DETAILED ACTION
1 Applicant’s amendment received on June 4, 2026 in which claims 1, 24 and 25 were amended, has been fully considered and entered, but the arguments are moot in view of the new grounds of rejection.
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 .
REMARKS
Claims 14-17 and 21 were indicated as being allowable in the last Office Action mailed on March 4th , 2026. However, new references were found, and these claims are no longer considered as being allowable in light of the new prior art. While the Examiner considered and entered the new amendment to claims 1, 24 and 25, a new non-final is being provided in order to give the Applicant time to appreciate the new rejection along with the newly cited prior art.
Claim Rejections - 35 USC § 103
2. 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.
3. 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.
4. Claims 1-13, 18-20, and 23-25 are rejected under 35 U.S.C. 103 as being unpatentable over Massarik et al. (US Patent Application Publication no. 2021/0033715) in view of Hamminga et al. (US Patent Application Publication no. 2022/0189326).
Regarding claim 1, Massarik discloses a method for generating one or more alerts based on the detection of one or more unmanned aerial systems (See Massarik’s Abstrack, and [0057]) the method comprising: receiving image data and position data from at least one imaging device, wherein the image data includes an image of an unmanned aerial (See Massarik [0081] “For example, the video data may comprise images of the west side of a known building in the area. Thus, the system operator may determine that the drone is located west of the building. The scale of the building within the images may be also used in determining the location of the drone”); determining a position of the unmanned aerial system based on at least one of the image data and the position data (See Massarik [0020]); and generating an alert indicating detection of the unmanned aerial system (See Massarik [0020] “Decoded data, such as the video data, may be presented to a system operator via a control terminal 104a, 104b (referred to generically as a control terminal 104). Further, an alert may be generated based on the decoded data.).
It is noted that Massarik is silent about detecting an unmanned aerial system based on the image data using a trained machine learning model.
However, Hamminga teaches a method for generating one or more alerts (See Hamminga [0013]), and detecting an unmanned aerial system based on the image data using a trained machine learning model (See Hamminga [0009], [0071]).
Therefore, it is considered obvious that one skilled in the art, before the effective filing date of the claimed invention, would recognize the advantage of modifying Massarik to incorporate Hamminga’s teachings to detect an unmanned aerial system based on the image data using a trained machine learning model. The motivation for performing such a modification in Massarik is because the deep learning model is a trained deep residual learning framework for image classification tasks as taught by Hamminga (See Hamminga [0026]-[0027]).
As per claim 2, most of the limitations of these claims have been noted in the above rejection of claim 1. I addition, the combination of Massarik and Hamminga further teaches wherein the position data comprises data corresponding to at least one of a geographic location of the at least one imaging device, a bearing of the at least one imaging device, and an observation angle of the at least one imaging device (See Massarik [0079], [0080] and [0083]).
As per claim 3, the combination of Massarik and Hamminga further teaches determining a distance between the at least one imaging device and the one or more unmanned aerial systems based on the image data and the position data (See Massarik [0083]).
As per claim 4, the combination of Massarik and Hamminga further teaches determining an altitude of the one or more unmanned aerial systems based on the observation angle of the imaging device and the distance between the imaging device and the one or more unmanned aerial systems (See Massarik [0022] and [0083]).
As per claim 5, the combination of Massarik and Hamminga further teaches determining a type of the unmanned aerial system based on the image data (See Massarik [0022]).
As per claim 6, the combination of Massarik and Hamminga further teaches determining at least one of a tactical use, a flight time capacity, a payload capacity, and a control frequency based on the type of unmanned aerial system (See Hamminga [0025]-[0027]).
As per claim 7, the combination of Massarik and Hamminga further teaches tracking an unmanned aerial system based on the image data and the position data from a first imaging device and the image data and the position data from a second imaging device (See Hamminga [0012]-[0013]).
As per claim 8, the combination of Massarik and Hamminga further teaches determining a position of the unmanned aerial system based on at least one of the image data and the position data received at a first time (See Hamminga [0012]); and updating the position of the unmanned aerial system based on at least one of the image data and the position data received at a second time (See Hamminga [0013], [0051]).
As per claim 9, the combination of Massarik and Hamminga further teaches detecting a first unmanned aerial system based on a first image (See Hamminga [0050], [0047] “While only one UAV 102 is shown in FIG. 1, multiple UAVs or even swarms of UAVs may be detected and classified.”) ; detecting a second unmanned aerial system based on a second image (See Hamminga [0023], [0024] Note that there must be at least 2 UAVs to classify the UAVs) as noted in [0047]; and determining that the first unmanned aerial system is the same unmanned aerial system as the second unmanned aerial system (See Hamminga [0059] and [0061] “radar plots from different digital radar images representing the same aerial object may be associated into a track for the aerial object by the electronic processing unit”.
As per claim 10, the combination of Massarik and Hamminga further teaches detecting a plurality of unmanned aerial systems based on the image data using a trained machine learning model; and determining a position of each of the detected unmanned aerial systems based on at least one of the image data and the position data (See Hamminga [0025], [0027] and [0059]).
As per claims 11, the combination of Massarik and Hamminga further teaches retraining the machine learning model based on the detected unmanned aerial system and the image data (See Hamminga [0025], and [0059]).
As per claim 12, the combination of Massarik and Hamminga further teaches transmitting the alert to at least one of a user device comprising the imaging device, a command system, and at least one user device located within a threshold distance from the position of the unmanned aerial system (See Massarik [0100], [0020], [0031]).
As per claim 13, the combination of Massarik and Hamminga further teaches wherein the threshold distance is a user-configurable threshold (See Massarik [0029], and [0071]).
As per claim 18, the combination of Massarik and Hamminga further teaches receiving a second request from a user device; and in response to receiving the second request, causing the user device to display a map, wherein the map depicts a geographic area associated with the position of the one or more detected unmanned aerial systems (See Massarik [0081]-[0082], The Applicant should note that the user can make multiple requests using the GUI of [0081]-[0082]).
As per claims 19-20, the combination of Massarik and Hamminga further teaches a map which comprises an icon depicting the location of the imaging device, and wherein the map comprises a user selectable icon indicating a position of the one or more unmanned aerial systems (See Massarik [0071], [0082]). The Applicant is reminded that Massarik GUI 105 with display may provide icons/applications to perform some specific tasks.
As per claim 23, the combination of Massarik and Hamminga further teaches wherein the imaging device is a surveillance camera (See Massarik [0021], and [0023]).
As per claim 24, Massarik discloses a computing system for generating one or more alerts based on the detection of one or more unmanned aerial systems in image data (See Massarik’s Abstrack, and [0057]), the system comprising one or more processors (See Massarik processor 32 of Fig. 1B, or [0040]), memory, and one or more programs stored in the memory for execution by the one or more processors (See Massarik [0040]), the one or more programs including instructions that when executed by the one or more processors (See Massarik [0040]) cause the system to: receive image data and position data from at least one imaging device, wherein the image data includes an image of an unmanned aerial (See Massarik [0081] “For example, the video data may comprise images of the west side of a known building in the area. Thus, the system operator may determine that the drone is located west of the building. The scale of the building within the images may be also used in determining the location of the drone”); determine a position of the unmanned aerial system based on at least one of the image data and the position data (See Massarik [0020]); and generate an alert indicating detection of the unmanned aerial system (See Massarik [0020] “Decoded data, such as the video data, may be presented to a system operator via a control terminal 104a, 104b (referred to generically as a control terminal 104). Further, an alert may be generated based on the decoded data.).
It is noted that Massarik is silent about detecting an unmanned aerial system based on the image data using a trained machine learning model.
However, Hamminga teaches a method for generating one or more alerts (See Hamminga [0013]), and detecting an unmanned aerial system based on the image data using a trained machine learning model (See Hamminga [0009], [0071]).
Therefore, it is considered obvious that one skilled in the art, before the effective filing date of the claimed invention, would recognize the advantage of modifying Massarik to incorporate Hamminga’s teachings to detect an unmanned aerial system based on the image data using a trained machine learning model. The motivation for performing such a modification in Massarik is because the deep learning model is a trained deep residual learning framework for image classification tasks as taught by Hamminga (See Hamminga [0026]-[0027]).
As per claim 25, Massarik discloses a non-transitory computer-readable medium storing instructions (See Massarik memory 46 of Fig. 1B, and [0040]) for generating one or more alerts based on the detection of one or more unmanned aerial systems in image data wherein the instructions are executable by a system comprising one or more processors (See Massarik’s Abstrack, and [0057]) to cause the system to: receive image data and position data from at least one imaging device, wherein the image data includes an image of an unmanned aerial system (See Massarik [0081], [0083] “For example, the image recognition techniques may identify which side of a building is depicted in the video data and this may be used to determine the location associated with the drone.), determine a position of the unmanned aerial system based on at least one of the image data and the position data (See Massarik [0020]), generate an alert indicating detection of the unmanned aerial system (See Massarik (See Massarik [0020] “Decoded data, such as the video data, may be presented to a system operator via a control terminal 104a, 104b (referred to generically as a control terminal 104). Further, an alert may be generated based on the decoded data.).
It is noted that Massarik is silent about detecting an unmanned aerial system based on the image data using a trained machine learning model.
However, Hamminga teaches a method for generating one or more alerts (See Hamminga [0013]), and detecting an unmanned aerial system based on the image data using a trained machine learning model (See Hamminga [0009], [0071]).
Therefore, it is considered obvious that one skilled in the art, before the effective filing date of the claimed invention, would recognize the advantage of modifying Massarik to incorporate Hamminga’s teachings to detect an unmanned aerial system based on the image data using a trained machine learning model. The motivation for performing such a modification in Massarik is because the deep learning model is a trained deep residual learning framework for image classification tasks as taught by Hamminga (See Hamminga [0026]-[0027]).
5. Claims 14-15, 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Massarik et al. (US Patent Application Publication no. 2021/0033715) in view of Hamminga et al. (US Patent Application Publication no. 2022/0189326) as applied to claim 1, and further in view of Cheatham, III et al. (US Patent no. 9,318,014).
As per claim 14, most of the limitations of these claims have been noted in the above rejection of claim 1.
While Massarik discloses generating an alert (See Massarik [0020]), it does not disclose generating a report comprising at least one of a location of the imaging device, a distance between the imaging device and the unmanned aerial system, an identifier associated with the imaging device, one or more images of the unmanned aerial system, and one or more confidence scores associated with the detection of the unmanned arial system.
However, in similar field of invention, Cheatham teaches a drone detection system (See Cheatam col. 5, lines 7-21) that generates a report to a user (See Cheatam col. 5, lines 22-24 “report the visibility vulnerability using a mobile communication device). The report comprising a location of the imaging device (col.5 lines 43-45 “The output subsystem may report a position or location of a drone, an imaging system …”).
Hence, the location of the drone is the location of the “imaging device”. Since the claim recites “at least one of”, the reference needs only disclose one element; not all of the elements listed.
It would have been obvious for one of ordinary skill in the art at the time of filing to combine Cheatham teaching with Massarik because it would have enabled a user to take appropriate action to protect his/her privacy (Cheatham col.3 lines 20-29).
As per claim 15, the combination of Massarik, Hamminga and Cheatam further teaches receiving a first request from a user device; and in response to receiving the request causing the user device to display the report (Cheatham col.6 lines 7-10 “The output subsystem may report the visibility vulnerability in response to a query from the user”).
As per claim 21, most of the limitations of this claim have been noted in the above rejection of claim 18.
It is noted that the combination of Massarir and Hamminga does not disclose receiving a third request from the user device associated with the user selectable icon, and in response to receiving the third request, causing the user device to display any one or more of: an image of the one or more unmanned aerial systems, a location of the one or more unmanned aerial systems relative to the imaging device, and temporal information associated with when the one or more unmanned aerial systems were detected.
In similar field of invention, Cheatham discloses receiving a request from a user device with a user selectable icon (col. 10, lines 46-48 “input subsystem may include … touchscreen … for directly receiving requests from the user. (Note: “requests”-plural- implicitly includes a ‘third’ request, and a “touchscreen” user interface implicitly includes selectable icons)), and in response causing the user device to display temporal information associated with when the one or more unmanned aerial systems were detected (col.10 lines 37-40 “the warning may include a visual or audio message conveying more detailed information, such as the time or duration of the vulnerability.” "Time" or "duration" is a temporal information. Since the claim recites “any one or more”, the reference needs only disclose one element; not all of the elements listed).
It would have been obvious for one of ordinary skill in the art at the time of filing to combine Cheatham teaching with Massarik because it would have enabled a user to take appropriate action to protect his/her privacy (Cheatham col.3 lines 20-29).
Regarding claim 22, the combination of Massarik and Hamminga is silent about wherein the imaging device is a mobile device.
However, Cheatham teaches wherein the imaging device is a mobile device (See Cheatham col. 9, lines 23-29).
Therefore, it is considered obvious that one skilled in the art, before the effective filing date of the claimed invention, would recognize the advantage of modifying the combination of Massarik and Hamminga to incorporate Massarik’s teaching wherein the imaging device is a mobile device. The motivation for performing such a modification in the combination of Massarik and Hamminga is to provide a separate independent mobile device to send report to another mobile communication device (See Cheatham col. 9, lines 25-34).
6. Claims 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Massarik et al. (US Patent Application Publication no. 2021/0033715) in view of Hamminga et al. (US Patent Application Publication no. 2022/0189326) and Cheatham, III et al. (US Patent no. 9,318,014) as applied to claim 15 above, and further in view of Moro Jr. et al. (US Patent Application Publication no. 2021/0385609).
As per claim 16, most of the limitations of this claim have been noted in the above rejection of claim 15.
It is noted that the combination of Massarik, Hamminga and Cheatham is silent about receiving information from the user device associated with a user selection of an unmanned aerial system type from a plurality of unmanned aerial system types; and in response to receiving the additional information from the user device, updating the report with the unmanned aerial system type.
In similar field of invention, Moro discloses a crowd-sourced unmanned aerial system (UAS) detection system using mobile devices as imaging devices (See Moro [0017]). Moro [0043] teaches identifying UAS make, model and payload(s) using information received from the mobile devices. Moro teaches the information includes human observations that are input via a GUI (See Moro [0043] line 6).
Therefore, one of ordinary skill in the art, at the time of filing, would have been motivated to combine the teaching of Moro with the combination of Massarik, Hamminga and Cheatham because it would have enabled wide-scale UAS detection in urban area that has limited lines of sight and high clutter environments without requiring the use of costly high-performance imaging systems and staffing (Moro [0016]).
Moro does not explicitly disclose the user selects a UAS type from a plurality of UAS types. However, providing a list of UAS types for the user to select from would have been an obvious variation of Moro teaching of “human observations” input. Using a selection list GUI to collect observation data is well known in the art. See for example Michela page 3 line 4-12. It would have been obvious for one of ordinary skill in the art to provide a UAS types selection list because it would have enhanced efficiency and accuracy in obtaining the user’s observations of the UAS. It is obvious that the report would be updated so that a user interested in the report can receive the latest information.
As per claim 17, most of the limitations of this claim have been noted in the above rejection of claim 16. In addition, the combination of Massarik, Hamminga, Cheatham and Moro further teaches wherein the at least one imaging device comprises the user device (See Moro [0017] “mobile device of multiple users”).
7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to GIMS S PHILIPPE whose telephone number is (571)272-7336. The examiner can normally be reached Maxi Flex.
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/GIMS S PHILIPPE/Primary Examiner, Art Unit 2424
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