3D model, wherein the event comprises one or more of an anomaly, a change in an actuator position of an equipment, or a change in the equipment in response to an unauthorized tampering, vibration, or weather;
Pilot Institute teaches:
generate a 3D model of the facility based on a first 3D data received from the one or more sensors at a first time (Pilot Institute: In the case of drones with LiDAR or photogrammetry capabilities, these are used to create digital twins. Essentially, 3D models of real-world assets and digital twins are valuable for making closer inspections of facilities or taking repeated volume or length measurements, Pg. 2, par. 5)
receive a second 3D data from the one or more sensors at a second time after the first time (Pilot Institute: it’s a simple matter of looking at previously generated 3D models to check for signs of damage over time, Pg. 2, par. 7; see Note 1A)
determine an event based on the second 3D data and the 3D model, wherein the event comprises one or more of an anomaly (Pilot Institute: Another advantage of a digital twin is that it can be stored perpetually and analyzed again if needed. Does it seem like one part of the facility is slowly deteriorating? Then it’s a simple matter of looking at previously generated 3D models to check for signs of damage over time, Pg. 2, par. 7), a change in an actuator position of an equipment, or a change in the equipment in response to an unauthorized tampering, vibration, or weather;
Note 1A: Because Pilot Institute teaches that previously generated 3D models can be checked to “check for signs of damage over time”, the Examiner submits that one of ordinary skill in the art would understand Pilot Institute to teach generating one or more 3D models at a first and second time to compare for damage.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Pilot Institute with Dasgupta in view of Levanti. Determining an event based on the second 3D data and the 3D model, as in Pilot Institute, would benefit the Dasgupta in view of Levanti teachings by enabling a UAV to detect anomalies that develop slowly over time: “Monitoring very slow and barely imperceptible changes has been an established use case for digital twins in many infrastructures” (Pilot Institute, Pg. 2, par. 7).
Regarding claim 2:
Dasgupta in view of Levanti and Pilot Institute teaches:
The system of claim 1 (as shown above), comprising an unmanned autonomous vehicle (UAV) having a first sensor of the one or more sensors (Levanti: In embodiments, one or more of the cameras may be mobile (e.g., on a robot/drone or carried by a person), paragraph (21)), wherein the one or more sensors comprises a second sensor at a fixed position in the facility (Levanti: some or all of the cameras may be at a fixed location of the facility (e.g., mounted onto a wall, ceiling, etc.), paragraph (21)), the first and second sensors each comprise a Light Detection and Ranging (LiDAR) sensor configured to generate LiDAR data (Levanti: For example, infrared sensor, light detection and ranging (LIDAR), and terahertz sensors may provide one or more streams of data concurrently with the video streams 106; paragraph 28), and the 3D model comprises a point cloud generated from the LiDAR data (Levanti: In embodiments, the model constructor may construct the model based on additional input of distance measurements that are provided by a LIDAR sensor(s), paragraph (29)).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Levanti with Dasgupta. Having one or more sensors on a drone and at a fixed location in a facility generate a 3D model via LiDAR, as in Levanti, would benefit the Dasgupta teachings by enabling 3D models of a facility to be continuously updated without manual effort: “Although the initial CAD files of a facility may be helpful for facility management in making decisions on how to make use of the facility, the CAD files may become less accurate and less useful over time. For example, over a few years (or even a few weeks), various walls may be added or removed and different equipment may be added removed. As initial design documents (CAD files, floor plans, etc.) become more out of date, it may become more difficult and time consuming for facility managers to plan changes to a facility or to ensure that a facility is meeting various safety standards.” (Levanti, paragraph (2))
Regarding claim 4:
Dasgupta in view of Levanti and Pilot Institute teaches:
The system of claim 1 (as shown above), wherein the one or more sensors comprise a Light Detection and Ranging (LiDAR) sensor (Dasgupta: Computer vision may also be applied using sensing technologies other than cameras, such as LIDAR. [0091]).
Regarding claim 5:
Dasgupta in view of Levanti and Pilot Institute teaches:
The system of claim 1 (as shown above), wherein the processor is configured to execute instructions to determine a classification of one or more objects within the 3D model of the facility (Levanti: In embodiments, object recognition may refer to a collection of one or more computer vision tasks (e.g. classification, paragraph (16); see Note 5A), and the classification of one or more objects comprises equipment (Levanti: For example, if the analyzer identifies that the object is a particular type of equipment (e.g., a ladder), then the analyzer may determine that the object is temporary, but if the analyzer identifies that the object is another type of equipment (e.g., new production equipment), then the analyzer may determine that the object is permanent (e.g., relative to more temporary objects like ladders), paragraph (36)), structural components of a building (Levanti: The 3D model analyzer may monitor construction over time. For example, as 3D models are constructed/updated, the 3D model analyzer determine, based on the 3D model, whether a structure that is being built matches design specifications for the structure, paragraph (63)), vehicles (Levanti: For example, a newly designed car may be removed (e.g., based on object recognition by the 3D analyzer that identifies the car as a confidential object) from the constructed 3D model, paragraph (58)), and humans (Levanti: If analysis of the 3D model and the location of a person (based on the location device) indicates that the person has entered a restricted area and the person is not authorized for the restricted area, then the geo-fencing application may generate an alarm message and send the alarm message to a security guard, paragraph (84))
Note 5A: Levanti detects objects in the 3D model of a facility, using a 3D model analyzer: “the 3D model analyzer may provide data indicating one or more types of detected objects (e.g., highlighting fire extinguishers in the 3D model as part of a safety analysis, types of production equipment, walls, etc.).” (paragraph (82). Therefore, the Examiner submits that the object detection (classification) may be performed within the 3D model of a facility.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Levanti with Dasgupta. Determining a classification of one or more objects within the 3D model of the facility, as in Levanti, would benefit the Dasgupta teachings by improving security by removing confidential objects from the 3D model: “For example, a newly designed car may be removed (e.g., based on object recognition by the 3D analyzer that identifies the car as a confidential object) from the constructed 3D model before the 3D model is provided to a viewing application or other application” (Levanti, paragraph (58)).
Regarding claim 6:
Dasgupta in view of Levanti and Pilot Institute teaches:
The system of claim 5 (as shown above), wherein the processor is configured to execute instructions to determine a classification of one or more areas within the 3D model of the facility (Levanti: the 3D modeling service may be configured to remove from the constructed 3D model certain areas, objects, and/or structures that are identified in the constructed 3D model in order to maintain confidentiality of the areas/object/structures, paragraph (58); see Note 6A), and the classification of one or more areas comprises one or more hazardous or restricted areas of the facility, and the event comprises a human entering the one or more hazardous or restricted areas (Levanti: If analysis of the 3D model and the location of a person (based on the location device) indicates that the person has entered a restricted area and the person is not authorized for the restricted area, then the geo-fencing application may generate an alarm message and send the alarm message to a security guard; paragraph (84)).
Note 6A: Levanti teaches: “For example, the 3D modeling service may identify, based on the 3D model, one or more objects at a facility as confidential objects and in response, remove the confidential objects from the 3D model.” In this context (paragraph (58)), Levanti teaches objects as interchangeable with areas and structures: “certain areas, objects, and/or structures that are identified in the constructed 3D model”. Therefore, one of ordinary skill in the art would understand Levanti to teach that both objects and areas may be classified as confidential or otherwise.
Regarding claim 7:
Dasgupta in view of Levanti and Pilot Institute teaches:
The system of claim 6 (as shown above), wherein the processor is configured to execute instructions to determine the classification of the one or more objects and the one or more areas within the 3D model via a first artificial intelligence (Al) model (Dasgupta: In some embodiments, the object detection system 142 can utilize a deep convolutional neural network for object detection [0049]; see Note 7A), and the first Al model is configured to implement machine learning to classify the one or more objects and the one or more areas within the 3D model (see Note 7A and Note 6A).
Note 7A: In [0049] cited above, Dasgupta teaches that object detection can be performed by a deep convolutional neural network (i.e., a first AI model). Levanti teaches: “In embodiments, object recognition may refer to a collection of one or more computer vision tasks (e.g. classification, detection, segmentation, localization, etc.) […] In embodiments, any suitable type of image processing models may be implemented (e.g., neural networks such as convolutional neural networks (CNNs).” (paragraph (16), emphasis added). Therefore, the Examiner submits that it would be obvious to one of ordinary skill in the art to classify objects/areas by using the deep convolutional neural network.
Regarding claim 11:
Claim 11 is substantially similar to claim 1, and is therefore rejected for similar reasons. Claim 11 contains the following notable differences:
Claim 11 claims a method instead of a system. In the rejection of claim 1, it was shown that Dasgupta in view of Levanti and Pilot Institute teaches the claimed system. It follows that Dasgupta in view of Levanti and Pilot Institute teaches the corresponding method.
Regarding claim 12:
Dasgupta in view of Levanti and Pilot Institute teaches:
The method of claim 11 (as shown above), wherein the one or more sensors comprise a Light Detection and Ranging (LiDAR) sensor coupled to an unmanned autonomous vehicle (UAV), a fixed location, or any combination thereof (Pilot Institute: Drones can be equipped with high-definition cameras, thermal cameras, and even LiDAR sensors that can allow for the inspection and recording of different types of data, Pg. 2, par. 3).
Regarding claim 13:
Dasgupta in view of Levanti and Pilot Institute teaches:
The method of claim 11 (as shown above), wherein the 3D model comprises
building components (Levanti: In embodiments, an updated 3D model may include any number of any type of new objects (e.g., new fire extinguishers, walls, etc.), different coverage gaps, fewer objects, and/or any other number of changes to the structure and/or items at the facility, paragraph (57)),
humans (Levanti: If analysis of the 3D model and the location of a person (based on the location device) indicates that the person has entered a restricted area and the person is not authorized for the restricted area, then the geo-fencing application may generate an alarm message, paragraph (84); see Note 13A),
vehicles (Levanti: For example, the 3D modeling service may identify, based on the 3D model, one or more objects at a facility as confidential objects and in response, remove the confidential objects from the 3D model. For example, a newly designed car may be removed (e.g., based on object recognition by the 3D analyzer that identifies the car as a confidential object) from the constructed 3D model, paragraph (58); see Note 13B),
and the equipment other than vehicles, wherein the equipment comprises one or more tanks, pumps, compressors, valves, separators, reactors, combustion engines, machines, tools (Levanti: In embodiments, an updated 3D model may include any number of any type of new objects (e.g., new fire extinguishers, walls, etc.), paragraph (57), see Note 13C), meters, wire, transformers, electric motors, actuators, piping, distillation towers, or any combination thereof.
Note 13A: Levanti teaches that a person may be detected via “analysis of the 3D model”, which requires that the person is included in the 3D model.
Note 13B: Levanti teaches an example where a car is removed from a constructed 3D model, due to the 3D analyzer determining that the car is a confidential object. However, in paragraph (57), Levanti teaches that the car is still part of the original constructed 3D model before removal: “a newly designed car may be removed […] from the constructed 3D model”. Therefore, Levanti teaches that the 3D model may contain vehicles.
Note 13C: The Examiner considered the fire extinguisher taught by Levanti to be a tool.
Regarding claim 15:
Claim 15 is substantially similar to claim 5, and is therefore rejected for similar reasons. Claim 15 contains the following notable differences:
Claim 15 claims a method instead of a system. In the rejection of claim 1, it was shown that Dasgupta in view of Levanti and Pilot Institute teaches the claimed system. It follows that Dasgupta in view of Levanti and Pilot Institute teaches the corresponding method.
Claim 15 discusses “classifying one or more objects in the 3D model” as opposed to “determine a classification of one or more objects within the 3D model of the facility” in claim 5. As such, Claim 15 is broader in scope than claim 5 but otherwise recites the same limitations.
Regarding claim 18:
Dasgupta teaches:
A tangible and non-transitory machine readable medium comprising instructions to cause a processing system to:
receive three-dimensional (3D) data from one or more sensors in a facility (Dasgupta: images of the physical environment captured by the onboard visual sensors are processed to extract semantic information about detected objects, Abstract);
generate a 3D model of the facility based on a first 3D data received from the one or more sensors at a first time (Dasgupta: data received from sensors onboard UAV 100 can be processed to generate a 3D map of the surrounding physical environment [0089]);
receive a second 3D data from the one or more sensors at a second time after the first time (Dasgupta: These depth estimates can then be used to continually update a generated 3D model of the physical environment taking into account motion estimates for the image capture device (i.e., UAV 100) through the physical environment. [0089]);
Dasgupta fails to explicitly teach:
determine a classification of one or more objects and one or more areas within the 3D model via a first artificial intelligence (Al) model,
wherein the classification of one or more objects comprises equipment, structural components of a building, vehicles, and humans,
wherein the classification of one or more areas comprises hazardous or restricted areas;
determine an event via a second artificial intelligence (AI) model based on the second 3D data and the 3D model,
wherein the event comprises one or more of an anomaly, a change in an actuator position of an equipment, or a change in the equipment in response to an unauthorized tampering, vibration, or weather; and
output a notification via an electronic device based on the event.
Levanti teaches:
determine a classification of one or more objects and one or more areas within the 3D model via a first artificial intelligence (Al) model (Levanti: In embodiments, object recognition may refer to a collection of one or more computer vision tasks (e.g. classification, detection, segmentation, localization, etc.) […] In embodiments, any suitable type of image processing models may be implemented (e.g., neural networks such as convolutional neural networks (CNNs), paragraph (16), emphasis added)),
wherein the classification of one or more objects comprises equipment (Levanti: For example, if the analyzer identifies that the object is a particular type of equipment (e.g., a ladder), then the analyzer may determine that the object is temporary, but if the analyzer identifies that the object is another type of equipment (e.g., new production equipment), then the analyzer may determine that the object is permanent (e.g., relative to more temporary objects like ladders), paragraph (36)), structural components of a building (Levanti: The 3D model analyzer may monitor construction over time. For example, as 3D models are constructed/updated, the 3D model analyzer determine, based on the 3D model, whether a structure that is being built matches design specifications for the structure, paragraph (63)), vehicles (Levanti: For example, a newly designed car may be removed (e.g., based on object recognition by the 3D analyzer that identifies the car as a confidential object) from the constructed 3D model, paragraph (58)), and humans (Levanti: If analysis of the 3D model and the location of a person (based on the location device) indicates that the person has entered a restricted area and the person is not authorized for the restricted area, then the geo-fencing application may generate an alarm message and send the alarm message to a security guard, paragraph (84)),
wherein the classification of one or more areas comprises hazardous or restricted areas (Levanti: As another example, a secret entrance to the facility or a keypad may be removed (e.g., based on object recognition of the keypad or based on configuration input from a user that identifies the door's location as a confidential area), paragraph (58));
output a notification via an electronic device based on the event (Levanti: If analysis of the 3D model and the location of a person (based on the location device) indicates that the person has entered a restricted area and the person is not authorized for the restricted area, then the geo-fencing application may generate an alarm message and send the alarm message to a security guard (e.g., to a display device, as an email, text message, etc.) or to a monitoring application that processes the alarm message to inform security personnel, paragraph (84)).
Dasgupta in view of Levanti still fails to teach:
determine an event via a second artificial intelligence (AI) model based on the second 3D data and the 3D model,
wherein the event comprises one or more of an anomaly, a change in an actuator position of an equipment, or a change in the equipment in response to an unauthorized tampering, vibration, or weather; and
Pilot Institute teaches:
generate a 3D model of the facility based on a first 3D data received from the one or more sensors at a first time (Pilot Institute: In the case of drones with LiDAR or photogrammetry capabilities, these are used to create digital twins. Essentially, 3D models of real-world assets and digital twins are valuable for making closer inspections of facilities or taking repeated volume or length measurements, Pg. 2, par. 5)
receive a second 3D data from the one or more sensors at a second time after the first time (Pilot Institute: it’s a simple matter of looking at previously generated 3D models to check for signs of damage over time, Pg. 2, par. 7; see Note 1A)
determine an event based on the second 3D data and the 3D model, wherein the event comprises one or more of an anomaly (Pilot Institute: Another advantage of a digital twin is that it can be stored perpetually and analyzed again if needed. Does it seem like one part of the facility is slowly deteriorating? Then it’s a simple matter of looking at previously generated 3D models to check for signs of damage over time, Pg. 2, par. 7), a change in an actuator position of an equipment, or a change in the equipment in response to an unauthorized tampering, vibration, or weather;
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Pilot Institute with Dasgupta in view of Levanti. Determining an event based on the second 3D data and the 3D model, as in Pilot Institute, would benefit the Dasgupta in view of Levanti teachings by enabling a UAV to detect anomalies that develop slowly over time: “Monitoring very slow and barely imperceptible changes has been an established use case for digital twins in many infrastructures” (Pilot Institute, Pg. 2, par. 7).
Regarding claim 20:
Dasgupta in view of Levanti and Pilot Institute teaches:
The medium of claim 18 (as shown above), wherein the one or more sensors comprise a Light Detection and Ranging (LiDAR) sensor (Dasgupta: Computer vision may also be applied using sensing technologies other than cameras, such as LIDAR. [0091]).
Claims 3, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Dasgupta (US 20180158197 A1) in view of Levanti (US 11836420 B1), Pilot Institute (NPL: Drone Use Cases in the Oil and Gas Industry), and Pensec (NPL: Smart Anomaly Detection and Monitoring of Industry 4.0 by Drones).
Regarding claim 3:
Dasgupta in view of Levanti and Pilot Institute teaches:
The system of claim 2 (as shown above), wherein the processor is configured to execute instructions, in response to the event, to
obtain sensor feedback from the first sensor and one or more additional sensors of the UAV, the one or more additional sensors of the UAV comprise one or more gas sensors, audio sensors, temperature sensors (Pilot Institute: This can also be complemented by drones with thermal imaging or laser sensors that can detect compromised material to help trace the source of any gas leak, Pg. 3), wind sensors, leak sensors (Pilot Institute: Specialized sensors can be mounted on drones that are then flown into areas with suspected or potential gas leaks, Pg. 3), pressure sensors, humidity sensors, or any combination thereof.
Dasgupta in view of Levanti and Pilot Institute fails to explicitly teach:
wherein the processor is configured to execute instructions, in response to the event, to
control the UAV to move to a location proximate to the event and obtain sensor feedback from the first sensor and one or more additional sensors of the UAV,
Pensec teaches:
wherein the processor is configured to execute instructions, in response to the event, to control the UAV to move to a location proximate to the event (Pensec: The goal of our approach is to move the drone over the anomaly that is detected by the java application from the reading of the sensors, Pg. 3, Section IV: Methodology, par. 3) and
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Pensec with Dasgupta in view of Levanti, Pilot Institute, and Levanti. Controlling the UAV to move to a location proximate to the anomaly, as in Pensec, would benefit the Dasgupta in view of Levanti and Pilot Institute teachings by enabling the use of drone to target a specific area that may not be accessible by conventional robots: “The main goal of this project is to be able to make a continuous analysis in an industry and thus on surfaces which can be very large and thus where it can be difficult to install multiple cameras working together.” (Pensec: Pg. 3, Section B: Drone, par. 4); “For monitoring an industrial control system, drones are a lot more effective than ground robots. They are faster and can fly over the machines or the personnel present in the industry.” (Pensec, Pg. 8, Section VI: Conclusions, par. 1)
Regarding claim 9:
Dasgupta in view of Levanti and Pilot Institute teaches:
The system of claim 7 (as shown above),
Dasgupta in view of Levanti and Pilot Institute fails to teach:
wherein the processor is configured to determine the event based on the second 3D data via a second artificial intelligence (AI) model configured to implement machine learning to determine the event.
Pensec teaches:
wherein the processor is configured to determine the event based on the second 3D data via a second artificial intelligence (AI) model (Pensec: To detect that an anomaly is present in a image [sic], a convolutional neural network can be used, Pg. 6, Section C: Anomaly confirmation and diagnosis, par. 3) configured to implement machine learning to determine the event (Pensec: The drone descend to the desired height and the Raspberry Pi takes a picture of the sensor that poses a problem and analyzes via the neural network if the anomaly is confirmed or denied, Pg. 6, Section V: Results, par. 3).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Pensec with Dasgupta in view of Levanti and Pilot Institute. Determining the event based on data from a second AI model, as in Pensec, would benefit the Dasgupta in view of Levanti and Pilot Institute teachings by enabling the use of drone to target a specific area that may not be accessible by conventional robots: “It allows to get results very quickly for a given dataset. Once a model is trained, it is possible to detect objects on a video in real time. Moreover, YoloV5 proposes different size of the model. For this reason, this system was chosen in our approach.” (Pensec, Pg. 6, Section C: Anomaly confirmation and diagnosis, par. 3)
Regarding claim 16:
The method of claim 12 (as shown above), comprising
determining a classification of one or more areas and one or more objects in the 3D model (Levanti: the 3D modeling service may be configured to remove from the constructed 3D model certain areas, objects, and/or structures that are identified in the constructed 3D model in order to maintain confidentiality of the areas/object/structures, paragraph (58); see Note 6A) via a first artificial intelligence (Al) model (Dasgupta: In some embodiments, the object detection system 142 can utilize a deep convolutional neural network for object detection [0049]; see Note 7A), and
monitoring for a plurality of events in the 3D model via a second artificial intelligence (Al) model (Levanti: In embodiments, any suitable type of image processing models may be implemented (e.g., neural networks such as convolutional neural networks (CNNs); paragraph (16)), wherein monitoring comprises tracking movement of the one or more objects in the facility (Levanti: In embodiment, the 3D model analyzer or 3D modeling service may receive, from the client/user, input indicating movement forward (or backward) in time; paragraph (60)) and anomality detection in the facility (Levanti: If analysis of the 3D model and the location of a person (based on the location device) indicates that the person has entered a restricted area and the person is not authorized for the restricted area, then the geo-fencing application may generate an alarm message and send the alarm message to a security guard, paragraph (84)).
Pensec teaches:
monitoring for a plurality of events in the 3D model via a second artificial intelligence (Al) model (Pensec: To detect that an anomaly is present in a image [sic], a convolutional neural network can be used, Pg. 6, Section C: Anomaly confirmation and diagnosis, par. 3), wherein monitoring comprises tracking movement of the one or more objects (see Note 16A) in the facility and anomality detection in the facility (Pensec: As aforementioned, drones can be used in production lines […] to prevent or detect anomalies on this production line and collect data, Pg. 2, Section 2: Related Works, par. 4).
Levanti teaches: “In embodiment, the 3D model analyzer […] may receive, from the client/user, input indicating movement forward (or backward) in time” (paragraph (60)). Levanti also teaches detection of anomalies, for example, an incident where a human enters a confidential area: “If analysis of the 3D model and the location of a person (based on the location device) indicates that the person has entered a restricted area and the person is not authorized for the restricted area, then the geo-fencing application may generate an alarm message and send the alarm message to a security guard” (paragraph (84)). When the teachings of Pensec are combined with Dasgupta in view of Levanti and Pilot Institute, it would be obvious to one of ordinary skill in the art to track movement of the one or more objects while monitoring for events.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Pensec with Dasgupta in view of Levanti and Pilot Institute. Determining the event based on data from a second AI model, as in Pensec, would benefit the Dasgupta in view of Levanti and Pilot Institute teachings by enabling the use of drone to target a specific area that may not be accessible by conventional robots: “It allows to get results very quickly for a given dataset. Once a model is trained, it is possible to detect objects on a video in real time. Moreover, YoloV5 proposes different size of the model. For this reason, this system was chosen in our approach.” (Pensec, Pg. 6, Section C: Anomaly confirmation and diagnosis, par. 3)
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Dasgupta (US 20180158197 A1) in view of Levanti (US 11836420 B1; see attachment for paragraph numbers) and Pilot Institute (NPL: Drone Use Cases in the Oil and Gas Industry) and Huang (US 20220043441 A1).
Regarding claim 8:
Dasgupta in view of Levanti and Pilot Institute teaches:
The system of claim 1 (as shown above),
Dasgupta in view of Levanti and Pilot Institute fails to explicitly teach:
wherein the processor is configured to execute instructions to send an actuation signal to at least one equipment of the facility based on the event, and the at least one equipment comprises a valve, a pump, a compressor, a reactor, or any combination thereof.
Huang teaches:
wherein the processor is configured to execute instructions to send an actuation signal to at least one equipment of the facility (Huang: Where embodiments of the present disclosure are applied to chemical processing and manufacturing use cases, the actions may include removal of a supply of fuel, removal of a supply electricity, turning off of fuel supply/pumps, turning off of reagent supply/pumps, shutting down a drilling subsystem[…] [0060]) based on the event (Huang: the method 400 includes analyzing, by the one or more processors, the dataset to detect anomalies associated with the dataset [0060]), and the at least one equipment comprises a valve, a pump, a compressor, a reactor, or any combination thereof.
Note 8A: Wikipedia teaches: “An actuator is a component of a machine that is responsible for moving and controlling a mechanism or system”. Huang teaches that their AI system may control various parts of the facility, such as turning off fuel pumps. The Examiner submits that one of ordinary skill in the art would understand that a signal sent by the system to control fuel pumps, as taught by Huang, would be analogous to an actuation signal.
Regarding claim 17:
Claim 17 is substantially similar to claim 8, and is therefore rejected for similar reasons. Claim 17 contains the following notable differences:
Claim 17 claims a method instead of a system. In the rejection of claim 8, it was shown that Dasgupta in view of Levanti, Pilot Institute, and Huang teaches the claimed system. It follows that Dasgupta in view of Levanti, Pilot Institute, and Huang teaches the corresponding method.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Dasgupta (US 20180158197 A1) in view of Levanti (US 11836420 B1), Pilot Institute (NPL: Drone Use Cases in the Oil and Gas Industry), Metzler (US 20220005332 A1) and Iynoolkhan (US 11012526 B1; see attachment for paragraph numbers).
Regarding claim 10:
Dasgupta in view of Levanti and Pilot Institute teaches:
The system of claim 1 (as shown above), wherein the processor is configured to execute instructions to monitor for a plurality of events based on the second 3D data and the 3D model, wherein the plurality of events comprise the anomaly (Pilot Institute: Another advantage of a digital twin is that it can be stored perpetually and analyzed again if needed. Does it seem like one part of the facility is slowly deteriorating? Then it’s a simple matter of looking at previously generated 3D models to check for signs of damage over time, Pg. 2, par. 7),
Dasgupta in view of Levanti and Pilot Institute fails to explicitly teach:
wherein the processor is configured to execute instructions to monitor for a plurality of events based on the second 3D data and the 3D model, wherein the plurality of events comprise
the change in the actuator position of the equipment,
and the change in the equipment in response to the unauthorized tampering, the vibration, or the weather, wherein the weather comprises wind, a flood, or an earthquake.
Metzler teaches:
wherein the processor is configured to execute instructions to monitor for a plurality of events based on the second 3D data and the 3D model (Metzler: The state can be “semantic”, e.g. person detected, door-open, etc. or also refer to at least one simple change in the data, e.g. a significant difference between a point cloud or an image of a sub-object acquired some hours ago with a point cloud or image acquired now [0287]; see Note 10B), wherein the plurality of events comprise
the change in the actuator position of the equipment (Metzler: Such state derivation means are e.g. detectors which detect change from one state to another (for example a sensor which detects opening of a window) [0004]; see Note 10A), and
Note 10A: Metzler teaches: “The system 1 further comprises state derivation means 6 for derivation of states at or within the building, e.g. if a person is present and/or light switched on or off in room 50a at time T1, the door 51c is open at time T2 and closed at time T3, etc. The state derivation means 6 can be integrated in the surveillance sensors 4, for example the ones embodied as contact sensors 42a,b or the surveillance robot 43, which detect e.g. a motion as an event on itself.” [0287]. In other words, Metzler teaches that state changes such as the positions of switches or doors may be detected as open or closed, on or off, etc. The specification of the present application similarly teaches: “The monitoring system 116 may compare the first location and orientation to the second location and orientation of the valve 186 (e.g., valve actuator 188) to determine an event, such as an unplanned or unauthorized change to the valve 186 orientation (e.g., closed, open).” [0052]. Therefore, the Examiner submits that Metzler teaches detecting a change in actuator position (e.g., the light switch) by analyzing the 3D model.
Note 10B: Metzler teaches that the state change can be detected from a change in point cloud in [0287] cited above. The Examiner submits that the point cloud is analogous to a 3D model, as the points of the point cloud are distributed in 3D space: “The verification information available by the fine measurement of second survey sensor 111 is for example a high resolution 3D-point cloud” [0349].
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Metzler with Dasgupta in view of Levanti and Pilot Institute. Determining the event based on the change in actuator position, as in Metzler, would benefit the Dasgupta in view of Levanti and Pilot Institute teachings by enhancing the classification analysis: “A person entering a room 50a-50c during night and not switching on the light could indicate that the person might be an intruder, therefore such a state has a high probability to be classified as “anomalous” and “critical”.” (Metzler, [0312]).
Dasgupta in view of Levanti, Pilot Institute, and Metzler still fails to teach:
wherein the processor is configured to execute instructions to monitor for a plurality of events based on the second 3D data and the 3D model, wherein the plurality of events comprise
the change in the equipment in response to the unauthorized tampering, the vibration, or the weather, wherein the weather comprises wind, a flood, or an earthquake.
Iynoolkhan teaches:
wherein the processor is configured to execute instructions to monitor for a plurality of events, wherein the plurality of events comprise
the change in the equipment (Iynoolkhan: As another example, Field UAVs 204 may be at an accident site and may capture real-time images of the disposition of the vehicles involved, capture real-time weather and/or traffic conditions, capture detailed images of damage to vehicles, and so forth, paragraph (27); see Note 10B) in response to the unauthorized tampering, the vibration, or the weather, wherein the weather comprises wind, a flood, or an earthquake (Iynoolkhan: The incident may be any type of incident that may require an insurance claim to be filed and processed. For example, the incident may be an accident, a home fire, a weather related incident (e.g., hail damage, tornado damage, hurricane damage, wind damage, flood damage, damage due to a fallen tree, etc.) paragraph (26)).
Note 10B: Iynoolkhan teaches detection of damage to vehicles as an example, but also teaches that any “item” may be analyzed: “In some embodiments, machine learning system 210 may estimate characteristics of a repair by analyzing a size of the damaged portion, age of the item, availability of replacement parts and/or accessories, and a cost of repair.” The Examiner submits that within the scope of claim 10, a vehicle may be considered equipment, and also notes that one of ordinary skill in the art would be motivated to apply the teachings of Iynoolkhan to objects other than vehicles because of the teachings of Iynoolkhan cited above.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Iynoolkhan with Dasgupta in view of Levanti, Pilot Institute and Metzler. Determining an event based on damage caused by weather, as in Iynoolkhan, would benefit the Dasgupta in view of Levanti, Pilot Institute, and Metzler teachings by enabling the system to determine a cost of repair or replacement in real time: “A field vehicle may receive field data from the one or more UAVs in real-time, analyze the field data by utilizing on-board edge-computing capabilities, determine materials needed and estimate a cost of repair or replacement” (Iynoolkhan, paragraph (11)).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Dasgupta (US 20180158197 A1) in view of Levanti (US 11836420 B1), Pilot Institute (NPL: Drone Use Cases in the Oil and Gas Industry) and Dittberner (US 20180292374 A1).
Regarding claim 14:
Dasgupta in view of Levanti and Pilot Institute teaches:
The method of claim 11, comprising
classifying one or more areas of the 3D model (Levanti: the 3D modeling service may be configured to remove from the constructed 3D model certain areas, objects, and/or structures that are identified in the constructed 3D model in order to maintain confidentiality of the areas/object/structures, paragraph (58); see Note 6A),
wherein the event is based on the second 3D data (Levanti: For example, the 3D modeling service may compare the recent or updated 3D model with one or more previously generated 3D models of the facility, paragraph (34)) and the one or more areas of the 3D model, wherein the event is a human, a vehicle, or an animal entering the one or more areas (Levanti: If analysis of the 3D model and the location of a person (based on the location device) indicates that the person has entered a restricted area and the person is not authorized for the restricted area, then the geo-fencing application may generate an alarm message, paragraph(84)).
Dasgupta in view of Levanti and Pilot Institute fails to teach:
wherein the one or more areas comprise one or more hazardous or restricted areas of the facility having a high-pressure-high temperature (HPHT), a potential hazardous gas concentration, a high noise, or any combination thereof,
Dittberner teaches:
wherein the one or more areas comprise one or more hazardous or restricted areas (see Note 14A) of the facility having a high-pressure-high temperature (HPHT), a potential hazardous gas concentration (Dittberner: The drone collects data regarding the presence of gas while flying the initial flight path 308 and identifies one or more regions in the geographic area that include higher than expected concentrations of gas. [0035]), a high noise, or any combination thereof,
Note 14A: When the teachings of Dittberner are combined with Dasgupta in view of Levanti and Pilot Institute, it would be obvious to determine the region with higher than expected concentrations of gas to be a restricted area, because Dittberner teaches: “Not only do these leaks have a major environmental impact, they can present a health hazard. If these leaks go unregulated or undetected, these hazards can become potentially detrimental and also a present a major financial burden on the operating companies.” [0003]. Similarly, Pilot Institute teaches: “One of the major hazards of the oil and gas industry is the leak of hazardous gases. In some cases, these gases can be imperceptible to the human nose and can only be detected using industry-grade detectors. The problem with this approach is that it may still require the exposure of personnel to hazardous gases before gas leaks can be detected.” (Pilot Institute, Pg. 3).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Dittberner with Dasgupta in view of Levanti and Pilot Institute. Determining one or more hazardous or restricted areas of the facility having a potential hazardous gas concentration, as in Dittberner, would benefit the Dasgupta in view of Levanti and Pilot Institute teachings by enabling the drone to detect and classify areas of the facility as hazardous gas leak areas without requiring personnel to physically enter the area: “One of the major hazards of the oil and gas industry is the leak of hazardous gases. In some cases, these gases can be imperceptible to the human nose and can only be detected using industry-grade detectors” (Pilot Institute, Pg. 3).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Dasgupta (US 20180158197 A1) in view of Levanti (US 11836420 B1), Pilot Institute (NPL: Drone Use Cases in the Oil and Gas Industry), Metzler (US 20220005332 A1) Iynoolkhan (US 11012526 B1), Agerstam (US 20190138423 A1) and Gan (US 20220221374 A1).
Regarding claim 19:
Dasgupta in view of Levanti and Pilot Institute teaches:
The medium of claim 18 (as shown above), wherein the instructions cause the processing system to monitor for a plurality of events based on the second 3D data and the 3D model (Pilot Institute: In the case of drones with LiDAR or photogrammetry capabilities, these are used to create digital twins. Essentially, 3D models of real-world assets and digital twins are valuable for making closer inspections of facilities or taking repeated volume or length measurements, Pg. 2, par. 5), wherein the plurality of events comprise
the anomaly (Pilot Institute: Another advantage of a digital twin is that it can be stored perpetually and analyzed again if needed. Does it seem like one part of the facility is slowly deteriorating? Then it’s a simple matter of looking at previously generated 3D models to check for signs of damage over time, Pg. 2, par. 7)
Dasgupta in view of Levanti and Pilot Institute fails to explicitly teach:
wherein the instructions cause the processing system to monitor for a plurality of events based on the second 3D data and the 3D model, wherein the plurality of events comprise
the change in the actuator position of the equipment,
the change in the equipment in response to the unauthorized tampering,
the change in the equipment in response to the vibration,
and the change in the equipment in response to the weather
Metzler teaches:
wherein the processor is configured to execute instructions to monitor for a plurality of events based on the second 3D data and the 3D model (Metzler: The state can be “semantic”, e.g. person detected, door-open, etc. or also refer to at least one simple change in the data, e.g. a significant difference between a point cloud or an image of a sub-object acquired some hours ago with a point cloud or image acquired now [0287]; see Note 10B), wherein the plurality of events comprise
the change in the actuator position of the equipment (Metzler: Such state derivation means are e.g. detectors which detect change from one state to another (for example a sensor which detects opening of a window) [0004]; see Note 10A), and
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Metzler with Dasgupta in view of Levanti and Pilot Institute. Determining the event based on the change in actuator position, as in Metzler, would benefit the Dasgupta in view of Levanti and Pilot Institute teachings by enhancing the classification analysis: “A person entering a room 50a-50c during night and not switching on the light could indicate that the person might be an intruder, therefore such a state has a high probability to be classified as “anomalous” and “critical”.” (Metzler, [0312]).
Dasgupta in view of Levanti, Pilot Institute and Metzler fails to explicitly teach:
wherein the instructions cause the processing system to monitor for a plurality of events based on the second 3D data and the 3D model, wherein the plurality of events comprise
the change in the equipment in response to the unauthorized tampering,
the change in the equipment in response to the vibration,
and the change in the equipment in response to the weather
Iynoolkhan teaches:
wherein the processor is configured to execute instructions to monitor for a plurality of events, wherein the plurality of events comprise
the change in the equipment (Iynoolkhan: As another example, Field UAVs 204 may be at an accident site and may capture real-time images of the disposition of the vehicles involved, capture real-time weather and/or traffic conditions, capture detailed images of damage to vehicles, and so forth, paragraph (27); see Note 10B) in response to the weather (Iynoolkhan: The incident may be any type of incident that may require an insurance claim to be filed and processed. For example, the incident may be an accident, a home fire, a weather related incident (e.g., hail damage, tornado damage, hurricane damage, wind damage, flood damage, damage due to a fallen tree, etc.) paragraph (26)).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Iynoolkhan with Dasgupta in view of Levanti, Pilot Institute and Metzler. Determining an event based on damage caused by weather, as in Iynoolkhan, would benefit the Dasgupta in view of Levanti, Pilot Institute, and Metzler teachings by enabling the system to determine a cost of repair or replacement in real time: “A field vehicle may receive field data from the one or more UAVs in real-time, analyze the field data by utilizing on-board edge-computing capabilities, determine materials needed and estimate a cost of repair or replacement” (Iynoolkhan, paragraph (11)).
Dasgupta in view of Levanti, Pilot Institute, Metzler, and Iynoolkhan fails to explicitly teach:
wherein the instructions cause the processing system to monitor for a plurality of events based on the second 3D data and the 3D model, wherein the plurality of events comprise
the change in the equipment in response to the unauthorized tampering,
the change in the equipment in response to the vibration,
Agerstam teaches:
wherein the instructions cause the processing system to monitor for a plurality of events, wherein the plurality of events comprise
the change in the equipment in response to the unauthorized tampering (Agerstam: For example, the anomaly detector 114 receives from the inference generator 604 probability values indicative of likelihoods that sensor(s) in the sensor deployment 200 are malfunctioning, being tampered with, etc. The example anomaly detector 114 generates a flag (e.g., sets a bit to 1, clears a bit to 0, stores an “anomaly detected” value, etc.) if it determines that the probability values are sufficiently indicative of anomalous operation [0063]),
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Agerstam with Dasgupta in view of Levanti, Pilot Institute, Metzler, and Iynoolkhan. Determining an event based on a change in the equipment in response to the unauthorized tampering, as in Agerstam, would benefit the Dasgupta in view of Levanti, Pilot Institute, Metzler and Iynoolkhan teachings by enabling the system to predict whether an anomaly will occur more accurately: “the anomaly detector 114 can utilize a statistical-based algorithm that can determine if a sensor is malfunctioning, is tampered with, etc. based on the prediction value” (Agerstam, [0072]).
Dasgupta in view of Levanti, Pilot Institute, Metzler, Iynoolkhan, and Agerstam fails to explicitly teach:
wherein the instructions cause the processing system to monitor for a plurality of events based on the second 3D data and the 3D model, wherein the plurality of events comprise
the change in the equipment in response to the vibration,
Gan teaches:
wherein the instructions cause the processing system to monitor for a plurality of events, wherein the plurality of events comprise
the change in the equipment (Gan: Signals measured under the same or substantially similar contexts deviating from the profiles by a threshold amount may be indicative of anomalous or abnormal operation of the machine, and may indicate the presence of a loose or malfunctioning component [0028]) in response to the vibration (Gan: A profile may be a group of vibration, acoustic, and/or joint signals measured within a contiguous and finitely bounded segment of time [0028]),
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Gan with Dasgupta in view of Levanti, Pilot Institute, Metzler, Iynoolkhan, and Agerstam. Determining an event based on the change in equipment in response to the vibration, as in Gan, would benefit the Dasgupta in view of Levanti, Pilot Institute, Metzler, Iynoolkhan, and Agerstam teachings by increasing the accuracy of detecting loose or malfunctioning parts: “such a system provides the advantage that the system can identify not only when an acoustic signal is anomalous or a vibrational signal is anomalous, but can also identify when an acoustic signal does not correspond with a vibrational signal in the expected way, even if the component acoustic signal and vibrational signal are otherwise non-anomalous. In this way, the system is capable of greater accuracy in identifying anomalies that indicate the presence of a loose or malfunctioning part.” (Gan, [0017])
Conclusion
The Examiner identified potential limitation(s) in the specification that would overcome the prior art rejections under 103 if amended into the claims. Note that in such a situation, further search and consideration would be required:
The prior art Gan cited in the rejection of claim 19 teaches: “In some embodiments, such as where the system is integrated, in communication with, or otherwise exercises some amount of control over the machine, the system may stop or slow down the machine, shut down malfunctioning or suspected to be malfunctioning components of the machine, et cetera” [0030]. However, the specification of the present application teaches: “In some embodiments, in response to a determination that a component (e.g., valve actuator 188), has moved position or orientation (e.g., due to vibrations) the monitoring system 116 may send a signal to adjust a parameter (e.g., speed, power, flow rate) of the associated equipment (e.g., compressors, reactor) to reduce vibrations. For example, in response to a determination that a position or orientation of a valve hand (e.g., valve actuator 188) has moved to an undesirable location or orientation, the monitoring system 116 may send a signal to a reactor to reduce a flow rate, thereby reducing vibrations.” [0065].
The specification of the present application recites: “For example, the UAVs 114 may perform a random or sequenced movement plan (e.g., flight plan, drive plan) configured to position the dynamic sensors 112 proximate to objects (e.g., objects 106) within the facility 104.” [0032] (emphasis added). The Examiner notes that although the prior art Pensec appears to further reference prior art Chen (NPL: Path Planning in Large Area Monitoring by Drones) discussing a sequenced movement plan, the prior art cited in this final action does not teach a random movement plan configured to position the dynamic sensors 112 proximate to objects.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VINCENT ALEXANDER PROVIDENCE whose telephone number is (571)270-5765. The examiner can normally be reached Monday-Thursday 8:30-5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, King Poon can be reached at (571)270-0728. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/VINCENT ALEXANDER PROVIDENCE/Examiner, Art Unit 2617 /KING Y POON/Supervisory Patent Examiner, Art Unit 2617
Prosecution Insights