Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This office action is in response to Applicant Amendments and Remarks filed on 11/26/2025, for application number 18/220,310 filed on 07/11/2023, in which claims 1, 2, 6-8, 12, and 13 were previously presented for examination.
Claims 1 and 8 are amended.
Claims 6-7 and 12-13 are canceled.
Claims 1, 2, and 8 are currently pending in this application.
Response to Arguments
Applicant Amendments and Remarks filed on 11/26/2025 in response to the Non-Final office action mailed on 08/28/2025 have been fully considered and are addressed as follows:
Regarding the Claim Rejections under 35 USC § 101: With respect to the previous claim rejections under 35 U.S.C. § 101, Applicant has amended the independent claims and these amendments have changed the scope of the original application. Therefore, the Office has supplied new grounds for rejection attached below in the Non-Final office action and therefore the prior arguments are considered moot.
Regarding Applicant’s arguments associated with Step 2A Prong 1, Applicant alleges that “Applicant's claim 1 is not "directed to" a mental process, but instead is directed to providing a flight path to an aircraft to configure the aircraft to follow the flight path for improving the operation of the aircraft. This is not a mental process and cannot be performed merely in the human mind or with a pen and paper” and “A human mind is not able to mentally configure or control an aircraft. Further, the human mind is not equipped to perform the machine learning functions included in Applicant's claims 1 and 8, including determining a respective local wind condition vector for each respective space region in the three-dimensional grid lattice based at least on the learned feature group information and current wind-condition data” (Applicant Amendments and Remarks filed on 11/26/2025 at pg. 10-11). Examiner disagrees.
Claim 1 and claim 8 similarly recite “a path planning system that performs optimization calculation of a flight path of the aircraft and provides the flight path to the aircraft to configure the aircraft to fly from the departure point to the destination according to the flight path” (emphasis added). The limitation is a process that under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components.
Under the broadest reasonable interpretation, the limitation does not require control of the aircraft. The limitation only requires providing the flight path to the aircraft, and the flight path may allow the aircraft flying according to the flight path. The Office’s interpretation of the limitation aligns with the disclosure in the specification. The specification states “according to the information processing system of the present invention, for each local space region of a flight path in which a small aircraft can fly during flight from a departure point to a destination, it is possible to evaluate flight difficulty and economic efficiency as the flight path and generate the evaluation result as three-dimensional information. Then, based on the three-dimensional information, it is possible to easily finalize a flight path that is excellent in flight difficulty and economic efficiency” (emphasis added) in page 14, lines 2-11. The specification does not disclose controlling of the aircraft to fly according to the flight path. Further, providing the flight path does not necessarily mean transmitting or communicating the flight path to the aircraft. Providing the flight path may mean making the flight path available to the aircraft. Moreover, the specification is silent on whether the flight path is actually transmitted or communicated to the aircraft.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Therefore, Applicant’s arguments are not persuasive.
Regarding Applicant’s arguments associated with Step 2A Prong 2, Applicant alleges that “Applicant's claim 1, when considered as a whole, integrates the alleged abstract idea into a practical application at least because the claim recites elements that improve the function of an aircraft by determining an optimal flight path for the aircraft based on wind conditions, determining an optimal flight path for the aircraft, and providing the flight path to configure the aircraft to fly from the departure to the destination according to the flight path” and “Applicant's claim 1 recites features related to a technical solution to a technical problem of determining an optimal path for an aircraft in various different wind conditions. In view of the foregoing, it is apparent that Applicant's claim 1 recites elements that reflect an improvement to the operation of another technology or technical field, namely, improving the operation safety and efficiency of an aircraft in view of estimated wind conditions between a departure point and destination of the aircraft. As such, Applicant's claim 1 is directed to patent-eligible subject matter at least because the claim includes additional elements that apply the alleged abstract idea in a meaningful way beyond generally linking the use of the abstract idea to a particular technological environment. Consequently, Applicant's independent claim 1 is not "directed to" an abstract idea because the abstract idea is integrated into a practical application” (Applicant Amendments and Remarks filed on 11/26/2025 at pg. 11-12). Examiner disagrees.
Claim 1 recites additional elements – “generates a wind-condition data group of each space region” “learns feature group information” “generates three-dimensional information,” “transmits the three-dimensional information,” and “provides the flight path to the aircraft.” The wind-condition acquisition unit, the wind-condition learning unit, and the three-dimensional information generation unit and the path planning system perform the function of the additional elements are recited at a high-level of generality such that the additional elements amount to no more than insignificant extra solution activity of data gathering, outputting, and transmitting. Accordingly, this additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Regarding Applicant’s arguments associated with Step 2B, Applicant alleges that “Applicant's claim 1, as presented herein, includes specific recitations directed to other than what is well-understood, routine, and conventional in the field” and “The additional elements of Applicant's claim 1 are significant (particularly, but not exclusively those emphasized above), at least because the claim includes a specific technique for improving the operation of an aircraft” (Applicant Amendments and Remarks filed on 11/26/2025 at pg. 12 and 15). Examiner disagrees.
The claim as a whole, under the broadest reasonable interpretation which aligns with the disclosure in the specification, may provide the flight path. However, an aircraft is not required to be controlled, influenced, or affected by the provided flight path. Further, providing the flight path does not necessarily mean transmitting or communicating the flight path to the aircraft. Providing the flight path may mean making the flight path available to the aircraft. Moreover, the specification is silent on whether the flight path is actually transmitted or communicated to the aircraft. Therefore, the alleged technical improvements are not warranted. Therefore, the claim as a whole does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
For at least the foregoing reasons, and the rejections outlined below, the rejections under 35 USC § 101 are maintained.
Regarding the Claim Rejections under 35 USC § 103: With respect to the previous claim rejections under 35 U.S.C. § 103, Applicant has amended the independent claims and these amendments have changed the scope of the original application. Therefore, the Office has supplied new grounds for rejection attached below in the Non-Final office action and therefore the prior arguments are considered moot.
Regarding Applicant’s arguments for the amended claims 1 and 8, Applicant alleges that “Imaki does not teach or suggest determining a respective local wind condition vector for each respective space region in a three-dimensional grid lattice of space regions based at least on the learned feature group information and current wind-condition data. Rather Imaki is entirely silent with respect to these elements” (Remarks filed on 11/26/2025 at pg. 20) and further alleges that “Hendrian, NPL-1, and Gu do not compensate for the shortcomings in Imaki pointed out above” (Remarks filed on 11/26/2025 at pg. 21). Examiner disagrees.
The limitation “a respective local wind condition vector for each respective space region in the three-dimensional grid lattice” is disclosed in Hendrian et al. as stated in the Non-Final office action mailed on 08/28/2025 at pg. 11-12. Hendrian et al. discloses how wind-condition information is estimated based on “wind-condition data”, while Imaki teaches how “wind-condition data” is collected and “feature group information” is learned to generate wind vectors (see Non-Final office action mailed on 08/28/2025 at pg. 11-16 and 23-27). Moreover, Imaki teaches dividing environmental space into a wire-frame mesh to form subregions of the environment space. The wind conditions are expressed by assigning vectors representing the wind direction and wind speed to each small region. The vectors are generated based on wind condition predicted by using trained AI (see Imaki et al. at para. [0025] and [0027]). Therefore, the limitation, determining a respective local wind condition vector for each respective space region in a three-dimensional grid lattice of space regions based at least on the learned feature group information and current wind-condition data, is taught by Hendrian et al. in view of Imaki et al.
Therefore, Applicant’s arguments are not persuasive.
For at least the foregoing reasons, and the rejections outlined below, the prior art rejections are maintained.
FINAL OFFICE ACTION
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.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 2, and 8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1 and 8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding eligibility step 1, the claimed invention of claims 1 and 8 fall into at least one of the enumerated categories of apparatuses and processes. Therefore, claims 1 and 8 pass step 1.
Proceeding to eligibility step 2A, the claimed invention of claims 1 and 8 are directed to a judicial exception, such as an abstract idea. If a claim limitation under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the mental process grouping of an abstract idea.
Claim 1 recites “estimates wind-condition information” and “evaluates flight difficulty and economic efficiency of an aircraft.” Claim 8 similarly recites the limitations. The limitations of “estimates wind-condition information” and “evaluates flight difficulty and economic efficiency of an aircraft” as drafted, are processes that under their broadest reasonable interpretation, cover performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “a wind-condition estimation unit” and “an evaluation unit,” nothing in the claim element precludes the processes from practically being performed in the mind. For example, but for the “a wind-condition estimation unit” and “an evaluation unit” languages, “estimates wind-condition information” and “evaluates flight difficulty and economic efficiency of an aircraft” in the context of this claim encompasses the user manually performs the processes of making a prediction about wind-condition and an evaluation about flight difficulty and economic efficiency of an aircraft.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Further, this judicial exception is not integrated into a practical application. In particular, claim 1 recites additional elements – ““generates a wind-condition data group of each space region” “learns feature group information” “generates three-dimensional information,” “transmits the three-dimensional information,” and “provides the flight path to the aircraft.” Claim 8 similarly recites the elements. The wind-condition acquisition unit, the wind-condition learning unit, and the three-dimensional information generation unit and the path planning system perform the function of the additional elements are recited at a high-level of generality such that the additional elements amount to no more than insignificant extra solution activity of data gathering, outputting, and transmitting.
Claim 1 further recites “a path planning system that performs optimization calculation of a flight path of the aircraft and provides the flight path to the aircraft to configure the aircraft to fly from the departure point to the destination according to the flight path” (emphasis added). Claim 8 similarly recites the limitation. The limitation is a process that under their broadest reasonable interpretation, does not integrate the judicial exception into a practical application.
Under the broadest reasonable interpretation, the limitation does not require control of the aircraft. The limitation only requires providing the flight path to the aircraft, and providing the flight path that may allow the aircraft flying according to the flight path. The Office’s interpretation of the limitation aligns with the disclosure in the specification. The specification states “according to the information processing system of the present invention, for each local space region of a flight path in which a small aircraft can fly during flight from a departure point to a destination, it is possible to evaluate flight difficulty and economic efficiency as the flight path and generate the evaluation result as three-dimensional information. Then, based on the three-dimensional information, it is possible to easily finalize a flight path that is excellent in flight difficulty and economic efficiency” (emphasis added) in page 14, lines 2-11. The specification does not disclose controlling of the aircraft to fly according to the flight path. Further, providing the flight path does not necessarily mean transmitting or communicating the flight path to the aircraft. Moreover, the specification is silent on whether the flight path is actually transmitted or communicated to the aircraft.
Accordingly, this additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Proceeding to eligibility step 2B, claims 1 and 8 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a generic computer component to perform estimating of wind-condition information, evaluating of flight difficulty and economic efficiency of an aircraft, generating of three-dimensional information, transmitting of the three-dimensional information, and providing of a flight path amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Therefore, claims 1 and 8 are not patent eligible.
Dependent claim 2, when analyzed as a whole, is held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claim is not directed to an abstract idea. The additional elements, if any, in the dependent claim is not sufficient to amount to significantly more than the judicial exception for the same reasons as with claim 1.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Hendrian et al. (US 2019/0147753 A1, which is found in the IDS submission on 07/11/2023) in view of Imaki et al. (WO 2023/152862 A1) further in view of NPL-1 (R. McAllister and J. Sheppard, "Deep learning for wind vector determination," 2017 IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, USA, 2017, pp. 1-8, doi: 10.1109/SSCI.2017.8280836) and Gu (US 2019/0318636 A1).
Regarding claim 1, Hendrian et al. discloses an information processing system comprising:
a wind-condition acquisition unit that generates a wind-condition data group of each space region (Hendrian et al. at para. [0057]: “During operation of system 102, real-time wind measurements/reports, such as wind vectors 111, are first matched with their respective coarsely-grained wind forecast (from e.g. the National Weather Service)”);
a wind-condition estimation unit that estimates wind-condition information in a plurality of space regions between a departure point and a destination of an aircraft (Hendrian et al. at para. [0028]: “region 104 is at least one of suburban region 105 or urban region 107” “region 104 is a city 103. City 103 includes at least one of suburban region 105 or urban region 107”; para. [0029]: “Wind speed calculator 108 is configured to determine wind vectors 111 within region 104 using measurements 112 from plurality of aerial vehicles 109”; para. [0035]: “wind speed calculator 108 associates wind measurements 128 with locations 130 and altitudes 132 of plurality of aerial vehicles 109 to form wind vectors 111”; FIG. 4 and para. [0078]: “In view 400, plurality of points 210 are replaced by wind vectors 402. Each of wind vectors 402 represents wind vectors determined by a wind speed calculator, such as wind speed calculator 108 of FIG. 1”), wherein a three-dimensional grid lattice (Hendrian et al. at para. [0030]: “Wind speed calculator 108 is configured to determine wind vectors 111 within region 104 using measurements 112 from plurality of aerial vehicles 109”; para. [0045]: “Three-dimensional wind map 162 includes interpolated wind vectors 164. Interpolated wind vectors 164 are associated with set grid points within region 104. Interpolated wind vectors 164 are on three-dimensional grid 166. Interpolated wind vectors 164 are associated with set grid points of three-dimensional grid 166 within region 104. Three dimensional wind map 162 of region 104 is generated including interpolated wind vectors 164 based on wind vectors 111”; para. [0046]: “Three-dimensional grid 166 is a grid in both lateral and vertical dimensions. Three-dimensional grid 166 explicitly defines locations by latitude/longitude/altitude. Locations 130 of wind vectors 111 are scattered throughout region 104 based on assigned operations and flight paths of plurality of aerial vehicles 109. By tailoring wind vectors 111 to a grid, such as three-dimensional grid 166, wind vectors 111 may be used in training using model training system 158. The tailoring process may be described as interpolation between wind vectors 111 so that interpolated wind vectors 164 at each grid point of three-dimensional grid 166 are calculated.” “wind vectors, such as interpolated wind vectors 164, on a lateral scale, as well as at different altitudes, are calculated at each grid point.”; The three-dimensional grid includes the grid points associated with three-dimensional coordinate points; para. [0057]: “During operation of system 102, real-time wind measurements/reports, such as wind vectors 111, are first matched with their respective coarsely-grained wind forecast (from e.g. the National Weather Service)”);
an evaluation unit that evaluates flight difficulty and economic efficiency of the aircraft based on the estimated wind-condition information (Hendrian et al. at para. [0052]: “flight plan 114 may take into account at least one of fuel efficiency, turbulence, maximum altitude, maximum speed of unmanned aerial vehicle 116, dimensions of unmanned aerial vehicle 116, order parameters, cargo type, or any other desirable parameters”; para. [0053]: “flight plan generator 110 is configured to determine maximum acceptable turbulence 178 for cargo 174 of unmanned aerial vehicle 116 and plan flight plan 114 such that unmanned aerial vehicle 116 is projected to encounter turbulence below maximum acceptable turbulence 178”), wherein evaluating the economic efficiency of the aircraft for traversing a possible flight path within a space region of the plurality of space regions includes (Hendrian et al. at para. [0091]: “path 910 is created based on wind vectors 912 in region 902. In some illustrative examples, wind vectors 912 are determined in real-time”; para. [0094]: “Path 910 may be generated to increase fuel efficiency of unmanned aerial vehicle 900”);
wherein the evaluation unit generates the flight difficulty and the economic efficiency of each respective space region of the plurality of space regions in the three-dimensional grid lattice as three-dimensional evaluation information (Hendrian et al. at para. [0089]: “Each of interpolated wind vectors 802 represents wind vectors determined by a three-dimensional model, such as three-dimensional model 156 of FIG. 1. Each of interpolated wind vectors 802 includes a wind speed and a wind direction. Each of interpolated wind vectors 802 is associated with a point of set grid points 602”; para. [0094]: “Path 910 may be generated to decrease turbulence experienced by unmanned aerial vehicle 900. Path 910 may be generated to increase fuel efficiency of unmanned aerial vehicle 900”); and
a three-dimensional information generation unit that generates three-dimensional information by adding the three-dimensional evaluation information to a three-dimensional map space including position data, shape data, and attribute data (Hendrian et al. at FIG. 4 and para. [0078]: “In view 400, plurality of points 210 are replaced by wind vectors 402. Each of wind vectors 402 represents wind vectors determined by a wind speed calculator, such as wind speed calculator 108 of FIG. 1”; para. [0089]: “Each of interpolated wind vectors 802 represents wind vectors determined by a three-dimensional model, such as three-dimensional model 156 of FIG. 1”),
wherein the three-dimensional information generation unit transmits the three-dimensional information to a path planning system that performs optimization calculation of a flight path of the aircraft and provides the flight path to the aircraft to configure the aircraft to fly from the departure point to the destination according to the flight path (Hendrian et al. at para. [0090]: “Path 908 is an initial path. Path 908 may be determined using any desirable method. In some illustrative examples, path 908 may be the fastest path without winds. In some illustrative examples, path 908 may be the most direct path”; para. [0091]: “Path 910 is a modified flight path, such as modified flight plan 172 of FIG. 1. In this illustrative example, path 910 is created based on wind vectors 912 in region 902”).
However, Hendrian et al. does not explicitly state:
a wind-condition acquisition unit that generates a wind-condition data group of each space region based on past wind-condition data, terrain data, and obstacle data;
a wind-condition learning unit that learns feature group information based on the wind-condition data group;
a three-dimensional grid lattice forming a plurality of polyhedrons having at least a lower layer of polyhedrons and an upper layer of polyhedrons is determined in a three-dimensional space, and each polyhedron corresponding to a different respective space region of the plurality of space regions,
wherein the wind condition estimation unit estimates the wind condition information by determining a respective local wind condition vector for each respective space region in the three-dimensional grid lattice based at least on the learned feature group information and current wind-condition data acquired from at least one of a weather sensor or a weather forecast organization;
comparing the respective local wind condition vector in the respective space region in the three-dimensional grid lattice with a threshold value for aircraft performance of the aircraft.
In the same field of endeavor, Imaki et al. teaches:
a wind-condition acquisition unit that generates a wind-condition data group of each space region based on past wind-condition data, terrain data, and obstacle data (Imaki et al. at para. [0025]: “Each of these divided subspaces is called a subregion of the environment space. The wind conditions in the environmental space are expressed by assigning vectors representing the wind direction and wind speed to each small region” and “The grid interval for dividing the environmental space may be changed depending on the shape of the terrain and structures”; para. [0028]: “In the learning data set, there are two possible methods: using wind altitude distribution model values as boundary conditions for the domain in which the simulation is performed; and using past meteorological data. Also, a method of using observation data from the wind condition measuring instrument 20 as the boundary condition can be considered”);
a wind-condition learning unit that learns feature group information based on the wind-condition data group (Imaki et al. at para. [0043]: “The trained AI possessed by the calculation unit 34 of the wind condition prediction device 30 in embodiment 2 has been trained with the explanatory variable being the wind conditions around the entire boundary between the environmental space and the outside world, and the objective variable being the wind condition distribution in the environmental space”);
wherein the wind condition estimation unit estimates the wind condition information by determining a respective local wind condition vector for each respective space region in the three-dimensional grid lattice based at least on the learned feature group information and current wind-condition data acquired from at least one of a weather sensor or a weather forecast organization (Imaki et al. at para. [0024]: “The number of wind condition measuring instruments 20 and their respective installation positions and orientations are adjusted in the learning phase to match those used in the inference phase”; para. [0025]: “Each of these divided subspaces is called a subregion of the environment space. The wind conditions in the environmental space are expressed by assigning vectors representing the wind direction and wind speed to each small region” and “The grid interval for dividing the environmental space may be changed depending on the shape of the terrain and structures”; para. [0030]: “The predicted wind conditions in the environmental space may be displayed as wind direction and speed values, a heat map, and an arrow feather”; para. [0043]: “Having such trained AI means that by inputting statistical wind condition data at the location where the wind condition measuring instrument 20 is installed, it is possible to predict the statistical wind condition distribution in the environmental space”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Hendrian et al. by utilizing the feature group information of Imaki et al. with a reasonable expectation of success. The motivation to modify the wind-condition estimation unit of Hendrian et al. in view of Imaki et al. is to enable precise prediction of wind conditions using machine learning (see Imaki et al. at para. [0005]).
However, Hendrian et al. in view of Imaki et al. does not explicitly state:
a three-dimensional grid lattice forming a plurality of polyhedrons having at least a lower layer of polyhedrons and an upper layer of polyhedrons is determined in a three-dimensional space, and each polyhedron corresponding to a different respective space region of the plurality of space regions,
comparing the respective local wind condition vector in the respective space region in the three-dimensional grid lattice with a threshold value for aircraft performance of the aircraft.
In the same field of endeavor, NPL-1 teaches a three-dimensional grid lattice forming a plurality of polyhedrons having at least a lower layer of polyhedrons and an upper layer of polyhedrons is determined in a three-dimensional space, and each polyhedron corresponding to a different respective space region of the plurality of space regions (NPL-1 at Figures 2-3 and pg. 2: “1) Discrete Global Grid Systems: NWP uses the Discrete Global Gridding Systems (DGGS) as a method of geographical binning. A DGGS is a system of adjacent polygons that cover the entire planet [10]. Such systems allow the collection of point observations that occur in the same vicinity so that each point collected in a polygon is seen as representative of that polygon. The size of these polygons determines the resolution of the weather information system”; pg. 2-3: “Each cell in an area of study is represented by Ch, a, t. The symbol h represents the height relative to the center cell, or cell of interest. The heights will be labeled “high,” representing the cells above the center cell level, “middle” (mid) for those at the same level, and “low” for cells at below the center cell”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the information processing system of Hendrian et al. in view of Imaki et al. by adding the three-dimensional grid lattice forming the plurality of polyhedrons as taught by NPL-1 with a reasonable expectation of success. The motivation to modify the information processing system of Hendrian et al. in view of Imaki et al. further in view of NPL-1 is to provide enhanced determination of wind vectors (see NPL-1 at Abstract).
However, Hendrian et al. in view of Imaki et al. further in view of NPL-1 does not explicitly state comparing the respective local wind condition vector in the respective space region in the three-dimensional grid lattice with a threshold value for aircraft performance of the aircraft.
In the same field of endeavor, Gu teaches comparing the respective local wind condition vector in the respective space region in the three-dimensional grid lattice with a threshold value for aircraft performance of the aircraft (Gu at para. [0062]: “The cost calculation unit 813 calculates the flight cost according to Equation (3), for example, when an instruction to generate a flight route minimizing the battery consumption at the time of the flight of the unmanned aerial vehicle 100 is specified as an optimization item” “V1 represents a ground flying speed of the unmanned aerial vehicle 100 (i.e., a moving speed of the unmanned aerial vehicle 100). V2 represents a wind speed vector, and f(V2) represents a function outputting a value of the power consumption of the unmanned aerial vehicle 100 according to the wind speed”; para. [0234]: “the communication terminal 80 detects the presence or absence of a change of equal to or greater than a predetermined threshold value (e.g., a predetermined angle) in the wind direction around the unmanned aerial vehicle 100”; para. [0235]: “in the case where the wind direction around the unmanned aerial vehicle 100 changes by a predetermined angle or more, the communication terminal 80 determines that the flight route generated in step S6 is not appropriate, and calculates the flight cost again based on the reacquired environment information (e.g., the wind direction and the wind speed) (S4)”; para. [0272]: “the mobile platform (e.g., the communication terminal 80) according to the modified example detects the presence or absence of a change equal to or greater than a predetermined threshold value in the environment information (e.g., wind direction and wind speed) with in the partial flight range. When the unmanned aerial vehicle 100 detects a change equal to or greater than a predetermined threshold value in the environment information wind direction or wind speed) while flying within any one of the partial flight ranges, the mobile platform calculates, based on the flight cost in the next partial flight range according to the environment information and the flight direction of the unmanned aerial vehicle 100, the flight direction”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the information processing system of Hendrian et al. in view of Imaki et al. further in view of NPL-1 by adding comparing the respective local wind condition vector as taught by Gu with a reasonable expectation of success. The motivation to modify the information processing system of Hendrian et al. in view of Imaki et al. further in view of NPL-1 and Gu is to adaptively generate a flight route according to the environmental changes such as wind direction and speed by recalculating the flight cost (Gu at para. [0235]).
Office Note: The office interprets the limitations as below:
“lattice” is interpreted as “a regular geometrical arrangement of points or objects over an area or in space” (see “Lattice.” Merriam-Webster.com Dictionary, Merriam-Webster, https://www.merriam-webster.com/dictionary/lattice. Accessed 25 Aug. 2025.).
“Flight difficulty” is interpreted as “the quality or state of being hard to fly” (see “Difficulty.” Merriam-Webster.com Dictionary, Merriam-Webster, https://www.merriam-webster.com/dictionary/difficulty. Accessed 1 May. 2025.).
“Economic efficiency” is interpreted as “the quality or degree of being efficient relating to production, distribution, and consumption of goods and services” (see “Efficiency.” Merriam-Webster.com Dictionary, Merriam-Webster, https://www.merriam-webster.com/dictionary/efficiency. Accessed 1 May. 2025.; “Economic.” Merriam-Webster.com Dictionary, Merriam-Webster, https://www.merriam-webster.com/dictionary/economic. Accessed 1 May. 2025.).
“Wind vector” is interpreted as “vector indicating the direction and speed of wind.”
Regarding claim 2, Hendrian et al. in view of Imaki et al. further in view of NPL-1 and Gu teaches the information processing system according to claim 1.
Hendrian et al. further discloses wherein the evaluation unit further
evaluates the flight difficulty based in part on a comparison between the aircraft performance, and a wind speed and a wind direction obtained from the estimated wind-condition information (Hendrian et al. at para. [0052]: “flight plan 114 may take into account at least one of fuel efficiency, turbulence, maximum altitude, maximum speed of unmanned aerial vehicle 116, dimensions of unmanned aerial vehicle 116, order parameters, cargo type, or any other desirable parameters”; para. [0110]: “Method 1200 generates a three-dimensional wind map of the region including interpolated wind vectors based on the wind vectors (operation 1204)”; para. [0111]: “flying the aerial vehicle based on the three-dimensional wind prediction for the region at the second time comprises modifying a flight plan that the aerial vehicle is actively flying (operation 1216)”; para. [0115]: “The illustrative examples provide a means to establish a four-dimensional weather model. The four-dimensional weather model of the illustrative examples is able to predict winds in lateral and vertical terms. In some illustrative examples, the four-dimensional weather model is also able to predict disturbances or turbulence in lateral and vertical terms”).
Regarding claim 8, Hendrian et al. discloses an information processing method comprising:
generating a wind-condition data group of each space region (Hendrian et al. at para. [0057]: “During operation of system 102, real-time wind measurements/reports, such as wind vectors 111, are first matched with their respective coarsely-grained wind forecast (from e.g. the National Weather Service)”);
estimating, by a processor configured by an executable program, wind-condition information in a plurality of space regions located between a departure point and a destination of an aircraft (Hendrian et al. at para. [0028]: “region 104 is at least one of suburban region 105 or urban region 107” “region 104 is a city 103. City 103 includes at least one of suburban region 105 or urban region 107”; para. [0029]: “Wind speed calculator 108 is configured to determine wind vectors 111 within region 104 using measurements 112 from plurality of aerial vehicles 109”; para. [0035]: “wind speed calculator 108 associates wind measurements 128 with locations 130 and altitudes 132 of plurality of aerial vehicles 109 to form wind vectors 111”; FIG. 4 and para. [0078]: “In view 400, plurality of points 210 are replaced by wind vectors 402. Each of wind vectors 402 represents wind vectors determined by a wind speed calculator, such as wind speed calculator 108 of FIG. 1”), wherein a three-dimensional grid lattice (Hendrian et al. at para. [0030]: “Wind speed calculator 108 is configured to determine wind vectors 111 within region 104 using measurements 112 from plurality of aerial vehicles 109”; para. [0045]: “Three-dimensional wind map 162 includes interpolated wind vectors 164. Interpolated wind vectors 164 are associated with set grid points within region 104. Interpolated wind vectors 164 are on three-dimensional grid 166. Interpolated wind vectors 164 are associated with set grid points of three-dimensional grid 166 within region 104. Three dimensional wind map 162 of region 104 is generated including interpolated wind vectors 164 based on wind vectors 111”; para. [0046]: “Three-dimensional grid 166 is a grid in both lateral and vertical dimensions. Three-dimensional grid 166 explicitly defines locations by latitude/longitude/altitude. Locations 130 of wind vectors 111 are scattered throughout region 104 based on assigned operations and flight paths of plurality of aerial vehicles 109. By tailoring wind vectors 111 to a grid, such as three-dimensional grid 166, wind vectors 111 may be used in training using model training system 158. The tailoring process may be described as interpolation between wind vectors 111 so that interpolated wind vectors 164 at each grid point of three-dimensional grid 166 are calculated.” “wind vectors, such as interpolated wind vectors 164, on a lateral scale, as well as at different altitudes, are calculated at each grid point.”; The three-dimensional grid includes the grid points associated with three-dimensional coordinate points; para. [0057]: “During operation of system 102, real-time wind measurements/reports, such as wind vectors 111, are first matched with their respective coarsely-grained wind forecast (from e.g. the National Weather Service)”);
evaluating flight difficulty and economic efficiency of an aircraft based on the estimated wind-condition information (Hendrian et al. at para. [0052]: “flight plan 114 may take into account at least one of fuel efficiency, turbulence, maximum altitude, maximum speed of unmanned aerial vehicle 116, dimensions of unmanned aerial vehicle 116, order parameters, cargo type, or any other desirable parameters”; para. [0053]: “flight plan generator 110 is configured to determine maximum acceptable turbulence 178 for cargo 174 of unmanned aerial vehicle 116 and plan flight plan 114 such that unmanned aerial vehicle 116 is projected to encounter turbulence below maximum acceptable turbulence 178”), wherein evaluating the economic efficiency of the aircraft for traversing a possible flight path within a space region of the plurality of space regions includes (Hendrian et al. at para. [0091]: “path 910 is created based on wind vectors 912 in region 902. In some illustrative examples, wind vectors 912 are determined in real-time”; para. [0094]: “Path 910 may be generated to increase fuel efficiency of unmanned aerial vehicle 900”);
generating the flight difficulty and the economic efficiency of each respective space region of the plurality of space regions in the three-dimensional grid lattice as three-dimensional evaluation information (Hendrian et al. at para. [0089]: “Each of interpolated wind vectors 802 represents wind vectors determined by a three-dimensional model, such as three-dimensional model 156 of FIG. 1. Each of interpolated wind vectors 802 includes a wind speed and a wind direction. Each of interpolated wind vectors 802 is associated with a point of set grid points 602”; para. [0094]: “Path 910 may be generated to decrease turbulence experienced by unmanned aerial vehicle 900. Path 910 may be generated to increase fuel efficiency of unmanned aerial vehicle 900”);
generating three-dimensional information by adding the three-dimensional evaluation information to a three-dimensional map space including position data, shape data, and attribute data (Hendrian et al. at FIG. 4 and para. [0078]: “In view 400, plurality of points 210 are replaced by wind vectors 402. Each of wind vectors 402 represents wind vectors determined by a wind speed calculator, such as wind speed calculator 108 of FIG. 1”; para. [0089]: “Each of interpolated wind vectors 802 represents wind vectors determined by a three-dimensional model, such as three-dimensional model 156 of FIG. 1”); and
transmitting the three-dimensional information to a path planning system that performs optimization calculation of a flight path of the aircraft and provides the flight path to the aircraft to configure the aircraft to fly from the departure point to the destination according to the flight path (Hendrian et al. at para. [0090]: “Path 908 is an initial path. Path 908 may be determined using any desirable method. In some illustrative examples, path 908 may be the fastest path without winds. In some illustrative examples, path 908 may be the most direct path”; para. [0091]: “Path 910 is a modified flight path, such as modified flight plan 172 of FIG. 1. In this illustrative example, path 910 is created based on wind vectors 912 in region 902”).
However, Hendrian et al. does not explicitly state:
generating a wind-condition data group of each space region based on past wind-condition data, terrain data, and obstacle data;
learning feature group information based on the wind-condition data group;
wherein a three-dimensional grid lattice forming a plurality of polyhedrons having at least a lower layer of polyhedrons and an upper layer of polyhedrons is determined in a three-dimensional space between the departure point and the destination, each polyhedron corresponding to a different respective space region of the plurality of space regions,
wherein estimating, by the processor, the wind condition information includes determining a respective local wind condition vector for each respective space region in the three-dimensional grid lattice based at least on the learned feature group information and current wind-condition data acquired from at least one of a weather sensor or a weather forecast organization,
comparing the respective local wind condition vector in the respective space region in the three-dimensional grid lattice with a threshold value for aircraft performance of the aircraft.
In the same field of endeavor, Imaki et al. teaches:
generating a wind-condition data group of each space region based on past wind-condition data, terrain data, and obstacle data (Imaki et al. at para. [0025]: “Each of these divided subspaces is called a subregion of the environment space. The wind conditions in the environmental space are expressed by assigning vectors representing the wind direction and wind speed to each small region” and “The grid interval for dividing the environmental space may be changed depending on the shape of the terrain and structures”; para. [0028]: “In the learning data set, there are two possible methods: using wind altitude distribution model values as boundary conditions for the domain in which the simulation is performed; and using past meteorological data. Also, a method of using observation data from the wind condition measuring instrument 20 as the boundary condition can be considered”);
learning feature group information based on the wind-condition data group (Imaki et al. at para. [0043]: “The trained AI possessed by the calculation unit 34 of the wind condition prediction device 30 in embodiment 2 has been trained with the explanatory variable being the wind conditions around the entire boundary between the environmental space and the outside world, and the objective variable being the wind condition distribution in the environmental space”);
wherein estimating, by the processor, the wind condition information includes determining a respective local wind condition vector for each respective space region in the three-dimensional grid lattice based at least on the learned feature group information and current wind-condition data acquired from at least one of a weather sensor or a weather forecast organization (Imaki et al. at para. [0025]: “Each of these divided subspaces is called a subregion of the environment space. The wind conditions in the environmental space are expressed by assigning vectors representing the wind direction and wind speed to each small region” and “The grid interval for dividing the environmental space may be changed depending on the shape of the terrain and structures”; para. [0030]: “The predicted wind conditions in the environmental space may be displayed as wind direction and speed values, a heat map, and an arrow feather”; para. [0043]: “Having such trained AI means that by inputting statistical wind condition data at the location where the wind condition measuring instrument 20 is installed, it is possible to predict the statistical wind condition distribution in the environmental space”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Hendrian et al. by utilizing the feature group information of Imaki et al. with a reasonable expectation of success. The motivation to modify the method of Hendrian et al. in view of Imaki et al. is to enable precise prediction of wind conditions using machine learning (see Imaki et al. at para. [0005]).
However, Hendrian et al. in view of Imaki et al. does not explicitly state:
wherein a three-dimensional grid lattice forming a plurality of polyhedrons having at least a lower layer of polyhedrons and an upper layer of polyhedrons is determined in a three-dimensional space between the departure point and the destination, each polyhedron corresponding to a different respective space region of the plurality of space regions,
comparing the respective local wind condition vector in the respective space region in the three-dimensional grid lattice with a threshold value for aircraft performance of the aircraft.
In the same field of endeavor, NPL-1 teaches a three-dimensional grid lattice forming a plurality of polyhedrons having at least a lower layer of polyhedrons and an upper layer of polyhedrons is determined in a three-dimensional space between the departure point and the destination, each polyhedron corresponding to a different respective space region of the plurality of space regions (NPL-1 at Figures 2-3 and pg. 2: “1) Discrete Global Grid Systems: NWP uses the Discrete Global Gridding Systems (DGGS) as a method of geographical binning. A DGGS is a system of adjacent polygons that cover the entire planet [10]. Such systems allow the collection of point observations that occur in the same vicinity so that each point collected in a polygon is seen as representative of that polygon. The size of these polygons determines the resolution of the weather information system”; pg. 2-3: “Each cell in an area of study is represented by Ch, a, t. The symbol h represents the height relative to the center cell, or cell of interest. The heights will be labeled “high,” representing the cells above the center cell level, “middle” (mid) for those at the same level, and “low” for cells at below the center cell”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Hendrian et al. in view of Imaki et al. by adding the three-dimensional grid lattice forming the plurality of polyhedrons as taught by NPL-1 with a reasonable expectation of success. The motivation to modify the method of Hendrian et al. in view of Imaki et al. further in view of NPL-1 is to provide enhanced determination of wind vectors (see NPL-1 at Abstract).
However, Hendrian et al. in view of Imaki et al. further in view of NPL-1 does not explicitly state comparing the respective local wind condition vector in the respective space region in the three-dimensional grid lattice with a threshold value for aircraft performance of the aircraft.
In the same field of endeavor, Gu teaches comparing the respective local wind condition vector in the respective space region in the three-dimensional grid lattice with a threshold value for aircraft performance of the aircraft (Gu at para. [0062]: “The cost calculation unit 813 calculates the flight cost according to Equation (3), for example, when an instruction to generate a flight route minimizing the battery consumption at the time of the flight of the unmanned aerial vehicle 100 is specified as an optimization item” “V1 represents a ground flying speed of the unmanned aerial vehicle 100 (i.e., a moving speed of the unmanned aerial vehicle 100). V2 represents a wind speed vector, and f(V2) represents a function outputting a value of the power consumption of the unmanned aerial vehicle 100 according to the wind speed”; para. [0234]: “the communication terminal 80 detects the presence or absence of a change of equal to or greater than a predetermined threshold value (e.g., a predetermined angle) in the wind direction around the unmanned aerial vehicle 100”; para. [0235]: “in the case where the wind direction around the unmanned aerial vehicle 100 changes by a predetermined angle or more, the communication terminal 80 determines that the flight route generated in step S6 is not appropriate, and calculates the flight cost again based on the reacquired environment information (e.g., the wind direction and the wind speed) (S4)”; para. [0272]: “the mobile platform (e.g., the communication terminal 80) according to the modified example detects the presence or absence of a change equal to or greater than a predetermined threshold value in the environment information (e.g., wind direction and wind speed) with in the partial flight range. When the unmanned aerial vehicle 100 detects a change equal to or greater than a predetermined threshold value in the environment information wind direction or wind speed) while flying within any one of the partial flight ranges, the mobile platform calculates, based on the flight cost in the next partial flight range according to the environment information and the flight direction of the unmanned aerial vehicle 100, the flight direction”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Hendrian et al. in view of Imaki et al. further in view of NPL-1 by adding comparing the respective local wind condition vector as taught by Gu with a reasonable expectation of success. The motivation to modify the method of Hendrian et al. in view of Imaki et al. further in view of NPL-1 and Gu is to adaptively generate a flight route according to the environmental changes such as wind direction and speed by recalculating the flight cost (Gu at para. [0235]).
Office Note: The office interprets the limitations as below:
“lattice” is interpreted as “a regular geometrical arrangement of points or objects over an area or in space” (see “Lattice.” Merriam-Webster.com Dictionary, Merriam-Webster, https://www.merriam-webster.com/dictionary/lattice. Accessed 25 Aug. 2025.).
“Flight difficulty” is interpreted as “the quality or state of being hard to fly” (see “Difficulty.” Merriam-Webster.com Dictionary, Merriam-Webster, https://www.merriam-webster.com/dictionary/difficulty. Accessed 1 May. 2025.).
“Economic efficiency” is interpreted as “the quality or degree of being efficient relating to production, distribution, and consumption of goods and services” (see “Efficiency.” Merriam-Webster.com Dictionary, Merriam-Webster, https://www.merriam-webster.com/dictionary/efficiency. Accessed 1 May. 2025.; “Economic.” Merriam-Webster.com Dictionary, Merriam-Webster, https://www.merriam-webster.com/dictionary/economic. Accessed 1 May. 2025.).
“Wind vector” is interpreted as “vector indicating the direction and speed of wind.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and can be found in the attached PTO-892 form.
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/JISUN CHOI/Examiner, Art Unit 3666
/SCOTT A BROWNE/Supervisory Patent Examiner, Art Unit 3666