DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed 03/30/2026 is being entered. Claims 1, 8, 11, 18, 19, and 20 are amended. Claims 1-20 are pending, and rejected as detailed below. This action is final as necessitated by amendment.
35 U.S.C. 112(b) Claim Rejections
Applicant's arguments filed 03/30/2026 have been fully considered but they are not persuasive. More specifically, “the HIRF source” teaches a single HIRF source, and “high-intensity radiated field (HIRF) sources teaches a plurality of HIRF sources. As a result, applicant argument is invalid as a single HIRF source can not refer back to multiple HIRF sources. According to the BRI of the claim, “determining a HIRF stand-off zone for each of the HIRF sources based on the radiation tolerance level of the airborne vehicle and a potential radiated field generated by the HIRF source” is referring to a particular HIRF source from the HIRF sources. As a result, examiner’s understanding of “the HIRF source” is different from the applicant’s explanation. Therefore the 35 U.S.C. 112(b) claim rejection for claims , 8, 11, 18, and 20 have been maintained.
In order to clarify the applicant’s intention, examiner advices the applicant to amend the respective claims to overcome the 112(b) claim rejection. For example, claim 1 can be amended as following:
“determining a HIRF stand-off zone for each of the HIRF sources based on the radiation tolerance level of the airborne vehicle and a potential radiated field generated by each of the HIRF sources, wherein…”
Response to Arguments
RESPONSE TO § 102 REJECTIONS
VELASTRI Does Not Teach Determining HIRF Stand-Off Zones Based on Vehicle Radiation Tolerance
Argument:
Applicant argues that the present approach is tailored to determining for a particular source a stand-off zone relevant to a particular vehicle. Independent claim 1 requires: "obtaining a radiation tolerance level for the airborne vehicle" and "determining a HIRF stand-off zone for each HIRF source based on the radiation tolerance level and a potential radiated field generated
by the HIRF source." The VELASTRI focus is different. VELASTRI creates a visualization of
aggregated aerial flight safety "risk" data (population density, weather, GPS availability,
electromagnetic fields, etc.) and represents that risk abstractly as "virtual obstacle objects."
See, e.g., VELASTRI, T [0037]. For the aggregation. VELASTRI relies on a collection of
existing risk factors for the corresponding area of interest, which may be dynamic
(VELASTRI, I [0044], [0075]), affecting a dimension (e.g., height) of the virtual obstacle
object. VELASTRI does not disclose, among other things, (1) a quantified vehicle-specific
radiation tolerance level (V/m); (2) calculating field strength as a function of transmitter
effective isotropic radiated power (EIRP) and distance; or (3) defining stand-off zones
where the radiation field intensity exceeds vehicle tolerance. By contrast, Applicant's
claims require steps involving physics-based HIRF calculations, including minimum safe
distances derived from electromagnetic propagation equations. This distinction is
fundamental, not semantic. Thus, VELASTRI's generalized "electromagnetic field data" is qualitatively different from Applicant's quantitative HIRF exclusion zones. VELASTRI fails to teach the specific process claimed in the present approach for generating stand-off zones based
on vehicle-specific data and transmitter EIRP data. Instead, VELASTRI is focused on
aggregation and visualization as a consumer of generalized data, without disclosing steps
for processing data from any single source or category. The focus on VELASTRI is
visualization of risk. This differs from a service focused on processing data to refine the
risk understanding from a single category of sources, namely that from transmitter radiated
power to a particular vehicle. Aside from that qualitative distinction, the differences matter in a number of related aspects of operation. VELASTRI teaches that aggregation of risk aids in "visualizing aerial flight safety risks." (VELASTI, 1 [0037]). A virtual obstacle object of aggregated risk data is more useful to VELASTRI than such an object limited to a single category of data. Yet the aggregation of risk taught as being useful by VELASTRI obscures an understanding of data from any single risk category. In fact, VELASTRI teaches expanding a virtual obstacle object not just on dynamic risk data but also based on other factors, such as a
decrease in confidence of a risk level (e.g., VELASTRI, 1 [0066]). Thus, the efficacy of
an aggregated virtual obstacle to visualization teaches away from the present approach.
Response:
Applicant's arguments filed 03/30/2026 have been fully considered but they are not persuasive. In reference to the BRI of claim 1, VELASTRI discloses a quantified vehicle-specific radiation tolerance level in para.0066 and calculating field strength as a function of transmitter power in para. 0060. Even though, VELASTRI does not explicitly teach calculating field strength as a function of distance, the combination of VELASTRI and Liang teach calculating field strength as a function of distance, as amended herein. VELASTRI also teach defining stand-off zones where the radiation field intensity exceeds vehicle tolerance in para. 0066. VELASTRI teaches the specific process claimed in the present approach for generating stand-off zones based on vehicle-specific data and transmitter data in para. 0066. Even though VELASTRI is able to provide the visualization of risk, VELASTRI identifies a region, identifies HIRF sources in the region, obtains radiation tolerance level for vehicle, determines a HIRF stand-off zones for each HIRF sources, and generates flight path before displaying the detailed flight path with the visualization of risk. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features (bolded limitations) upon which applicant relies (i.e., a quantified vehicle-specific radiation tolerance level (V/m), calculating field strength as a function of transmitter effective isotropic radiated power (EIRP) and distance, requiring steps involving physics-based HIRF calculations, including minimum safe distances derived from electromagnetic propagation equations) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
VELASTRI Does Not Teach Directional or Frequency-Dependent HIRF Modeling
Argument:
Applicant argues that applicant's invention expressly accounts for: (1) transmitter directionality; (2) antenna elevation and azimuth limits; (3) frequency-dependent tolerance levels; and (4) two- or three-dimensional stand-off regions. VELASTRI assumes omnidirectional, abstract risk volumes (e.g., "a 'virtual obstacle object' is a three-dimensional (3D) volume " VELASTRI, 1 [0044]) and does not compute direction-limited or frequency-specific radiation exclusion regions, as required by the claims.
Response:
Applicant's arguments filed 03/30/2026 have been fully considered but they are not persuasive. More specifically, the combination of VELASTRI and Liang teaches Directional or Frequency-Dependent HIRF Modeling, transmitter directionality, according to the BRI of the claimed language [Liang para. 0159 and 0114], as amended herein. VELASTRI teaches Directional or Frequency-Dependent HIRF Modeling, frequency-dependent tolerance levels, according to the BRI of the claimed language [VELASTRI, para. 0044]. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features (bolded limitations) upon which applicant relies (i.e., antenna elevation and azimuth limits and two-or three-dimensional stand-off regions) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
VELASTRI Does Not Generate Flight Plans Based on HIRF Stand-Off Zones
Argument:
Applicant argues that VELASTRI's flight planning avoids visually rendered "risk objects" derived from aggregated, heterogeneous factors. Applicant's claims instead require that the flight plan is generated based on HIRF stand-off zones, i.e., exclusion regions defined solely by electromagnetic interference thresholds relative to the vehicle's tolerance. VELASTRI
never isolates HIRF as a determinative planning constraint. For the same reasons,
VELASTRI fails to disclose the additional limitations of claims 2-7 and 9-17, including,
for example, (1) selecting tolerance levels to trade navigable airspace versus shielding cost;
(2) determining stand-off zones as fan-shaped or arc-limited regions; and (3) calculating
minimum safe distances using transmitter power and antenna geometry. Accordingly,
VELASTRI does not anticipate the claims, and the § 102 rejections should be withdrawn.
Response:
Applicant's arguments filed 03/30/2026 have been fully considered but they are not persuasive. More specifically, VELASTRI's flight planning avoids HIRF sources as the hexagonal or other shaped of the risk related factors shows the 3-dimensional effective area of the risk factors. Then, the flight path is based upon the 3-dimensional shape of the risk factors [VELASTRI, para. 0066] and the radiation tolerance level [VELASTRI, para. 0060]. As a result, VELASTRI is able to calculate the minimum safe distances using transmitter power and antenna geometry. Furthermore, VELASTRI’s para. 0068 is able to teach the selecting of tolerance levels of the navigable airspace. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features (bolded limitations) upon which applicant relies (i.e., shielding cost and determining stand-off zones as fan-shaped or arc-limited regions) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
RESPONSE TO § 103 REJECTIONS
Liang Does Not Teach Vehicle-Tolerance-Based Stand-Off Zones
Argument:
Applicant argues that, assuming Liang discloses the items asserted within the Office Action, Liang is nevertheless unavailing to disclose an HIRF stand-off zone limited to an angular region corresponding to the direction of radiation of the HIRF source. Further, the combination does not disclose: (1) deriving minimum safe distances from transmitter EIRP; (2) comparing those distances to a vehicle-specific tolerance threshold; or (3) generating flight plans constrained by those zones. Thus, claims 8 and 18 remain non-obvious.
Response:
Applicant's arguments filed 03/30/2026 have been fully considered but they are not persuasive. More specifically, the combination of VELASTRI and Liang teaches HIRF stand-off zone limited to an angular region corresponding to the direction of radiation of the HIRF source [Liang, para. 0159 and 0114]. The combination of VELASTRI and Liang also teaches the HIRF source as a function of distance [Liang, para. 0135], comparing those distances to a vehicle-specific tolerance threshold [VELASTRI. para. 0060 and para. 0066], generating flight plans constrained by those zones [VELASTRI, para. 0068].
Liang Addresses Localization, Not Avoidance in generating a flight plan
Argument:
Applicant argues that, Liang is focused on locating or localizing radioactive sources using directional detectors mounted on mobile platforms ("there is a need for portable directional radiation detector systems and techniques to provide directionality and/or localization of ionizing
radiation sources " Liang, I [0003]). Much of Liang is directed to improvements to or configuration of radiation detectors, systems, and methods for finding or generating the
radiation source localization survey information. While Liang does contemplate survey
patterns that avoid specific radiological plumes that might contaminate a mobile platform,
such course adjustments are within the context of using the sensors to generate a radiation
map for locating the radioactive material. (Liang, 11 [0155]-[0158]) Thus, Liang's general
purpose is to move an unmanned sensor platform closer to a radiation risk source so that it can identify its location using radiation detectors onboard that platform ("moving a radiation detector during a spatial search can be used for estimating a direction to the source. Liang, 1 [0019]). As might be expected, Liang teaches that a search pattern that decreases distance in approaching radioactive material, increases radioactive intensity and aids in locating the radioactive materials. (Liang, T [0019]). Applicant's approach is directed to the opposite. First, the present approach requires HIRF sources that are known and cataloged by government agencies (FAA, NTIA, NOAA databases)(See, e.g., " [0025]-[0029]). In contrast, Liang is dealing with radiation sources
of unknown location and strength. Second, the data of such HIRF sources are processed
to define stand-off zones where the radiation field intensity exceeds the vehicle tolerance.
The objective of the present approach is a particular teaching for avoidance, not localization or data collection. Given that the present approach relies on existing databases, no onboard directional radiation detector is required. Similarly, combining Liang with VELASTRI would require a complete inversion of each other's purpose, contrary to established § 103 jurisprudence.
Response:
Applicant's arguments filed 03/30/2026 have been fully considered but they are not persuasive. More specifically, VELASTRI identifies a region, identifies HIRF sources in the region, obtains radiation tolerance level for vehicle, determines a HIRF stand-off zones for each HIRF sources, and generates flight path. Liang teaches different aspects of localizing the HIRF sources ( estimating the angular direction of the HIRF and intensity level of the HIRF sources). As a result, the combination of VELASTRI and Liang anticipate the claimed limitations. It is well settled that non-obviousness cannot be established by attacking references individually where the rejection is based on the teachings for a combination of references. Therefore, applicant is incorrect to point out that Liang does not teach the generation of the flight plan, when the VELASTRI teaches the generation of the flight path.
No Motivation to Combine
Argument:
Applicant argues that, as noted above, VELASTRI is focused on visualizing aerial flight safety risks (VELASTRI, 1 [0037]). While an aerial vehicle may host one or more sensors, data
collection is not required and the availability of existing risk associated with a flight path
is assumed. To the extent that an aerial vehicle might include a sensor, the teaching of
VELASTRI is the avoidance of risk visualized in the form of "virtual obstacle objects"
(VELASTRI, 1 [0037]). The avoidance of VELASTRI is essentially directed to the
opposite of the data collection and localization of Liang. There is no articulated reason
why one of ordinary skill would retrofit VELASTRI with Liang's hardware-based radiation
localization system merely to compute exclusion zones that Applicant already computes
analytically. The proposed combination relies on hindsight reconstruction.
Response:
Applicant's arguments filed 03/30/2026 have been fully considered but they are not persuasive. In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). Furthermore, VELASTRI [VELASTRI, 0060] talks about data collection through sensors so that the flight path can be calculated based on the collected data and not assumed. Furthermore, calculated light path also takes into consideration of the avoidance of risk factors. Liang also [Liang, 0046] talks about data collection through the sensors so that radiation sources can be localized with reference to radiation intensity. Therefore, VELASTRI and Liang utilize similar method to obtain data regarding the electromagnetic fields so that operation of the aerial vehicle can be optimized thus localizing the radiation sources and reducing the adverse effect of the flight safety (by interfering with navigation systems, sensors, components, etc. onboard the aerial vehicle).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 19 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Applicant has mentioned “build stage” in claim 19. However, Applicant fails to provide any additional information what type of apparatus is recognized as “build stage”.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1, 8, 11, 18, 19, and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the term "HIRF source" in line 11. There is insufficient antecedent basis for this term in the claim.
Claim 8 recites the term "HIRF source" in line 3. There is insufficient antecedent basis for this term in the claim.
Claim 11 recites the term "HIRF source" in line 10. There is insufficient antecedent basis for this term in the claim.
Claim 18 recites the term "HIRF source" in line 3. There is insufficient antecedent basis for this term in the claim.
Claim 19 recites “build stage” that is a relative term which renders the claim indefinite. The term “build stage” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear that the “build stage” is referring to manufacturing cost, manufacturing process, or anything else.
Claim 20 recites the term "HIRF source" in line 9. There is insufficient antecedent basis for this term in the claim.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-7, 9-17, and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by VELASTRI (US 20200286388 A1).
Regarding claim 1, VELASTRI teaches (currently Amended) A method of generating a flight plan for an airborne vehicle (VELASTRI, at least one para. 0003; “According to one embodiment, a method comprises determining a flight path for an aerial vehicle. The method also comprises calculating at least one risk area along the flight path based on risk-related data associated with the flight path (e.g., data on population density, electromagnetic fields, absence of location signals such as Global Positioning System (GPS) signals, weather, network coverage, aviation related data, etc.). ”), the method comprising:
identifying a region of operation of the airborne vehicle (VELASTRI, at least one para. 0051; “In step 301, the routing module 201 determines a flight path or potential flight path of an aerial vehicle 101. The flight path, for instance, can be determined using any routing engine known in the art based on an origin and destination specified by a pilot of the aerial vehicle 101 for the route at a given time (e.g., expected start time of the route).”) from one or more transmitter databases (VELASTRI, at least one para. 0060; “Electromagnetic field data can also be sensed used using sensors located on aerial vehicles 101, in the infrastructure (e.g., smart city infrastructure), and/or from any other sensor in the area of interest. In addition or alternatively, historical or previously sensed electromagnetic data that has been stored for the areas of interest along the flight path can be stored and retrieved from the geographic database 121.”);
identifying high-intensity radiated field (HIRF) sources associated with the region of operation of the airborne vehicle (VELASTRI, at least one para. 0052-0058; “In step 303, the data ingestion module 203 retrieves risk-related related data on the flight path and areas within a threshold distance of the flight path for the given time specified in step 301. By way of example, the risk-related data includes but is not limited to at least one of any the following risk factors: Population density data; Electromagnetic field data; Data on an absence of location signals; Weather data; Network coverage data; and Aviation related data.”) that defines a maximum electromagnetic field strength at which avionics of the airborne vehicle remain operational (VELASTRI, at least one para. 0066; “given thresholds on confidence levels and/or number of observations of risk-related data for the area, the risk module 205 determines the size of the hexagon or other shape (e.g., by picking the minimum possible size of the hexagon or shape that meets the confidence or observation thresholds).”, It is also inherent and obvious that the maximum electromagnetic field strength is the radiation tolerance level.);
obtaining a radiation tolerance level for the airborne vehicle (VELASTRI, at least one para. 0060; “In one embodiment, the electromagnetic field data can be used to determine whether electromagnetic fields in the areas of the flight path can adversely affect flight safety, for instance, by interfering with navigation systems, sensors, components, etc. onboard the aerial vehicle 101.”, in other words, to determine the adverse interference between the onboard components of the aerial vehicle and the electromagnetic fields, the radiation tolerance level for aerial vehicle has to be obtained. Therefore, it is inherent that the radiation tolerance level for aerial vehicle is obtained.);
determining a HIRF stand-off zone for each of the HIRF sources (VELASTRI, at least one para. 0066; “After aggregating risk-related data for a flight path or potential flight path of interest, the risk module 205 can calculate at least one risk area along the flight path based on risk-related data associated with the flight path. The at least one risk area can correspond to a geographic area to be represented by a virtual obstacle object. For example, the risk area can be defined as a hexagon or any other shape (e.g., rectilinear polygon, Voronoi shape, etc.) forming the footprint of the virtual obstacle object. In one embodiment, the size of the hexagon or shape (e.g., the extent of the geographic area represented by or corresponding to the shape) can be based on the density of the available risk-related data for a given area. For example, given thresholds on confidence levels and/or number of observations of risk-related data for the area, the risk module 205 determines the size of the hexagon or other shape (e.g., by picking the minimum possible size of the hexagon or shape that meets the confidence or observation thresholds).”) based on the radiation tolerance level of the airborne vehicle (VELASTRI, at least one para. 0102; “The learner module then compares the predicted matching probability of the predicted population density feature to the ground truth population density data for each input feature set in the ground truth training data set. The learner module then computes an accuracy of the predictions for the initial set of model parameters.”) and (VELASTRI, at least one para. 0052; “In step 303, the data ingestion module 203 retrieves risk-related related data on the flight path and areas within a threshold distance of the flight path for the given time specified in step 301. By way of example, the risk-related data includes but is not limited to at least one of any the following risk factors: [0053] Population density data; [0054] Electromagnetic field data”) and a potential radiated field generated by the HIRF source (VELASTRI, at least one para. 0066; “After aggregating risk-related data for a flight path or potential flight path of interest, the risk module 205 can calculate at least one risk area along the flight path based on risk-related data associated with the flight path. The at least one risk area can correspond to a geographic area to be represented by a virtual obstacle object. For example, the risk area can be defined as a hexagon or any other shape (e.g., rectilinear polygon, Voronoi shape, etc.) forming the footprint of the virtual obstacle object. In one embodiment, the size of the hexagon or shape (e.g., the extent of the geographic area represented by or corresponding to the shape) can be based on the density of the available risk-related data for a given area. For example, given thresholds on confidence levels and/or number of observations of risk-related data for the area, the risk module 205 determines the size of the hexagon or other shape (e.g., by picking the minimum possible size of the hexagon or shape that meets the confidence or observation thresholds).”), wherein determining the HIRF stand-off zone comprises calculating a minimum separation distance at which a field strength generated by the HIRF source falls below the radiation tolerance level of the airborne vehicle (VELASTRI, at least one para. 0066; “the risk module 205 determines the size of the hexagon or other shape (e.g., by picking the minimum possible size of the hexagon or shape that meets the confidence or observation thresholds”); and
generating the flight plan for the airborne vehicle based on the HIRF stand-off zones within the region of operation (VELASTRI, at least one para. 0068; “In the example of FIG. 4, the mapping platform 117 receives or generates a flight path 401 that passes over the geographic areas corresponding to hexagons 403a-403f. For each area of interest of the corresponding hexagons 403a-403f, the mapping platform 117 aggregates the risk factors for a time associated with the flight path 401 (e.g., time that the aerial vehicle 101 is expected to pass over each of the hexagons 403a-4030. The mapping platform 117 then generates a virtual obstacle object representation of each hexagon 403a-403f according to the embodiments described herein. As shown, a virtual obstacle object representation 405 is generated for the area corresponding to hexagon 403a.”) such that the airborne vehicle avoids operation within the HIRF stand-off zones (VELASTRI, at least one para. 0068; “In the example of FIG. 4, the mapping platform 117 receives or generates a flight path 401 that passes over the geographic areas corresponding to hexagons 403a-403f.”, wherein passing over the geographic areas corresponding to hexagons 403a-403f teach that the airborne vehicle avoids operation within the HIRF stan-off zones).
Regarding claim 2, VELASTRI teaches (Previously Presented) The method of claim 1, wherein the region of operation of the airborne vehicle is identified based on a predetermined distance from at least one of an origin location or a destination location for the airborne vehicle (VELASTRI, at least one para. 0052; “In step 303, the data ingestion module 203 retrieves risk-related related data on the flight path and areas within a threshold distance of the flight path for the given time specified in step 301. [0051] The flight path, for instance, can be determined using any routing engine known in the art based on an origin and destination specified by a pilot of the aerial vehicle 101 for the route at a given time (e.g., expected start time of the route).”).
Regarding claim 3, VELASTRI teaches (Previously Presented) The method of claim 1, wherein the radiation tolerance level of the airborne vehicle is selected based on an amount of navigable airspace within the region of operation for the airborne vehicle relative to the HIRF sources (VELASTRI, at least one para. 0072; “FIG. 5 is a diagram of a trip planning user interface (UI) 501 for visualizing risk levels for aerial vehicle flights, according to one embodiment. In this example, the UI 501 includes a first element 503 for presenting information on how the risk levels were calculated. For example, the element 503 lists the risk factors (e.g., population, electric fields, loss of GPS, and high winds) that were considered in computing the risk levels represented on the element 505. Element 505b displays the rendered virtual obstacle objects determined over areas around or near the flight path 507. In this way, the flight path 507 can be drawn or computed to avoid passing through any of the virtual obstacle objects to minimize safety risks over the flight path 507. For example, the mapping platform 117 can initiate a generation of a different flight path based on determining that a risk level associated with a current or potential flight path is above a risk threshold.”, wherein the flight path 507 shows the amount of navigable airspace within the region of operation for the airborne vehicle relative to the HIRF sources).
Regarding claim 4, VELASTRI teaches (Previously Presented) The method of claim 1, wherein the radiation tolerance level for the airborne vehicle is selected based on a quantity of HIRF sources within the region of operation and a radiation intensity of each of the HIRF sources within the region of operation (VELASTRI, at least one para. 0072; “FIG. 5 is a diagram of a trip planning user interface (UI) 501 for visualizing risk levels for aerial vehicle flights, according to one embodiment. In this example, the UI 501 includes a first element 503 for presenting information on how the risk levels were calculated. For example, the element 503 lists the risk factors (e.g., population, electric fields, loss of GPS, and high winds) that were considered in computing the risk levels represented on the element 505. Element 505b displays the rendered virtual obstacle objects determined over areas around or near the flight path 507. In this way, the flight path 507 can be drawn or computed to avoid passing through any of the virtual obstacle objects to minimize safety risks over the flight path 507. For example, the mapping platform 117 can initiate a generation of a different flight path based on determining that a risk level associated with a current or potential flight path is above a risk threshold.”, as shown below, wherein the flight path 1 in between two virtual obstacle shows the quantity of HIRF sources, and fight path 2 over the virtual obstacle shows the intensity of HIRF sources).
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Regarding claim 5, VELASTRI teaches (Previously Presented) The method of claim 1, wherein each HIRF stand-off zone defines a geographical space in the region of operation having a potential radiated field intensity that is equal to or greater than the radiation tolerance level of the airborne vehicle (VELASTRI, at least one para. 0044; “the height and/or any other dimension of the virtual obstacle object can be scaled to be proportional to the calculated risk level for the area. In one embodiment, the height of the virtual obstacle object is a function of time and hence creates a user interface with a dynamic landscape of multiple virtual obstacle objects that go “up and down” over the course of the day or other period of time to reflect the frequently changing patterns of the risk function aggregating the risk factors for an area of interest such as but not limited to population density and/or any other risk parameters that can affect the safety of operating the aerial vehicle 101.”, since the flight path is created to avoid the virtual obstacles, it is inherent that the defined geographical spaces are equal or greater than the radiation tolerance level of the airborne vehicle.).
Regarding claim 6, VELASTRI teaches (Previously Presented) The method of claim 5, wherein the geographical space of the HIRF stand-off zone is defined as a two-dimensional space in the region of operation (VELASTRI, at least one para. 0123; “In one embodiment, geographic features (e.g., two-dimensional or three-dimensional features) are represented using polygons.”, As shown below, the flight path 507 of the aerial vehicle traveling in between two virtual obstacles represents a two-dimensional space.).
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Regarding claim 7, VELASTRI teaches (Previously Presented) The method of claim 6, wherein the geographical space of the HIRF stand-off zone is defined as a three-dimensional space in the region of operation (VELASTRI, at least one para. 0123; “In one embodiment, geographic features (e.g., two-dimensional or three-dimensional features) are represented using polygons.”, As shown below, the flight path 507 of the aerial vehicle traveling over the virtual obstacle represents a three-dimensional space.).
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Regarding claim 9, VELASTRI teaches (Previously Presented) The method of claim 1, wherein generating the flight plan for the airborne vehicle based on the HIRF stand-off zone includes plotting a flight plan for the airborne vehicle that avoids intersecting the HIRF stand-off zones (VELASTRI, at least one para. 0072; “FIG. 5 is a diagram of a trip planning user interface (UI) 501 for visualizing risk levels for aerial vehicle flights, according to one embodiment. In this example, the UI 501 includes a first element 503 for presenting information on how the risk levels were calculated. For example, the element 503 lists the risk factors (e.g., population, electric fields, loss of GPS, and high winds) that were considered in computing the risk levels represented on the element 505. Element 505b displays the rendered virtual obstacle objects determined over areas around or near the flight path 507. In this way, the flight path 507 can be drawn or computed to avoid passing through any of the virtual obstacle objects to minimize safety risks over the flight path 507. For example, the mapping platform 117 can initiate a generation of a different flight path based on determining that a risk level associated with a current or potential flight path is above a risk threshold.”).
Regarding claim 10, VELASTRI teaches (Previously Presented) The method of claim 1, wherein identifying the HIRF sources includes obtaining transmitter location and characteristics from regulatory databases (VELASTRI, at least one para. 0060; “Electromagnetic field data can also be sensed used using sensors located on aerial vehicles 101, in the infrastructure (e.g., smart city infrastructure), and/or from any other sensor in the area of interest. In addition or alternatively, historical or previously sensed electromagnetic data that has been stored for the areas of interest along the flight path can be stored and retrieved from the geographic database 121.”).
Regarding claim 11, VELASTRI teaches (Currently Amended) A non-transitory computer-readable storage medium embodying programmed instructions which, when executed by a processor, are operable for performing a method (VELASTRI, at least one para. 0004; “According to another embodiment, an apparatus comprises at least one processor, and at least one memory including computer program code for one or more computer programs, the at least one memory and the computer program code configured to, with the at least one processor, cause, at least in part, the apparatus to determine a flight path for an aerial vehicle.”) comprising:
identifying a region of operation of an airborne vehicle (VELASTRI, at least one para. 0051; “In step 301, the routing module 201 determines a flight path or potential flight path of an aerial vehicle 101. The flight path, for instance, can be determined using any routing engine known in the art based on an origin and destination specified by a pilot of the aerial vehicle 101 for the route at a given time (e.g., expected start time of the route).”);
identifying high-intensity radiated field (HIRF) sources associated with the region of operation of the airborne vehicle (VELASTRI, at least one para. 0052-0058; “In step 303, the data ingestion module 203 retrieves risk-related related data on the flight path and areas within a threshold distance of the flight path for the given time specified in step 301. By way of example, the risk-related data includes but is not limited to at least one of any the following risk factors: Population density data; Electromagnetic field data; Data on an absence of location signals; Weather data; Network coverage data; and Aviation related data.”) from one or more transmitter databases (VELASTRI, at least one para. 0060; “Electromagnetic field data can also be sensed used using sensors located on aerial vehicles 101, in the infrastructure (e.g., smart city infrastructure), and/or from any other sensor in the area of interest. In addition or alternatively, historical or previously sensed electromagnetic data that has been stored for the areas of interest along the flight path can be stored and retrieved from the geographic database 121.”);
obtaining a radiation tolerance level for the airborne vehicle (VELASTRI, at least one para. 0060; “In one embodiment, the electromagnetic field data can be used to determine whether electromagnetic fields in the areas of the flight path can adversely affect flight safety, for instance, by interfering with navigation systems, sensors, components, etc. onboard the aerial vehicle 101.”, in other words, to determine the adverse interference between the onboard components of the aerial vehicle and the electromagnetic fields, the radiation tolerance level for aerial vehicle has to be obtained. Therefore, it is inherent that the radiation tolerance level for aerial vehicle is obtained.);
determining a HIRF stand-off zone for each of the HIRF sources (VELASTRI, at least one para. 0066; “After aggregating risk-related data for a flight path or potential flight path of interest, the risk module 205 can calculate at least one risk area along the flight path based on risk-related data associated with the flight path. The at least one risk area can correspond to a geographic area to be represented by a virtual obstacle object. For example, the risk area can be defined as a hexagon or any other shape (e.g., rectilinear polygon, Voronoi shape, etc.) forming the footprint of the virtual obstacle object. In one embodiment, the size of the hexagon or shape (e.g., the extent of the geographic area represented by or corresponding to the shape) can be based on the density of the available risk-related data for a given area. For example, given thresholds on confidence levels and/or number of observations of risk-related data for the area, the risk module 205 determines the size of the hexagon or other shape (e.g., by picking the minimum possible size of the hexagon or shape that meets the confidence or observation thresholds).”) based on the radiation tolerance level of the airborne vehicle (VELASTRI, at least one para. 0102; “The learner module then compares the predicted matching probability of the predicted population density feature to the ground truth population density data for each input feature set in the ground truth training data set. The learner module then computes an accuracy of the predictions for the initial set of model parameters.”) and a potential radiated field generated by the HIRF source (VELASTRI, at least one para. 0066; “After aggregating risk-related data for a flight path or potential flight path of interest, the risk module 205 can calculate at least one risk area along the flight path based on risk-related data associated with the flight path. The at least one risk area can correspond to a geographic area to be represented by a virtual obstacle object. For example, the risk area can be defined as a hexagon or any other shape (e.g., rectilinear polygon, Voronoi shape, etc.) forming the footprint of the virtual obstacle object. In one embodiment, the size of the hexagon or shape (e.g., the extent of the geographic area represented by or corresponding to the shape) can be based on the density of the available risk-related data for a given area. For example, given thresholds on confidence levels and/or number of observations of risk-related data for the area, the risk module 205 determines the size of the hexagon or other shape (e.g., by picking the minimum possible size of the hexagon or shape that meets the confidence or observation thresholds).”), wherein the HIRF stand-off zone corresponds to a geographic region (VELASTRI, at least one para. 0060; “the size of the hexagon or shape (e.g., the extent of the geographic area represented by or corresponding to the shape) can be based on the density of the available risk-related data for a given area.”) in which an electromagnetic field strength exceeds the radiation level of the airborne vehicle (VELASTRI, at least one para. 0060; “Electromagnetic field data can also be sensed used using sensors located on aerial vehicles 101, in the infrastructure (e.g., smart city infrastructure), and/or from any other sensor in the area of interest. In addition or alternatively, historical or previously sensed electromagnetic data that has been stored for the areas of interest along the flight path can be stored and retrieved from the geographic database 121. In one embodiment, the electromagnetic field data can be used to determine whether electromagnetic fields in the areas of the flight path can adversely affect flight safety, for instance, by interfering with navigation systems, sensors, components, etc. onboard the aerial vehicle 101.); and
generating a flight plan for the airborne vehicle based on the HIRF stand-off zones within the region of operation (VELASTRI, at least one para. 0068; “In the example of FIG. 4, the mapping platform 117 receives or generates a flight path 401 that passes over the geographic areas corresponding to hexagons 403a-403f. For each area of interest of the corresponding hexagons 403a-403f, the mapping platform 117 aggregates the risk factors for a time associated with the flight path 401 (e.g., time that the aerial vehicle 101 is expected to pass over each of the hexagons 403a-4030. The mapping platform 117 then generates a virtual obstacle object representation of each hexagon 403a-403f according to the embodiments described herein. As shown, a virtual obstacle object representation 405 is generated for the area corresponding to hexagon 403a.”).
Regarding claim 12, VELASTRI teaches (Previously Presented) The computer-readable storage medium of claim 11, including directing the airborne vehicle to operate along a flight path defined by the flight plan (VELASTRI, at least one para. 0072; “FIG. 5 is a diagram of a trip planning user interface (UI) 501 for visualizing risk levels for aerial vehicle flights, according to one embodiment. In this example, the UI 501 includes a first element 503 for presenting information on how the risk levels were calculated. For example, the element 503 lists the risk factors (e.g., population, electric fields, loss of GPS, and high winds) that were considered in computing the risk levels represented on the element 505. Element 505b displays the rendered virtual obstacle objects determined over areas around or near the flight path 507. In this way, the flight path 507 can be drawn or computed to avoid passing through any of the virtual obstacle objects to minimize safety risks over the flight path 507. For example, the mapping platform 117 can initiate a generation of a different flight path based on determining that a risk level associated with a current or potential flight path is above a risk threshold.”).
Regarding claim 13, VELASTRI teaches (Previously Presented) The computer-readable storage medium of claim 11, wherein the region of operation of the airborne vehicle is identified based on predetermined distance from at least one of an origin location or a destination location for the airborne vehicle (VELASTRI, at least one para. 0052; “In step 303, the data ingestion module 203 retrieves risk-related related data on the flight path and areas within a threshold distance of the flight path for the given time specified in step 301. [0051] The flight path, for instance, can be determined using any routing engine known in the art based on an origin and destination specified by a pilot of the aerial vehicle 101 for the route at a given time (e.g., expected start time of the route).”).
Regarding claim 14, VELASTRI teaches (Previously Presented) The computer-readable storage medium of claim 11, wherein the radiation tolerance level of the airborne vehicle is selected based on an amount of navigable airspace within the region of operation for the airborne vehicle relative to the HIRF sources (VELASTRI, at least one para. 0072; “FIG. 5 is a diagram of a trip planning user interface (UI) 501 for visualizing risk levels for aerial vehicle flights, according to one embodiment. In this example, the UI 501 includes a first element 503 for presenting information on how the risk levels were calculated. For example, the element 503 lists the risk factors (e.g., population, electric fields, loss of GPS, and high winds) that were considered in computing the risk levels represented on the element 505. Element 505b displays the rendered virtual obstacle objects determined over areas around or near the flight path 507. In this way, the flight path 507 can be drawn or computed to avoid passing through any of the virtual obstacle objects to minimize safety risks over the flight path 507. For example, the mapping platform 117 can initiate a generation of a different flight path based on determining that a risk level associated with a current or potential flight path is above a risk threshold.”).
Regarding claim 15, VELASTRI teaches (Previously Presented) The computer-readable storage medium of claim 11, wherein each HIRF stand-off zone defines a geographical space in the region of operation having a potential radiated field intensity that is equal to or greater than the radiation tolerance level of the airborne vehicle (VELASTRI, at least one para. 0044; “the height and/or any other dimension of the virtual obstacle object can be scaled to be proportional to the calculated risk level for the area. In one embodiment, the height of the virtual obstacle object is a function of time and hence creates a user interface with a dynamic landscape of multiple virtual obstacle objects that go “up and down” over the course of the day or other period of time to reflect the frequently changing patterns of the risk function aggregating the risk factors for an area of interest such as but not limited to population density and/or any other risk parameters that can affect the safety of operating the aerial vehicle 101.”, since the flight path is created to avoid the virtual obstacles, it is inherent that the defined geographical spaces are equal or greater than the radiation tolerance level of the airborne vehicle.).
Regarding claim 16, VELASTRI teaches (Previously Presented) The computer-readable storage medium of claim 11, wherein generating the flight plan for the airborne vehicle based on the HIRF stand-off zone includes plotting a flight plan for the airborne vehicle that avoids intersecting the HIRF stand-off zones (VELASTRI, at least one para. 0072; “FIG. 5 is a diagram of a trip planning user interface (UI) 501 for visualizing risk levels for aerial vehicle flights, according to one embodiment. In this example, the UI 501 includes a first element 503 for presenting information on how the risk levels were calculated. For example, the element 503 lists the risk factors (e.g., population, electric fields, loss of GPS, and high winds) that were considered in computing the risk levels represented on the element 505. Element 505b displays the rendered virtual obstacle objects determined over areas around or near the flight path 507. In this way, the flight path 507 can be drawn or computed to avoid passing through any of the virtual obstacle objects to minimize safety risks over the flight path 507. For example, the mapping platform 117 can initiate a generation of a different flight path based on determining that a risk level associated with a current or potential flight path is above a risk threshold.”).
Regarding claim 17, VELASTRI teaches (Previously Presented) The computer-readable storage medium of claim 11, wherein identifying the HIRF sources includes obtaining transmitter location and characteristics from regulatory databases (VELASTRI, at least one para. 0060; “Electromagnetic field data can also be sensed used using sensors located on aerial vehicles 101, in the infrastructure (e.g., smart city infrastructure), and/or from any other sensor in the area of interest. In addition or alternatively, historical or previously sensed electromagnetic data that has been stored for the areas of interest along the flight path can be stored and retrieved from the geographic database 121.”).
Regarding claim 19, VELASTRI teaches (Currently Amended) The method of claim 1, wherein the generated flight plan lowers a cost and weight of a design and build stage for the airborne vehicle (VELASTRI, at least one para. 0063; “In some embodiments, various elements or portions of elements of system 100 may be integrated with each other, for example, or may be integrated onto a single printed circuit board (PCB) to reduce system complexity, manufacturing costs, power requirements, coordinate frame errors, and/or timing errors between the various sensor measurements.”).
Regarding claim 20, VELASTRI teaches (Currently Amended) A method of operating an airborne vehicle along a flight plan (VELASTRI, at least one para. 0003; “According to one embodiment, a method comprises determining a flight path for an aerial vehicle. The method also comprises calculating at least one risk area along the flight path based on risk-related data associated with the flight path (e.g., data on population density, electromagnetic fields, absence of location signals such as Global Positioning System (GPS) signals, weather, network coverage, aviation related data, etc.). ”), the method comprising:
identifying a region of operation of the airborne vehicle (VELASTRI, at least one para. 0051; “In step 301, the routing module 201 determines a flight path or potential flight path of an aerial vehicle 101. The flight path, for instance, can be determined using any routing engine known in the art based on an origin and destination specified by a pilot of the aerial vehicle 101 for the route at a given time (e.g., expected start time of the route).”);
identifying high-intensity radiated field (HIRF) sources associated with the region of operation of the airborne vehicle (VELASTRI, at least one para. 0052-0058; “In step 303, the data ingestion module 203 retrieves risk-related related data on the flight path and areas within a threshold distance of the flight path for the given time specified in step 301. By way of example, the risk-related data includes but is not limited to at least one of any the following risk factors: Population density data; Electromagnetic field data; Data on an absence of location signals; Weather data; Network coverage data; and Aviation related data.””);
obtaining a radiation tolerance level for the airborne vehicle (VELASTRI, at least one para. 0060; “In one embodiment, the electromagnetic field data can be used to determine whether electromagnetic fields in the areas of the flight path can adversely affect flight safety, for instance, by interfering with navigation systems, sensors, components, etc. onboard the aerial vehicle 101.”, in other words, to determine the adverse interference between the onboard components of the aerial vehicle and the electromagnetic fields, the radiation tolerance level for aerial vehicle has to be obtained. Therefore, it is inherent that the radiation tolerance level for aerial vehicle is obtained.);
determining a HIRF stand-off zone for each of the HIRF sources (VELASTRI, at least one para. 0066; “After aggregating risk-related data for a flight path or potential flight path of interest, the risk module 205 can calculate at least one risk area along the flight path based on risk-related data associated with the flight path. The at least one risk area can correspond to a geographic area to be represented by a virtual obstacle object. For example, the risk area can be defined as a hexagon or any other shape (e.g., rectilinear polygon, Voronoi shape, etc.) forming the footprint of the virtual obstacle object. In one embodiment, the size of the hexagon or shape (e.g., the extent of the geographic area represented by or corresponding to the shape) can be based on the density of the available risk-related data for a given area. For example, given thresholds on confidence levels and/or number of observations of risk-related data for the area, the risk module 205 determines the size of the hexagon or other shape (e.g., by picking the minimum possible size of the hexagon or shape that meets the confidence or observation thresholds).”) based on the radiation tolerance level of the airborne vehicle (VELASTRI, at least one para. 0102; “The learner module then compares the predicted matching probability of the predicted population density feature to the ground truth population density data for each input feature set in the ground truth training data set. The learner module then computes an accuracy of the predictions for the initial set of model parameters.”) and a potential radiated field generated by the HIRF source (VELASTRI, at least one para. 0066; “After aggregating risk-related data for a flight path or potential flight path of interest, the risk module 205 can calculate at least one risk area along the flight path based on risk-related data associated with the flight path. The at least one risk area can correspond to a geographic area to be represented by a virtual obstacle object. For example, the risk area can be defined as a hexagon or any other shape (e.g., rectilinear polygon, Voronoi shape, etc.) forming the footprint of the virtual obstacle object. In one embodiment, the size of the hexagon or shape (e.g., the extent of the geographic area represented by or corresponding to the shape) can be based on the density of the available risk-related data for a given area. For example, given thresholds on confidence levels and/or number of observations of risk-related data for the area, the risk module 205 determines the size of the hexagon or other shape (e.g., by picking the minimum possible size of the hexagon or shape that meets the confidence or observation thresholds).”);
generating the flight plan for the airborne vehicle based on the HIRF stand-off zones within the region of operation (VELASTRI, at least one para. 0068; “In the example of FIG. 4, the mapping platform 117 receives or generates a flight path 401 that passes over the geographic areas corresponding to hexagons 403a-403f. For each area of interest of the corresponding hexagons 403a-403f, the mapping platform 117 aggregates the risk factors for a time associated with the flight path 401 (e.g., time that the aerial vehicle 101 is expected to pass over each of the hexagons 403a-4030. The mapping platform 117 then generates a virtual obstacle object representation of each hexagon 403a-403f according to the embodiments described herein. As shown, a virtual obstacle object representation 405 is generated for the area corresponding to hexagon 403a.”), wherein the HIRF stand-off zones define regions of prohibited operation for the airborne vehicle based on exceedance of the radiation tolerance level (VELASTRI, at least one para. 0035; “Other issues occurring in specific areas of the flight path or potential flight path of the aerial vehicle 101 can also increase potential risks associated with operating the aerial vehicle 101. For example, flying over highly populated areas can increase the probability that a crash of the aerial vehicle 101 will result in casualties. While pilots (e.g., human or machine) of aerial vehicles 101 can typically see potential physical obstacles (e.g., buildings or structures that can increase collision risks) and avoid them, it is much more difficult for pilots to determine or perceive risks such as flying over populated areas that may also significantly increase safety risks. Other similar risks include but are not limited to flying through strong electromagnetic fields that can affect flight sensors, flying in areas with limited GPS or other location signals, flying in areas with adverse weather conditions, and/or the like.”, wherein similar to building or structures prohibits operations of the airborne vehicle, electromagnetic fields also prevent operation of the airborne vehicle because the electromagnetic fields can adversely affect the sensors of the airborne vehicle); and
directing the airborne vehicle to operate along a flight path defined by the flight plan (VELASTRI, at least one para. 0072; “FIG. 5 is a diagram of a trip planning user interface (UI) 501 for visualizing risk levels for aerial vehicle flights, according to one embodiment. In this example, the UI 501 includes a first element 503 for presenting information on how the risk levels were calculated. For example, the element 503 lists the risk factors (e.g., population, electric fields, loss of GPS, and high winds) that were considered in computing the risk levels represented on the element 505. Element 505b displays the rendered virtual obstacle objects determined over areas around or near the flight path 507. In this way, the flight path 507 can be drawn or computed to avoid passing through any of the virtual obstacle objects to minimize safety risks over the flight path 507. For example, the mapping platform 117 can initiate a generation of a different flight path based on determining that a risk level associated with a current or potential flight path is above a risk threshold.”).
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.
Claim(s) 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over VELASTRI (US 20200286388 A1) as applied to claim 1 and 11 above, respectively, and further in view of Liang (US 20220268952 A1).
Regarding claim 8, VELASTRI teaches (Currently Amended) The method of claim 1, wherein determining the HIRF stand-off zone (VELASTRI, at least one para. 0052-54; “In step 303, the data ingestion module 203 retrieves risk-related related data on the flight path and areas within a threshold distance of the flight path for the given time specified in step 301. By way of example, the risk-related data includes but is not limited to at least one of any the following risk factors: Population density data; Electromagnetic field data;”) includes identifying a potential radiation intensity level and direction of radiation of the HIRF source and a direction of radiation of each of the HIRF sources, wherein the HIRF stand-off zone is limited to an angular corresponding to the direction of radiation of the HIRF source.
VELASTRI does not explicitly teach includes identifying a potential radiation intensity level and direction of radiation of the HIRF source and a direction of radiation of each of the HIRF sources, wherein the HIRF stand-off zone is limited to an angular corresponding to the direction of radiation of the HIRF source.
Liang, in the same field of endeavor (Liang, at least one para. 0001; “The present invention relates generally to radiation source localization and, more particularly, to systems and methods for radiation source localization using a portable directional radiation detector that can be coupled to a mobile sensor platform.”) teaches includes identifying a potential radiation intensity level and direction of radiation of the HIRF source and a direction of radiation of each of the HIRF sources (Liang, at least one para. 0159; “In the embodiment shown in FIG. 13B, display view 1504 includes many of the same features of display view 1500, and additionally includes dose rate or ionizing radiation intensity and/or radiological source concentration/likelihood boundaries 1546-1550 bounding respective dose rate or ionizing radiation intensity and/or radiological source concentration/likelihood segments 1540-1544, as shown.”) and (Liang, at least one para. 0046; “In some embodiments, sensor payload 140 may be implemented with a second or additional imaging modules similar to imaging module 142, for example, that may include detector elements configured to detect other electromagnetic spectrums, such as visible light, ultraviolet, and/or other electromagnetic spectrums or subsets of such spectrums.”), wherein the HIRF stand-off zone is limited to an angular corresponding to the direction of radiation of the HIRF source (Liang, at least one para. 0114; “Embodiments described herein have been tested using two NaI based detectors and a planar collimating panel to estimate the direction of a radiation source. Simulations were performed to obtain the counts distributed and energy deposited in the two detectors as a function of the source angle.”).
VELASTRI and Liang are both considered to be analogous to the claimed invention because both of them are in the same field as generating a flight path to avoid electromagnetic interference as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the HIRF sources of the VELASTRI with teaching of Liang. One of the ordinary skill in the art would have been motivated to make this modification because all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention. Furthermore, the heatmap 1530 make sure that the aircraft is able to generate the flight path without interfering with any of the segments 1540-1544 (Liang; 0159).
Regarding claim 18, VELASTRI teaches (Currently Amended) The computer-readable storage medium of claim 11, wherein determining the HIRF stand-off zone (VELASTRI, at least one para. 0052-54; “In step 303, the data ingestion module 203 retrieves risk-related related data on the flight path and areas within a threshold distance of the flight path for the given time specified in step 301. By way of example, the risk-related data includes but is not limited to at least one of any the following risk factors: Population density data; Electromagnetic field data;”) , and calculating the potential radiated field as a function of transmitter power, frequency, (VELASTRI, at least one para. 0060; “Electromagnetic field data can also be sensed used using sensors located on aerial vehicles 101, in the infrastructure (e.g., smart city infrastructure), and/or from any other sensor in the area of interest.”, wherein teach the HIRF source as a function of power and frequency through the different sensors).
VELASTRI does not explicitly teach includes identifying a potential radiation intensity level and direction of radiation of the HIRF source and a direction of radiation of each of the HIRF sources,
and calculating the potential radiated field as a function of antenna directionality, and distance from the HIRF source.
Liang, in the same field of endeavor (Liang, at least one para. 0001; “The present invention relates generally to radiation source localization and, more particularly, to systems and methods for radiation source localization using a portable directional radiation detector that can be coupled to a mobile sensor platform.”) teaches includes identifying a potential radiation intensity level and direction of radiation of the HIRF source and a direction of radiation of each of the HIRF sources (Liang, at least one para. 0159; “In the embodiment shown in FIG. 13B, display view 1504 includes many of the same features of display view 1500, and additionally includes dose rate or ionizing radiation intensity and/or radiological source concentration/likelihood boundaries 1546-1550 bounding respective dose rate or ionizing radiation intensity and/or radiological source concentration/likelihood segments 1540-1544, as shown.”) and (Liang, at least one para. 0046; “In some embodiments, sensor payload 140 may be implemented with a second or additional imaging modules similar to imaging module 142, for example, that may include detector elements configured to detect other electromagnetic spectrums, such as visible light, ultraviolet, and/or other electromagnetic spectrums or subsets of such spectrums.”),
and calculating the potential radiated field as a function of antenna directionality, and distance from the HIRF source (Liang, at least one para. 0114; “Embodiments described herein have been tested using two NaI based detectors and a planar collimating panel to estimate the direction of a radiation source. Simulations were performed to obtain the counts distributed and energy deposited in the two detectors as a function of the source angle.”, wherein teaches the HIRF source as a function of antenna directionality) and (Liang, at least one para. 0135; “For example, when a gamma radiation detector is moved to approach and pass a source (e.g., along a linear path), the greatest signal strength/largest count flux will be present when the distance to the radiation detector is minimized (when a perpendicular can be drawn from the path of the detector's movement to the source)”, wherein teaches the HIRF source as a function of distance).
VELASTRI and Liang are both considered to be analogous to the claimed invention because both of them are in the same field as generating a flight path to avoid electromagnetic interference as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the HIRF sources of the VELASTRI with teaching of Liang. One of the ordinary skill in the art would have been motivated to make this modification because all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention. Furthermore, the heatmap 1530 make sure that the aircraft is able to generate the flight path without interfering with any of the segments 1540-1544 (Liang; 0159).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to UPUL P CHANDRASIRI whose telephone number is (703)756-5823. The examiner can normally be reached M-F 8.30 am to 5pm.
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/U.P.C./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665
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