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 .
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.
Claim Status
This action is in response to applicant’s filing on 4/29/2024. Claims 1-20 are pending and considered below.
Specification
The disclosure is objected to because of the following informalities:
As indicated by paragraph [0016] (PGPub) and FIG. 1, UAV is numbered “102” and one or more servers are numbered “104”. In paragraphs [0018-0024], UAV is incorrectly numbered “104”.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“filter unit” (claims 1-7) is one or more of: HEPA filters; UV light filters; electrostatic filters; washable filters; media filters; spun glass filters; pleated filters; and a filter motor coupled to a Fibonacci spiral and a particulate matter filter, as disclosed in applicant’s specification, paragraphs [0020] and [0026]; FIG. 1, UAV-102, and filter system-114; and FIG. 3, UAV-102, control board-202, Fibonacci spiral-302, motor-304, and particulate matter filter-306.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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, 5-11 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al. (US-2019/0108746-A1, hereinafter Chang) in view of Mohammed et al. (Powering UAV with Deep Q-Network for Air Quality Tracking, Sensors 2022, 22, 6118, 8/16/2022, pp. 1-17, hereinafter Mohammed).
Regarding claim 1, Chang discloses:
An unmanned aerial vehicle (UAV) comprising: (paragraphs [0026-0040] and FIG. 1, indoor air quality control system-100, fixed detectors-110, first communication interface-112, unmanned vehicle platform-120, second communication interface-122, processing unit-124, mobile detector-126, mobile air cleaning device-128, cloud computing platform-200, and external data source-300);
one or more processors (paragraphs [0032-0036]);
a memory coupled to the one or more processors (paragraphs [0032-0036]);
a filter unit coupled to the one or more processors (paragraph [0036]); and
initiate, based on the second particulate matter measurement and the first particulate matter measurement, a filtration process (paragraphs [0033-0038]).
Chang does not disclose moving a UAV to a second location that is at a predetermined distance from the first location. However, Mohammed discloses a system for guiding a multi-navigation direction of a UAV utilizing Deep Q-network (DQN) based UAV Pollution Tracking (DUPT), including the following features:
wherein the one or more processors are configured to: (3.5.1. Experience Replay Memory and Mini-Batch (ERM); and FIG. 4, ERM architecture in DUPT);
determine information about a first location (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions);
travel to the first location (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions);
move to a second location that is at a predetermined distance from the first location (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions);
determine first particulate matter measurement at the second location (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions);
determine, based on the first particulate matter measurement and the first location, information about a third location (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions); and
determine second particulate matter measurement at the third location (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions).
Mohammed teaches that an unmanned aerial vehicle (UAV) should collect Air Quality Index (AQI) factors. When a difference between a current AQI and two previous consecutive AQIs is larger than a threshold, then the UAV should traverse to reach an unhealthy area (5. Conclusions). It would have been obvious for a person of ordinary skill in the art at the time of the effective filing date of the claimed invention to incorporate the UAV travel pattern of Mohammed into the miniature aircraft which monitors air quality and performs air cleaning when air quality reaches an air pollution warning value of Chang. A person of ordinary skill would have been motivated to do so, with a reasonable expectation of success, for the purpose of causing the UAV to travel to a position with unhealthy air by intelligently traversing a short path.
Regarding claim 5, Chang does not disclose moving a UAV to a second location that is at a predetermined distance from the first location. However, Mohammed further discloses:
wherein the second particulate matter measurement is lower than the first particulate matter measurement (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions).
Mohammed teaches that an unmanned aerial vehicle (UAV) should collect Air Quality Index (AQI) factors. When a difference between a current AQI and two previous consecutive AQIs is larger than a threshold, then the UAV should traverse to reach an unhealthy area (5. Conclusions). It would have been obvious for a person of ordinary skill in the art at the time of the effective filing date of the claimed invention to incorporate the UAV travel pattern of Mohammed into the miniature aircraft which monitors air quality and performs air cleaning when air quality reaches an air pollution warning value of Chang. A person of ordinary skill would have been motivated to do so, with a reasonable expectation of success, for the purpose of causing the UAV to travel to a position with unhealthy air by intelligently traversing a short path.
Regarding claim 6, Chang further discloses:
wherein the one or more processor causes the UAV to initiate the filtration process at the second location (paragraph [0036]).
Regarding claim 7, Chang does not disclose moving a UAV to a second location that is at a predetermined distance from the first location. However, Mohammed further discloses:
wherein prior to determining the information about the first location, the one or more processors are configured to receive information about an area of interest, the area of interest corresponding to a geographical area in which pollution is present (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; 5. Conclusions; and FIG. 1, DQN UAV agent for tracking unhealthy areas (AQI > 150)).
Mohammed teaches that an unmanned aerial vehicle (UAV) should collect Air Quality Index (AQI) factors. When a difference between a current AQI and two previous consecutive AQIs is larger than a threshold, then the UAV should traverse to reach an unhealthy area (5. Conclusions). It would have been obvious for a person of ordinary skill in the art at the time of the effective filing date of the claimed invention to incorporate the UAV travel pattern of Mohammed into the miniature aircraft which monitors air quality and performs air cleaning when air quality reaches an air pollution warning value of Chang. A person of ordinary skill would have been motivated to do so, with a reasonable expectation of success, for the purpose of causing the UAV to travel to a position with unhealthy air by intelligently traversing a short path.
Regarding claim 8, Chang further discloses:
initiating, based on the first particulate matter measurement and the second particulate matter measurement, a filtration process (paragraphs [0033-0038]).
Chang does not disclose moving a UAV to a second location that is at a predetermined distance from the first location. However, Mohammed further discloses:
determining a first location within an area of interest (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions);
traveling to the first location (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions);
moving to a second location, the second location being a predetermined distance from the first location in x, y, or z directions (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions);
determining a first particulate matter measurement at the second location (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions);
determining, based on the first particulate matter measurement and the first location, information about a third location within the area of interest (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions);
moving to the third location (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions); and
determining a second particulate matter measurement at the third location (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions).
Mohammed teaches that an unmanned aerial vehicle (UAV) should collect Air Quality Index (AQI) factors. When a difference between a current AQI and two previous consecutive AQIs is larger than a threshold, then the UAV should traverse to reach an unhealthy area (5. Conclusions). It would have been obvious for a person of ordinary skill in the art at the time of the effective filing date of the claimed invention to incorporate the UAV travel pattern of Mohammed into the miniature aircraft which monitors air quality and performs air cleaning when air quality reaches an air pollution warning value of Chang. A person of ordinary skill would have been motivated to do so, with a reasonable expectation of success, for the purpose of causing the UAV to travel to a position with unhealthy air by intelligently traversing a short path.
Regarding claim 9, Chang further discloses:
wherein initiating the filtration process further comprising initiating the filtration process at the second location (paragraph [0036]).
Regarding claim 10, Chang does not disclose moving a UAV to a second location that is at a predetermined distance from the first location. However, Mohammed further discloses:
wherein the first particulate matter measurement is higher than the second particulate matter measurement (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions).
Mohammed teaches that an unmanned aerial vehicle (UAV) should collect Air Quality Index (AQI) factors. When a difference between a current AQI and two previous consecutive AQIs is larger than a threshold, then the UAV should traverse to reach an unhealthy area (5. Conclusions). It would have been obvious for a person of ordinary skill in the art at the time of the effective filing date of the claimed invention to incorporate the UAV travel pattern of Mohammed into the miniature aircraft which monitors air quality and performs air cleaning when air quality reaches an air pollution warning value of Chang. A person of ordinary skill would have been motivated to do so, with a reasonable expectation of success, for the purpose of causing the UAV to travel to a position with unhealthy air by intelligently traversing a short path.
Regarding claim 11, Chang further discloses:
further comprising, prior to initiating the filtration process: (paragraphs [0033-0038]); and
initiating the filtration process further based on the third particulate measurement (paragraphs [0033-0038]).
Chang does not disclose moving a UAV to a second location that is at a predetermined distance from the first location. However, Mohammed further discloses:
determining a fourth location within the area of interest (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions); and
determining a third particulate measurement at the fourth location (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions).
Mohammed teaches that an unmanned aerial vehicle (UAV) should collect Air Quality Index (AQI) factors. When a difference between a current AQI and two previous consecutive AQIs is larger than a threshold, then the UAV should traverse to reach an unhealthy area (5. Conclusions). It would have been obvious for a person of ordinary skill in the art at the time of the effective filing date of the claimed invention to incorporate the UAV travel pattern of Mohammed into the miniature aircraft which monitors air quality and performs air cleaning when air quality reaches an air pollution warning value of Chang. A person of ordinary skill would have been motivated to do so, with a reasonable expectation of success, for the purpose of causing the UAV to travel to a position with unhealthy air by intelligently traversing a short path.
Regarding claim 13, Chang does not disclose moving a UAV to a second location that is at a predetermined distance from the first location. However, Mohammed further discloses:
wherein the second location is a first predetermined distance from the first location in the x-direction (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions);
the method further comprising, prior to moving to the third location (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions);
moving to a fourth location that is the first predetermined distance from the first location in the y-direction (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions);
determining a third particulate measurement at the fourth location (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions);
moving back to the first location (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions);
moving to a fifth location that is the first predetermined distance from the first location in the z-direction (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions);
determining a fourth particulate measurement at the fifth location (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions); and
wherein determining the third location is further based on the third particulate measurement and the fourth particulate measurement (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions).
Mohammed teaches that an unmanned aerial vehicle (UAV) should collect Air Quality Index (AQI) factors. When a difference between a current AQI and two previous consecutive AQIs is larger than a threshold, then the UAV should traverse to reach an unhealthy area (5. Conclusions). It would have been obvious for a person of ordinary skill in the art at the time of the effective filing date of the claimed invention to incorporate the UAV travel pattern of Mohammed into the miniature aircraft which monitors air quality and performs air cleaning when air quality reaches an air pollution warning value of Chang. A person of ordinary skill would have been motivated to do so, with a reasonable expectation of success, for the purpose of causing the UAV to travel to a position with unhealthy air by intelligently traversing a short path.
Claims 14-15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Dhawan et al. (U.S. Patent Number 11,295,131, hereinafter Dhawan).
Regarding claim 14, Dhawan discloses:
A method comprising, by an unmanned aerial vehicle (UAV): (col. 26, lines 31-53; and FIG. 6, self steering unmanned aerial system-620);
determining, a bounding box associated with an area of interest (col. 26, line 54 - col. 27, line 5; col. 28, lines 17-43; and FIG. 7, arid vegetation land-731, semi-arid vegetation land-732, arid vegetation land with active human interference-733, and arid vegetation land with dry lightning-734);
determining a first set of locations within the bounding box (col. 26, line 54 - col. 27, line 5; col. 28, lines 17-43);
determining a first location within the bounding box (col. 26, line 54 - col. 27, line 5; col. 28, lines 17-43);
moving to the first location (col. 26, lines 46-50);
determining a first estimated air quality value based on the first location and a second location from the first set of locations (col. 25, line 62 - col. 26, line 30; and FIG. 5, server-500, and training module-522);
determining a first actual air quality value at the first location (col. 27, line 6 - col. 28, line 16);
determining that an absolute difference between the first estimated air quality value and the first actual air quality value is less than or equal to a first threshold value (col. 25, line 62 - col. 26, line 30);
removing the second location from the first set of locations (col. 26, line 54 - col. 27, line 5; col. 28, lines 17-43);
determining a third location within the bounding box (col. 26, line 54 - col. 27, line 5; col. 28, lines 17-43);
moving to the third location (col. 26, lines 46-50); and
performing steps (v) – (ix) at the third location (col. 25, line 62 - col. 26, line 30; and col. 26, line 54 - col. 28, line 43).
Regarding claim 15, Dhawan further discloses:
determining that a number of locations in the first set of locations is less than a second threshold (col. 26, line 54 - col. 27, line 5; col. 28, lines 17-43);
determining a median location of the number of locations (col. 26, line 54 - col. 27, line 5; col. 28, lines 17-43); and
moving to the median location (col. 26, lines 46-50).
Regarding claim 20, Dhawan further discloses:
determining a second estimated air quality value using the third location and a fourth location from the first set of locations (col. 25, line 62 - col. 26, line 30; and FIG. 5, server-500, and training module-522);
determining a second actual air quality value at the third location (col. 27, line 6 - col. 28, line 16);
determining that the absolute difference between the second estimated air quality value and the second actual air quality value is more than the first threshold value (col. 25, line 62 - col. 26, line 30); and
retaining the third location in the first set of locations (col. 26, line 54 - col. 27, line 5; col. 28, lines 17-43).
Claims 16-17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Dhawan, as applied to claims 14-15 above, and further in view of Mohammed.
Regarding claim 16, Dhawan does not disclose moving a UAV to a fourth location that is at a predetermined distance from a median location. However, Mohammed further discloses:
moving to a fourth location, wherein the fourth location is at a predetermined distance from the median location in x, y, or z directions (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions); and
determining a first particulate matter measurement at the fourth location (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions).
Mohammed teaches that an unmanned aerial vehicle (UAV) should collect Air Quality Index (AQI) factors. When a difference between a current AQI and two previous consecutive AQIs is larger than a threshold, then the UAV should traverse to reach an unhealthy area (5. Conclusions). It would have been obvious for a person of ordinary skill in the art at the time of the effective filing date of the claimed invention to incorporate the UAV travel pattern of Mohammed into the smoke and fire monitoring system which uses a bounding box of Dhawan. A person of ordinary skill would have been motivated to do so, with a reasonable expectation of success, for the purpose of causing the UAV to travel to a position with unhealthy air by intelligently traversing a short path.
Regarding claim 17, Dhawan does not disclose moving a UAV to a second location that is at a predetermined distance from the first location. However, Mohammed further discloses:
moving to a fourth location, wherein the fourth location is at a first predetermined distance from the median location (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions);
determining a first particulate matter measurement at the fourth location (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions);
moving to a fifth location, wherein the fifth location is at a second predetermined distance from the median location (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions);
determining a second particulate matter measurement at the fifth location (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions); and
determining that the first particulate matter measurement is higher than the second particulate matter measurement (3. DQN-Based UAV Pollution Tracking (DUPT) Methodology; and 5. Conclusions).
Mohammed teaches that an unmanned aerial vehicle (UAV) should collect Air Quality Index (AQI) factors. When a difference between a current AQI and two previous consecutive AQIs is larger than a threshold, then the UAV should traverse to reach an unhealthy area (5. Conclusions). It would have been obvious for a person of ordinary skill in the art at the time of the effective filing date of the claimed invention to incorporate the UAV travel pattern of Mohammed into the smoke and fire monitoring system which uses a bounding box of Dhawan. A person of ordinary skill would have been motivated to do so, with a reasonable expectation of success, for the purpose of causing the UAV to travel to a position with unhealthy air by intelligently traversing a short path.
Regarding claim 19, Dhawan does not disclose a Gaussian field estimation function. However, Mohammed further discloses:
wherein determining the first estimated air quality value includes using a Gaussian field estimation function (Abstract).
Mohammed teaches that Deep Q-network (DQN) based UAV Pollution Tracking (DUPT) is evaluated and validated using an air pollution environment generated by a well-known Gaussian distribution and kriging interpolation (Abstract). It would have been obvious for a person of ordinary skill in the art at the time of the effective filing date of the claimed invention to incorporate the Gaussian distribution of Mohammed into the smoke and fire monitoring system which uses a bounding box of Dhawan. A person of ordinary skill would have been motivated to do so, with a reasonable expectation of success, for the purpose of causing the UAV to travel to a position with unhealthy air by intelligently traversing a short path.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Dhawan in view of Mohammed, as applied to claim 17 above, and further in view of Chang.
Regarding claim 18, Dhawan in view of Mohammed does not disclose initiating a filtration process. However, Chang further discloses:
further comprising initiating a filtration process at the fourth location (paragraphs [0033-0038]).
Chang teaches that a miniature aircraft should monitor air quality and perform air cleaning when air quality reaches an air pollution warning value (paragraphs [0028] and [0033-0038]). It would have been obvious for a person of ordinary skill in the art at the time of the effective filing date of the claimed invention to incorporate the air cleaning of Chang into the smoke and fire monitoring system which uses a bounding box of Dhawan in view of Mohammed. A person of ordinary skill would have been motivated to do so, with a reasonable expectation of success, for the purpose of causing the UAV to travel to a position with unhealthy air by intelligently traversing a short path.
Allowable Subject Matter
Claims 2-4 and 12 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bai et al. (U.S. Patent Number 10,648,805) discloses a method for tracking and identifying the transmission trajectory in real 3-D space of a polluted air mass (Abstract).
Villa et al., An Overview of Small Unmanned Aerial Vehicles for Air Quality Measurements: Present Applications and Future Prospectives, Sensors 2016, 16, 1072, 7/12/2016, pp. 1-29, discloses small Unmanned Aerial Vehicles (UAVs) equipped with sensors for in-situ air quality monitoring (Abstract).
Marinov et al., UAVs Based Particulate Matter Pollution Monitoring, Proc. XXVIII International Scientific Conference Electronics - ET2019, September 12 - 14, 2019, pp. 1-4, discloses the use of an off the shelf UAV equipped with mobile monitoring devices to collect three-dimensional air pollutant concentration data (Abstract).
Dabrowska et al., Environmental quality monitoring with unmanned aircraft vehicle, 2020 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA 2020), September 23-25, 2020, pp. 128-132, discloses using unmanned aerial vehicles to obtain a large number of measurement points from a large area; which allows for mapping zones particularly exposed to emissions and to assessing existing hazards on an ongoing basis (Abstract).
Gao et al., AQ360: UAV-Aided Air Quality Monitoring by 360-Degree Aerial Panoramic Images in Urban Areas, IEEE Internet of Things Journal, Vol. 8, No. 1, 1/1/2021, pp. 428-442, discloses an unmanned aerial vehicle-aided (UAV-aided) monitoring system which detects the air quality level from 360-degree aerial panoramic images taken by an onboard camera (Abstract).
Chhikara et al., Federated Learning and Autonomous UAVs for Hazardous Zone Detection and AQI Prediction in IoT Environment, IEEE Internet of Things Journal, Vol. 8, No. 20, 10/15/2021, pp. 15456-15467, discloses a distributed federated learning (FL) algorithm within a UAV swarm that collects air quality data using built-in sensors (Abstract).
Hossain et al., A GPS Based Unmanned Drone Technology for Detecting and Analyzing Air Pollutants, IEEE Instrumentation & Measurement Magazine, Vol. 25, Issue 9, December 2022, pp. 53-60, discloses an automatic unmanned aerial system which collects and monitors air quality data (Measuring Particulates by Drone).
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TAMARA L. WEBER
Examiner
Art Unit 3667
/TAMARA L WEBER/ Examiner, Art Unit 3667