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 . 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.
DETAILED ACTION
The following NON-FINAL Office Action is in response to Application 18/854,601 - filed on 10/7/2024.
Priority
The Examiner has noted the Applicant claiming Priority from Provisional Application 63/327,446 filed 4/5/2022.
Status of Claims
Claims 1-20 are currently pending of which:
Claims 1-20 are currently under examination and have been rejected as follows.
IDS
The information disclosure statement filed on 10/7/2024 complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 and is considered by the Examiner.
Claim Objections
Claims 7, 20 are objected to for the following informalities.
Claim 7 recites: “The method claim 1, wherein…” [bolded emphasis added].
Claim 7 is recommended to recite: “The method of claim 1, wherein….”
Claim 20 recites: “deploying the microservices or FaaS wih the placement as determined” [bolded emphasis added].
Claim 20 is recommended to recite: “deploying the microservices or FaaS with the placement as determined”.
Appropriate corrections are required.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-10 are directed to a method or process which is a statutory category.
Claims 11-20 are directed to a system or machine which is a statutory category.
Step 2A Prong One: The claims recite, describe, or set forth a judicial exception of an abstract idea (see MPEP 2106.04(a)). Specifically, the claims recite, describe or set forth mathematical relationships and calculations including: “computing a penalty factor score (SPF)”; “computing a compactness factor score (SCF) based on how close or far the possible outcomes are from an average score”; “maximize coverage”; “minimize overlap”; and “minimize a targeted deployment cost”. Additionally, the claims recite, describe or set forth concepts performed in the human mind (including observation, evaluation, and judgement) including: “defining possible outcomes for a plurality of criteria having predefined constraints and associated priority weights”; “determining a ranking of the possible outcomes using SPF and SCF”; “selecting the preliminary number of UAVs, microservices or FaaS based on the ranking”; “determining positions of the preliminary number of UAVs”; “determining orientations of the preliminary number of UAVs”; and “determining a placement of the preliminary number of microservices or FaaS”. Computing scores and averages, maximizing coverage quantity, and minimizing costs fall within mathematical relationships and calculations under the larger abstract grouping of Mathematical Concepts (MPEP 2106.04(a)(2) I). Defining outcomes based on criteria and constraints, ranking outcomes, selecting based on rankings, and determining positions, orientations, orientations, and placements fall within concepts performed in the human mind (including observation, evaluation, and judgement) under the larger abstract grouping of Mental Processes (MPEP 2106.04(a)(2) II). Accordingly, the claims recite a an abstract idea.
Step 2A Prong Two: Independent claims 1, 11 recite the following additional elements: “UAVs”, “microservices”, “FaaS”, “distributed communications environment”, “processor”, “non-transitory computer readable medium”, and “machine learning”. The functions of these additional elements include examples such as “defining possible outcomes for a plurality of criteria having predefined constraints and associated priority weights”, “computing a penalty factor score (SPF)”, “computing a compactness factor score (SCF)”, “determining a ranking of the possible outcomes”, “determining positions of the preliminary number of UAVs within the area of interest to maximize coverage”, “determining orientations of the preliminary number of UAVs within the area of interest”, and “determining a placement of the preliminary number of microservices or FaaS over the distributed communication environment to minimize a targeted deployment cost”. The additional elements are recited at a high level of generality (i.e. as a generic computer performing functions of calculating scores, ranking and organizing data, etc.) such that they amount to no more than mere instructions to apply the exception using generic computer components. Therefore, these functions can be viewed as not meaningfully different than a business method or mathematical algorithm being applied on a general-purpose computer as tested per MPEP 2106.05(f)(2)(i). The claims are directed to an abstract idea and the judicial exception does not integrate the abstract idea into a practical application.
The additional element “machine learning” language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “machine learning” alone is insufficient to show a practical application of the recited abstract idea.
Step 2B: According to MPEP 2106.05(f)(1), considering whether the claim recites only the idea of a solution or outcome i.e., the claims fail to recite the technological details of how the actual technological solution to the actual technological problem is accomplished. The recitation of claim limitations that attempt to cover an entrepreneurial and thus abstract solution to an entrepreneurial problem with no technological details on how the technological result is accomplished and no description of the mechanism for accomplishing the result do not provide significantly more than the judicial exception.
Dependent claims 2-10, 12-20 do not appear to provide any additional computer-based elements, let alone for such additional computer-based elements to integrate the abstract idea into practical application (Step 2A prong two) or providing significantly more (Step 2B).
Further, dependent claims 2-10, 12-20 merely incorporate the additional elements recited in claims 1, 11 along with further narrowing of the abstract idea of claims 1, 11 and their execution of the abstract idea. Specifically, the dependent claims narrow the “UAVs”, “microservices”, “FaaS”, “distributed communications environment”, “processor”, “non-transitory computer readable medium”, and “machine learning” to capabilities such as reassessing, synchronizing, selecting, computing, measuring, ranking, modeling, defining, converting, and differentiating various forms of data such as numbers of UAVs, numbers of microservices/FaaS, rotations, positions, orientations, factor scores, distances, constraints, rankings, shaped areas of interest, sub-areas, linguistic quantifiers, logic operators, textual terms, parameters, etc. which, when evaluated per MPEP 2106.05(f)(2) represent mere invocation of computers to perform existing processes. Therefore, the additional elements recited in the claimed invention individually and in combination fail to integrate a judicial exception into a practical application (Step 2A prong two) and for the same reasons they also fail to provide significantly more (Step 2B). Thus, claims 1-20 are reasoned to be patent ineligible.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
REJECTIONS BASED ON PRIOR ART
Examiner Note: Some rejections will contain bracketed comments preceded by an “EN” that will denote an examiner note. This will be placed to further explain a rejection.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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 of this title, 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.
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.
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.
Claims 1-6, 9-16, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over:
Shattil US 20170235316 A1, hereinafter Shattil, in view of
E. Tuba et al. (2018). Drone Placement for Optimal Coverage by Brain Storm Optimization Algorithm, hereinafter Tuba. As per,
Regarding claims 1, 11: Shattil teaches:
A method (claim 1) / system (claim 11) for selecting and deploying one of a set of unmanned aerial vehicles (UAVs) over a geographical area of interest (Shattil ¶ [0005]: … swarm control may be used to control the UAVs as they fly over a targeted geographical area. ¶ [0082]: The flight control problem in step 412 for UAV deployment can involve finding the optimal UAV separations, hovering altitude, and geographical location to enhance coverage in addition to RAN performance. ¶ [0100]: … when the number of UEs in a particular geographical area exceeds the number of server-side antennas configured to serve the UEs … the UAVs may call for additional UAVs to support cooperative MIMO processing in the RAN) and
a set of microservices or Functions-as-a-Service (FaaS) over a distributed communication environment (Shattil ¶ [0002]: The present invention relates, in general, to control of unmanned aerial vehicles (UAVs), and, more particularly, to control methods and systems for use with swarms of UAVs configured to function as relays [EN: microservices] in a radio access network [EN: distributed communication environment]). ¶ [0036]: … the active scattering platforms… may organize themselves into groups (or clusters)…. each group can have a cluster head…. Each cluster head… can provide services to other members of its respective group, including (but not limited to) synchronizing the other members, communicating with BTSs on behalf of the group, organizing group operations in accordance with command and control signals received from ground units, controlling communications within the group, controlling communications between groups, performing central processing operations for the group, coordinating the group to communicate via the RAN with the UEs and/or BTSs, determining heading for the group, organizing the group's flight formation, performing computational and/or power load balancing among group members, and/or transferring control responsibilities to another group member), the method (claim 1) / system (claim 11) comprising:
a processor; and a non-transitory computer readable medium having stored thereon program instructions executable by the processor for (claim 11 only) (See Shattil ¶s [0014-0015]):
selecting a preliminary number of UAVs, or
defining possible outcomes for a plurality of criteria having predefined constraints and associated priority weights (Shattil ¶ [0087]: In some aspects of the disclosure, optimization comprises determining UAV locations (including flight patterns) [EN: outcomes] that reduce cost functions and/or increase benefits, but within constraints governed by avionic systems (e.g., max/ min speed, altitude, maneuverability, etc.), air traffic control (e.g., situational awareness, minimum required aircraft spacing, optimal spacing, max/min altitude, predetermined flight paths, intended destination, etc.), and optionally, environmental factors (e.g., weather, terrain, buildings, etc.). End-¶ [0069]: UAVs may be provided with different levels of priority, and those with higher priority may move UAVs with lower priority out of the way. Priority levels may be determined by a cluster's mission, a UAVs function in the cluster, each UAV's RAN performance measurement, maneuverability, processing capability, battery life, and/or other criteria. End-¶ [0122]: Finally, the problem is broken up such that the transmitter array and/or the receiver array can calculate a single coverage parameter that leads the individual nodes to find their own optimum weights, distributing the calculation over the network);
computing a penalty factor score (SPF) based on whether or not each one of the possible outcomes violates the predefined constraints (Shattil ¶ [0135]: … Various algorithms may be employed in different aspects, including (but not limited to): an augmented Lagrangian (AL), which is a regularization technique that is obtained by adding a quadratic penalty term to the ordinary Lagrangian. [Examiner notes that the Augmented Lagrangian is an algorithm for solving constrained optimization problems using a penalty factor, thus consistent with the problem in the instant application]);
computing a compactness factor score (SCF) based on how close or far the possible outcomes are from an average score of all values falling under a same type of criteria (Shattil ¶ [0084]: Besides the antenna separation, the azimuth angle and pitching angle also influence the average channel capacity. For example, a smaller pitching angle can provide larger average channel capacity. Also, the UAV antenna might be oriented horizontally to provide a larger channel capacity. ¶ [0124]: A perturbation [EN: variance from average or normal, thus consistent with compactness factor score described in the claim limitation] to course, cluster flight formation, and/or altitude can be used to adapt the cluster to seek improved array performance. For example, a constraint is chosen, such as SNR, eigenvalues corresponding to MIMO eigenvectors, or another measurable parameter. Based on the constraint, the UAVs can measure the constraint directly from signals received from the set of UEs, and/or the set of UEs can measure the constraint and return feedback information to the UAVs. Then the perturbation to the cluster's heading and/or other flight characteristics is performed [EN: score computed]…. In one aspect, effects characterized by variances having a zero mean can be averaged out with a sufficient number of samples);
determining a ranking [..] using SPF and SCF (Shattil mid-¶ [0103]: a swarm might comprise multiple clusters, and UAVs within a cluster might need to be spatially separated by a sufficient distance such as to achieve sufficient channel rank or reduce the condition number 512, synthesize a sufficiently large aperture (not shown), or any other number of reasons); and
selecting the preliminary number of UAVs, microservices or FaaS based on the ranking (Shattil ¶ [0118]: Cluster Manager 651 via the Scheduler 652 schedules UAVs to operate in a cluster. Scheduler 652 can assign [EN: select] UAVs to a cluster. By way of example, scheduler 652 operating in cooperation with the Processor 653 and/or Manager 655 can assign duties to individual UAVs, including signal processing, data storage, and/or routing functions. End-¶ [0073]: Scheduler 254 can determine which UAVs process RAN signals, such as calculating MIMO weights. Scheduler 254 may assign different signal processing tasks [EN: microservices/FaaS] to the UAVs based on any of various criteria, including battery life, processing capability, storage capacity, among others.); and
one of:
determining positions of the preliminary number of UAVs within the area of interest to maximize coverage of the area of interest and minimize overlap between the preliminary number of UAVs, and determining orientations of the preliminary number of UAVs within the area of interest for the positions as determined (Shattil ¶ [0082]: The flight control problem in step 412 for UAV deployment can involve finding the optimal UAV separations [EN: orientation], hovering altitude [EN: orientation], and geographical location [EN: position] to enhance coverage in addition to RAN performance. End-¶ [0066]: Therefore, in aspects of the disclosure, the positioning of UAVs in a Cooperative-MIMO UAV cluster can be adapted by each flight controller 202 to enhance advantageous characteristics of the UAV-MIMO channel to improve RAN performance. ¶ [0135]: Some aspects can employ a distributed optimization algorithm which allows for autonomous computation of the beamforming decisions by each cluster while taking into account intra- and inter-cluster interference [EN: overlap] effects); and
Although Shattil teaches computing penalty values, compactness factors, and rankings for UAV swarm deployment optimization, Shattil does not specifically teach ranking the outcomes, i.e. the specific deployment arrangement solutions generated by the optimization.
However, Tuba in analogous art of UAV swarm optimization teaches or suggests:
[determining a ranking] of the possible outcomes [..] (Tuba p. 169: Our proposed model defines coverage quality γ [EN: optimization score] of the target ti by the drone u by the following equation… where d(ti,u) represents Euclidean distance between target ti and drone u and max(R) is maximal possible covering radius which can be calculated by drone’s height and visibility angle. P. 170: Algorithm 1. Pseudo-code of the BSO algorithm, step 5: Rank solutions in each cluster and set the best one as cluster center).
Examiner notes that in addition to the teachings of Shattil, Tuba teaches computing optimization scores based on coverage quality constrained by a drone’s limitations (see Tuba eqn. 1 and related text), analogous to applying a penalty, and based on variance from an average (see Tuba eqn. 3 and related text) analogous to a compactness factor
Tuba and Shattil are found as analogous art of UAV swarm optimization. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Shattil’s airborne relay system and method to have included Tuba’s teachings around ranking UAV swarm optimization solutions. The benefit of these additional features would have maximized drone coverage while minimizing drone altitude and thus minimizing fuel costs (Tuba Abstract). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Shattil in view of Tuba (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of UAV swarm optimization. In such combination each element would have merely performed the same function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Byun in view of Tuba above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
Regarding claims 2, 12: Shattil / Tuba teaches all the limitations of claims 1, 11 above.
Shattil further teaches:
reassessing the preliminary number of UAVs for deployment as a function of the positions and orientations as determined or reassessing the preliminary number of microservices or FaaS for deployment as a function of the placement as determined (Shattil mid-¶ [0100]: By way of example, when the number of UEs in a particular geographical area exceeds the number of server-side antennas configured to serve the UEs (e.g., the total of available base transceiver antennas and UAV antennas provisioned to serve a set of UEs), the UEs, the BTSs, the CP, and/or the UAVs may call for additional UAVs to support cooperative MIMO processing in the RAN).
Regarding claims 3, 13: Shattil / Tuba teaches all the limitations of claims 1, 11 above.
Shattil further teaches:
synchronizing a rotation of the UAVs based on the positions and orientations of the preliminary number of UAVs (Shattil ¶ [0068]: A UAV functioning as a UAV cluster head can comprise the fleet manager 202, which can provide cluster management, including (but not limited to) one or more of assigning UAVs to operate in a cluster, assigning operations to the UAVs, scheduling resources to be used by the UAVs, synchronizing the UAVs in a cluster, performing synchronization with other clusters, providing navigation control messages to the UAVs, providing for fronthaul network management of the cluster's network, coordinating UAVs to perform mitigations, and coordinating the UAVs to perform cooperative MIMO processing of the RAN signals…. ¶ [0007]: In the context of flight management, synchronization is agreement in time or simultaneous operation, which is an important concept in the control of dynamical systems. An important technique to improve synchronization performance is cross coupling, which is based on sharing the feedback information of control loops, and it has many applications in the motion synchronization of multi-axis and cooperative manipulator robots. ¶ [0080]: Control systems for a vehicle's orientation (attitude) include actuators, which exert forces in various directions and generate rotational forces or moments about the aerodynamic center of the aircraft, and thus rotate the aircraft in pitch, roll, or yaw).
Regarding claims 4, 14: Shattil / Tuba teaches all the limitations of claims 1, 11 above.
Although Shattil teaches computing penalty values, compactness factors, and rankings for UAV swarm deployment optimization, Shattil does not specifically teach computing a distance factor score or ranking the outcomes, i.e. the specific deployment arrangement solutions generated by the optimization.
However, Tuba in analogous art of UAV swarm optimization teaches or suggests:
wherein selecting the preliminary number of UAVs, microservices or FaaS further comprises computing a distance factor score (SDF) by measuring a distance between the possible outcomes for the plurality of criteria and the corresponding constraints for the criteria; and wherein ranking the possible outcomes comprises ranking using SPF, SCF and SDF (Tuba p. 169: Our proposed model defines coverage quality γ of the target ti by the drone u by the following equation… where d(ti,u) represents Euclidean distance between target ti and drone u and max(R) is maximal possible covering radius which can be calculated by drone’s height and visibility angle. P. 170: Algorithm 1. Pseudo-code of the BSO algorithm, step 5: Rank solutions in each cluster and set the best one as cluster center).
Tuba and Shattil are found as analogous art of UAV swarm optimization. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Shattil’s airborne relay system and method to have included Tuba’s teachings around computing a distance factor score and ranking UAV swarm optimization solutions. The benefit of these additional features would have maximized drone coverage while minimizing drone altitude and thus minimizing fuel costs (Tuba Abstract). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Shattil in view of Tuba (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of UAV swarm optimization. In such combination each element would have merely performed the same function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Byun in view of Tuba above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
Regarding claims 5, 15: Shattil / Tuba teaches all the limitations of claims 1, 11 above.
Although Shattil teaches UAV swarm deployment optimization for particular geographic area of interest, Shattil does not specifically teach modeling the optimization with specifically shaped areas.
However, Tuba in analogous art of UAV swarm optimization teaches or suggests:
wherein the geographical area of interest is modeled as one of circular, rectangular, triangular, and square shaped areas (See Tuba p. 173-174, Figs. 1-2 depicting square areas of interest and circular clusters or sub-areas).
Tuba and Shattil are found as analogous art of UAV swarm optimization. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Shattil’s airborne relay system and method to have included Tuba’s teachings around modeling the optimization with specifically shaped areas. The benefit of these additional features would have maximized drone coverage while minimizing drone altitude and thus minimizing fuel costs (Tuba Abstract). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Shattil in view of Tuba (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of UAV swarm optimization. In such combination each element would have merely performed the same function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Byun in view of Tuba above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
Regarding claims 6, 16: Shattil / Tuba teaches all the limitations of claims 1, 11 above.
Although Shattil teaches UAV swarm deployment optimization for particular geographic area of interest, Shattil does not specifically teach dividing the areas of interest into sub-areas. However, Tuba in analogous art of UAV swarm optimization teaches or suggests:
wherein the geographical area of interest is divided into sub-areas (See Tuba p. 173-174, Figs. 1-2 depicting square areas of interest and circular clusters or sub-areas).
Tuba and Shattil are found as analogous art of UAV swarm optimization. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Shattil’s airborne relay system and method to have included Tuba’s teachings around dividing the areas of interest into sub-areas. The benefit of these additional features would have maximized drone coverage while minimizing drone altitude and thus minimizing fuel costs (Tuba Abstract). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Shattil in view of Tuba (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of UAV swarm optimization. In such combination each element would have merely performed the same function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Byun in view of Tuba above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
Regarding claims 9, 19: Shattil / Tuba teaches all the limitations of claims 1, 11 above.
Shattil further teaches:
wherein a parameter is used to differentiate between beneficial criteria and cost criteria, where the parameter is 1 for the beneficial criteria and 0 for the cost criteria, and wherein the possible outcomes that violate the predefined constraints are retained for the ranking (Shattil mid-¶ [0087]: In some aspects of the disclosure, UAV flight paths are determined not only by interactions with each other (such as to provide course adjustments, aircraft spacing, etc.), but also by measured and/or calculated parameters in a multi-dimensional search space corresponding to radio communications with a set of UEs. In a population-based search algorithm, optimization can comprise minimizing a cost function or maximizing a benefit. In some aspects of the disclosure, optimization comprises determining UAV locations (including flight patterns) that reduce cost functions and/or increase benefits, but within constraints governed by avionic systems (e.g., max/min speed, altitude, maneuverability, etc.), air traffic control (e.g., situational awareness, minimum required aircraft spacing, optimal spacing, max/min altitude, predetermined flight paths, intended destination, etc.), and optionally, environmental factors (e.g., weather, terrain, buildings, etc.)).
Examiner notes that the information of different data for the parameters (0 and 1) claim 9 is construed as nonfunctional descriptive material and is given no patentable weight because these items have no functional relationship with a computer in the claim. This is the same as the example in MPEP 2111.05 of a table containing batting averages, where the information only has significance to a user reading the information. Nonetheless, for compact prosecution, art will be applied to the “names” of the data.
Regarding claims 10, 20: Shattil / Tuba teaches all the limitations of claims 1, 11 above.
Shattil further teaches:
deploying the UAVs with the positions and orientations as determined or deploying the microservices or FaaS with the placement as determined (Shattil ¶ [0082]: The flight control problem in step 412 for UAV deployment can involve finding the optimal UAV separations [EN: orientation], hovering altitude [EN: orientation], and geographical location [EN: position] to enhance coverage in addition to RAN performance).
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Claims 7-8, 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over:
Shattil / Tuba as applied above, and in further view of
Jeong et al. US 20240273416 A1, hereinafter Jeong. As per,
Regarding claims 7, 17: Shattil / Tuba teaches all the limitations of claims 1, 11 above.
Although Shattil teaches UAV swarm deployment optimization using constraints, Shattil does not specifically teach defining the constraints with linguistic quantifiers and logic operators.
However, Jeong in analogous art to the instant application of optimization modeling teaches or suggests:
wherein the predefined constraints are defined using linguistic quantifiers and logic operators (Jeong ¶ [0031]: The intent may be obtained by the node in the form of a natural language statement, or may be obtained as a logical specification using logical symbols. Where the intent is obtained as a natural language statement, it may be converted into a logical specification, for example, using an intent converter. An example of a natural language statement of an intent, in the context of a telecommunications network, is ‘SINR, network coverage and received signal quality are never degraded together’. A logical specification corresponding to the above natural language statement, using linear temporal logic symbols, would be □(¬(SINRLow∧covLow∧quaLow)), where □ is a logical “always” operator, ¬ is a logical “not” operator, ∧ is a logical “and” operator, SINRLow indicates a low average [EN: quantifier] SINR, covLow indicates a low network coverage [EN: quantifier] and quaLow indicates a low average received signal quality [EN: quantifier]. In this example, the environment is all or part of a telecommunications network; the state of the environment would be encoded by a ML agent using a set of features representing the state of the network, such as the average SINR, network coverage, average received signal quality, total network capacity, and so on).
Jeong, Tuba and Shattil are found as analogous art of optimization modeling. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Shattil / Tuba’s airborne relay system and method to have included Jeong’s teachings around modeling with linguistic quantifiers and logic operators. The benefit of these additional features would have enabled efficient optimization computation with more complex requirements (Jeong ¶ [0006]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Shattil in view of Tuba and Jeong (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of optimization modeling. In such combination each element would have merely performed the same function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Byun in view of Tuba and Jeong above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
Regarding claims 8, 18: Shattil / Tuba / Jeong teaches all the limitations of claims 7, 17 above.
Although Shattil teaches UAV swarm deployment optimization using constraints, Shattil does not specifically teach using ML to convert textual terms into linguistic quantifiers.
However, Jeong in analogous art to the instant application of optimization modeling teaches or suggests:
wherein textual terms are converted into the linguistic quantifiers using machine learning (Jeong ¶ [0031]: The intent may be obtained by the node in the form of a natural language statement, or may be obtained as a logical specification using logical symbols. Where the intent is obtained as a natural language statement, it may be converted into a logical specification, for example, using an intent converter. An example of a natural language statement of an intent, in the context of a telecommunications network, is ‘SINR, network coverage and received signal quality are never degraded together’. A logical specification corresponding to the above natural language statement, using linear temporal logic symbols, would be □(¬(SINRLow∧covLow∧quaLow)), where □ is a logical “always” operator, ¬ is a logical “not” operator, ∧ is a logical “and” operator, SINRLow indicates a low average [EN: quantifier] SINR, covLow indicates a low network coverage [EN: quantifier] and quaLow indicates a low average received signal quality [EN: quantifier]. In this example, the environment is all or part of a telecommunications network; the state of the environment would be encoded by a ML agent using a set of features representing the state of the network, such as the average SINR, network coverage, average received signal quality, total network capacity, and so on).
Jeong, Tuba and Shattil are found as analogous art of optimization modeling. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Shattil / Tuba’s airborne relay system and method to have included Jeong’s teachings around using ML to convert textual terms into linguistic quantifiers. The benefit of these additional features would have enabled efficient optimization computation with more complex requirements (Jeong ¶ [0006]). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Shattil in view of Tuba and Jeong (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of optimization modeling. In such combination each element would have merely performed the same function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Byun in view of Tuba and Jeong above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Conclusion
The following art is made of record and considered pertinent to Applicant’s disclosure:
Boss; Gregory J. et al. US 20160028471 A1, Deployment criteria for unmanned aerial vehicles to improve cellular phone communications.
TANG; Jie et al. US 20210255641 A1, Method for designing three-dimensional trajectory of unmanned aerial vehicle based on wireless power transfer network.
Chai; Eugene et al. US 20220103246 A1, Unmanned aerial vehicle network.
VRIND; TUSHAR et al. US 20200260404 A1, Method and system for positioning low altitude platform station (LAPS) drone cells.
Saravanan; M et al. US 20240224063 A1, Determining allocation of unmanned aerial vehicle base stations in a wireless network.
MELODIA; Tommaso et al. US 20220189320 A1, Software Defined Drone Network Control System.
Neubauer; Thomas et al. US 20200394928 A1, Apparatus and Method for Guiding Unmanned Aerial Vehicles.
OHSHIO; Keiichi US 20230221718 A1, Flight vehicle control apparatus, method and program.
Yamine; Badawi US 11870539 B2, Controlling dispatch of radio coverage drones.
KIRKBRIDE D. W. US 20190389575 A1, Unmanned aerial vehicle (UAV) cluster for delivering payloads, has core UAVs distributed throughout cluster according to selected distribution pattern that distributes core UAVs according to predefined mission characteristic of UAV cluster.
Klaric; Matthew Nicholas et al. US 20100100835 A1, Visualizing geographic-area change detected from high-resolution, remotely sensed imagery.
BRISTOW GRANT MARK et al. EP 3839689 A1, Fleet scheduler.
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REED M. BOND whose telephone number is (571) 270-0585. The examiner can normally be reached Monday - Friday 8:00 am - 5:00 pm.
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, Patricia Munson can be reached at (571) 270-5396. 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.
/REED M. BOND/Examiner, Art Unit 3624 December 31, 2025
/HAMZEH OBAID/Primary Examiner, Art Unit 3624 January 6, 2026