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 .
Status of Claims
This office action is in response to the patent application filed on August 11, 2025. Claims 18-34 are currently pending.
Priority
Applicant’s claim for the benefit of a prior-filed application, 17/907,892 under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Claim Rejections – 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 18-24, 26-28, & 30-34 are rejected under 35 U.S.C. 103 as being unpatentable over US 2020/0043348 A1, to Ghosh et al., hereafter Ghosh in view of US 10,466,700 B1, to Carmack et al., hereafter Carmack.
Regarding Claim 18, as shown above, Ghosh discloses A computer implemented method in a communications network for determining location information about an actual location of a drone, the method comprising (Ghosh [0101], Examiner Note: Ghosh discloses a technique to determine a prediction actual position of a UAV based on a received signal from the UAV):
obtaining a reported location of the drone at a first time point (Ghosh [0096] & Fig. 5, Examiner Note: Ghosh discloses a UAV (402) position at time t0);
obtaining a measurement of radio conditions between the drone and a node in the communications network, at the first time point (Ghosh [0096] & Fig. 5, Examiner Note: Ghosh discloses the UAV sending a signal to a base station (i.e. node) (504));
predicting radio conditions at one or more locations related to the reported location of the drone (Ghosh [0099] & [0103] & Fig. 7, Examiner Note: Ghosh discloses receiving a signal which represents a position and further predicting signal characteristics and comparing them to the determined signal characteristics which then verifies the position. [0099] verifies what the signal characteristics map be “(e.g. any of cell IDs, RSRP (reference Signal Receive Power), PRS (Positioning Reference Signal) correlation values, etc.”); and
determining the location information about the actual location of the drone based on the measured radio conditions and the predicted radio conditions (Ghosh [0103] & Fig. 7, Examiner Note: Ghosh discloses Steps 708 & 710 which respectively compare predicted signal characteristics to a determined signal characteristic, and verifies the position based on the comparison),…
…when the method is performed by a serving node of the drone, the method further comprises signalling out-of-path detection result from the serving node to a neighbouring node during handover, for cross-checking between the serving node and the neighbouring node (Ghosh [0101]-[0103], Examiner Note: Ghosh discloses using neighboring base stations (i.e. neighboring nodes) which can determine based on the characteristics received from the UAV’s signal (i.e. handover), path-loss (i.e. out-of-path detection), which is compared to values measured by the base station 504).
However, Ghosh does not specifically disclose wherein if the location information indicates that the drone has deviated from the reported location of the drone, the method further comprises sending a message to the drone, the message comprising a request that the drone returns to ground-level.
Carmack, directed to the same problem, discloses wherein if the location information indicates that the drone has deviated from the reported location of the drone, the method further comprises sending a message to the drone, the message comprising a request that the drone returns to ground-level (Carmack Col. 40 Rows 62-67 & Fig. 14, Examiner Note: Carmack teaches when determining if the spoofing of GPS being used from a nearby source (i.e. reported location of the drone has deviated) has not been resolved (e.g. 1408 of Fig. 14, No), the drone may enter a safe state (e.g. 1416 of Fig. 14) where one of the actions taken while in the safe state is landing).
Therefore, it would have been obvious for one of ordinary skill in the art, before the filing date of the claimed invention and with a reasonable likelihood of success, to modify the method of positioning autonomous agents using signal characteristics of Ghosh with the safe state action of Carmack in order to prevent the drone from getting lost.
Regarding Claim 19, Ghosh in view of Carmack discloses The method as in claim 18, wherein the location information about the actual location of the drone is determined based on a comparison between the measured radio conditions and the predicted radio conditions (Ghosh [0101], Examiner Note: Ghosh discloses comparing signals on one or more frequencies (i.e. radio conditions) to determine the reported location of the UAV which is compared to measured values and is determined to have deviated if it goes beyond a reasonable tolerance).
With respect to Claim 20, all the limitations have been analyzed in view of claim 19, and it has been determined that claim 20 does not teach or define any new limitations beyond those previously recited in Claim 19. Therefore, claim 20 is also rejected over the same rationale as claim 19.
With respect to Claim 21, all the limitations have been analyzed in view of claim 19, and it has been determined that claim 21 does not teach or define any new limitations beyond those previously recited in Claim 19. Therefore, claim 21 is also rejected over the same rationale as claim 19.
Regarding Claim 22, Ghosh in view of Carmack teaches The method as in claim 18, wherein obtaining the measurement of radio conditions comprises:
obtaining a sequence of measurements of radio conditions between a node in the communications network and the drone, the sequence of measurements being made at a sequence of locations along a flight path as reported by the drone (Ghosh [0110]-[0112], Examiner Note: Ghosh discloses continuingly measuring a UAV’s location through multimodal position verification. The UAV repeatedly reports its GPS position to an operating center according to a schedule which is verified by wireless channel signal properties); and
wherein the step of determining the location information comprises: determining an actual flight path of the drone by pattern matching the obtained sequence of measurements of radio conditions to patterns in the predicted radio conditions at the one or more locations related to the reported location of the drone (Ghosh [0110]-[0112], Examiner Note: Ghosh discloses verifying the continuous gps position data with the received signal properties).
Regarding Claim 23, Ghosh in view of Carmack teaches The method as in claim 22 wherein the predicted radio conditions comprise a map of radio conditions that covers an area that includes the flight path reported by the drone (Ghosh [0094], Examiner Note: Ghosh discloses mapping missions where UAVs collect signal characteristic fingerprints of locations).
Regarding Claim 24, Ghosh in view of Carmack teaches The method as in claim 18, wherein the measured radio conditions comprise a plurality of measurements of radio conditions between the drone and each one of a plurality of different nodes in the communications network (Ghosh [0094], Examiner Note: Ghosh discloses using Reference Signal Received Power to receive signal conditions from several different base stations).
Regarding Claim 26, Ghosh in view of Carmack teaches The method as in claim 18, wherein the step of predicting radio conditions at one or more locations related to the reported location of the drone comprises: predicting the radio conditions using a channel model and deployment information (Ghosh [0095] and Equations 1 & 2, Examiner Note: Ghosh discloses using variables such as reported distance, signal loss, and path loss in conjunction to Equations 1 & 2 to determine the predicted location of a UAV).
Regarding Claim 27, Ghosh in view of Carmack teaches The method as in claim 18, wherein the step of predicting radio conditions at one or more locations related to the reported location of the drone comprises:
However, the modification does not specifically teach using a model trained using a machine learning process to predict the radio conditions at the one or more locations.
Carmack teaches using a model trained using a machine learning process to predict the radio conditions at the one or more locations (Carmack, Column 36 Rows 60-67, Examiner Note: Carmack teaches using a machine learning algorithm to detect expected signal strength (i.e. radio conditions) by using historical location data with corresponding signal strengths and current condition data such as the current location of a UAV).
Therefore, it would have been obvious for one of ordinary skill in the art, before the filing date of the claimed invention and with a reasonable likelihood of success, to modify the method of positioning autonomous agents using signal characteristics of Ghosh in view of Carmack with the machine learning algorithm of Carmack in order to better track unmanned flying vehicles between destinations (Carmack Col. 1 Rows 29-39).
Regarding Claim 28, Ghosh in view of Carmack teaches The method as in claim 27, wherein the model has been trained using training data, wherein each piece of training data comprises:
However, the modification does not specifically teach i) an example drone location; and ii) ground truth measurements of radio conditions at the example drone location.
Carmack further teaches i) an example drone location (Carmack, Column 36 Rows 60-67, Examiner Note: Carmack teaches historical UAV GPS data); and ii) ground truth measurements of radio conditions at the example drone location (Carmack, Column 36 Rows 60-67, Examiner Note: Carmack teaches signal strengths at the corresponding historical GPS locations).
Therefore, it would have been obvious for one of ordinary skill in the art, before the filing date of the claimed invention and with a reasonable likelihood of success, to modify the method of positioning autonomous agents using signal characteristics of Ghosh in view of Carmack with the machine learning algorithm of Carmack in order to better track unmanned flying vehicles between destinations (Carmack Col. 1 Rows 29-39).
Regarding Claim 30, Ghosh in view of Carmack teaches The method as in claim 18, wherein if the location information indicates that the drone has deviated from the reported location of the drone, the method further comprises: sending a second message to the drone, the second message comprising one of i) a warning to the drone that it has deviated from its reported location; and ii) an indication that the drone will be disconnected from the communications network if it fails to alter its flight trajectory; and/or disconnecting the drone from the communications network (Ghosh [0091], Examiner Note: Ghosh discloses if the UAV has unverified or an disputed UAV position, the UAV may be disabled).
Regarding Claim 31, Ghosh in view of Carmack teaches The method as in claim 18 wherein the method is performed by a base station, network node or network function node in the communications network (Ghosh [0120], Examiner Note: “The location verification device may be configured as one or more unmanned aerial vehicles, as one or more autonomous agents, and/or one or more base stations”).
Regarding Claim 32, Ghosh discloses The method as in claim 18, wherein the method is performed in a distributed manner, or in a cloud (Ghosh [0120], Examiner Note: Ghosh discloses being able to perform the method on one or more unmanned aerial vehicles (i.e. distributed)).
Regarding Claim 33, all the limitations have been analyzed in view of claim 18, and it has been determined that claim 18 does not teach or define any new limitations beyond those previously recited in Claim 33 aside where noted below; Therefore, claim 33 is also rejected over the same rationale as claim 18.
A node in a communications network for determining location information about an actual location of a drone, wherein the node comprises a memory comprising instruction data representing a set of instructions (Ghosh [0352], Examiner Note: Ghosh discloses an autonomous agent localization system contains memory); and
a processor configured to communicate with the memory and to execute the set of instructions (Ghosh [0352], Examiner Note: Ghosh discloses an autonomous agent localization system contains a processor which communicates to the memory).
Regarding Claim 34, Ghosh in view of Carmack teaches A non-transitory computer readable medium, the computer readable medium having computer readable code embodied therein, the computer readable code being configured such that, on execution by a computer or processor, the computer or processor is caused to perform the method as claimed in claim 18 (Ghosh [0380], Examiner Note: Ghosh discloses a non-transient computer readable medium which works with a processor).
Claim 25 is rejected under 35 U.S.C. 103 as being unpatentable over US 2020/0043348 A1, to Ghosh et al., hereafter Ghosh in view of US 10,466,700 B1, to Carmack et al., hereafter Carmack as applied to claim 18 above, and further in view of US 2008/0183344 A1, to Doyen et al., hereafter Doyen.
Regarding Claim 25, as shown above, Ghosh in view of Carmack teaches The method as in claim 18,
However, the modification does not specifically disclose further comprising: determining that the actual position of the drone is in a no-fly zone if the measured radio conditions are inconsistent with the predicted radio conditions.
Doyen, directed to the same problem, teaches further comprising: determining that the actual position of the drone is in a no-fly zone if the measured radio conditions are inconsistent with the predicted radio conditions (Goyen teaches a restricted-area alert methodology which includes the step “(3) conflict analysis between an actual, or predicted, vehicle position in reference to a boundary of a restricted area”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the filing date of the claimed invention and with a reasonable likelihood of success, to modify the method of positioning autonomous agents using signal characteristics of Ghosh in view of Carmack with the restricted area detection step of Goyen in order to create a wider range of possible conclusions when the predicted results and actual results do not align.
Claim 29 is rejected under 35 U.S.C. 103 as being unpatentable over US 2020/0043348 A1, to Ghosh et al., hereafter Ghosh in view of US 10,466,700 B1, to Carmack et al., hereafter Carmack as applied to claims 18 & 27 above, and further in view of US 2022/0292867 A1, to Zhang et al.
Regarding Claim 29, Ghosh in view of Carmack teaches The method as in claim 27,
However, the modification does not specifically teach wherein the model comprises a neural network or a random forest model.
Zhang, directed to the same problem, teaches wherein the model comprises a neural network or a random forest model (Zhang [0020], Examiner Note: Zhang teaches using a neural network when using a trajectory model which analyzes images to predict an object or vehicle’s trajectory and therefore, its final location).
Therefore, it would have been obvious for one of ordinary skill in the art, before the filing date of the claimed invention and with a reasonable likelihood of success, to modify the method of positioning autonomous agents using signal characteristics of Ghosh in view of Carmack, with the neural network of Zhang in order to accurately predict movement, and therefore prevent collisions, of objects or vehicles (Zhang [0001]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL T DOWLING whose telephone number is (703)756-1459. The examiner can normally be reached M-T: 8-5:30, First F: Off, Second F: 8-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Erin Piateski can be reached at (571) 270-7429. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL T DOWLING/Examiner, Art Unit 3669
/Erin M Piateski/Supervisory Patent Examiner, Art Unit 3669
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