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 .
Status of Claims
Claims 1-6 are currently pending in application 18/745,429.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 6/17/2024 and 5/7/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
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.
Claims 1-6 are rejected under 35 U.S.C. 103(a) as being unpatentable over Liu et al. (LIU, Z. [et al.]: A Review of Progress and Applications of Pulsed Doppler Wind LiDARs. Remote Sensing, Vol. 11, 2019, No. 21 MDPI [online], DOI: 10.3390/rs11212522, In: MDPI.)(From IDS submitted by Application 5/7/2026, Document #2) in view of Stock-Williams et al. (STOCK-WILLIAMS, C. [et al]: Wind field reconstruction from lidar measurements at high- frequency using machine learning. Journal of Physics: Conference Series, Vol. 1102, October 2018, No. 1, IOP Publishing [online], DOI: 10.1088/1742-6596/1102/1/012003, In: IOP.) (From IDS submitted by Application 5/7/2026, Document #3).
As per independent Claims 1-3, Liu discloses a laser radar device comprising a signal processor [a signal processing device that acquires and processes information from a laser radar device, the signal processing device comprising; a signal processing device that acquires and processes information from a plurality of laser radar devices, the signal processing device comprising] (See at least Abstract, Section 1, "Doppler LIDAR technique and data processing algorithms will provide accurate measurements with high Spatial and temporal resolutions under different environmental conditions. ... In the coherent LiDAR systems, frequency shifts are measured by comparing the return signal to a reference signal; while in the direct detection systems, the return signal is filtered or resolved into its spectral components to obtain frequency shifts."; and Section 2.2, 4: "LiDAR beam can also be swept through a slice, as shown in Figure 3c. This pattern is known as the range height indicator (RHI) and is commonly used in dual or multiple LiDAR systems [48-51]"), wherein
the signal processor includes a wind field calculator, a blind region extractor, and a learning algorithm calculator (See at least Fig. 1, Section 1-2.3, The fact that a CDW-LiDAR system is equipped with appropriate units to carry out its intended measurement is automatically inferred by the person skilled in the art.),
the wind field calculator obtains a Doppler frequency from a peak position of a spectrum at each of observation points and calculates a wind velocity [vector] (See at least Section 2.1. "A Doppler wind LiDAR estimates the component of wind velocity projected onto the laser beam propagation direction, named line-of-sight velocity or the radial velocity v_R, based on the measurement of Doppler wavelength shift distribution. ... The return signal with frequency fO+Δf is mixed with the reference light in the interferometer. Then the combined single field is focused onto a detector. Using the Doppler shift Δf obtained from the mixed signal by fast Fourier transform or other methods [28,29]."),
the blind region extractor extracts a blind region by referring to a geometrical relationship including a laser irradiation direction and disposition of a structure (See at least Section 2.1, 2.4, Fig. 2, "As shown in Figure 2, if the LiDAR is placed at the origin of the Cartesian coordinate system, the beam orientation is defined in terms of the azimuth angle θ and the elevation angle Ф. ... Site or platform conditions should also be taken into consideration during LiDAR measurements. If the view of a site is limited by obstacles such as buildings and trees, a larger φ should be adopted to decrease the impacts of the obstacles.”; Liu explicitly teaches that obstacles (structures) restrict the measurement volume depending on the beam direction Ф, and that signals caused by obstacles must be excluded from useful utilization. This is functionally identical to the extraction of a blind region: the blind region is geometrically defined by the direction Ф and the position of the structure.).
Liu fails to expressly disclose the learning algorithm calculator includes a learned artificial intelligence, and estimates [calculates an estimated value of] a wind velocity value [the wind velocity vector] in the blind region.
However, the analogous art of Stock-Williams discloses the learning algorithm calculator includes a learned artificial intelligence, and estimates [calculates an estimated value of] a wind velocity value [the wind velocity vector] in the blind region (See at least Abstract, Section 2.2, 3, "A novel Machine Learning method is developed here, based on Gaussian Process regression, to remove these assumptions when producing full 3D wind fields from Lidar measurements. [...] The method also infers data during measurement gaps, offering the potential for 100% data availability. ... This means that one can express a prior probability distribution over functions, which can be updated with data collected to provide predictions at any other point in the input space. Finally, evidence is shown in Figure 7 that GP inference during times of low data availability can result in reliable wind speed estimates." The ML model taught in Stock-Williams is trained using LiDAR measurement data and makes predictions about wind speed fields in blind regions where no measurement data is available.)
Therefore, before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have included the learning algorithm calculator includes a learned artificial intelligence, and estimates [calculates an estimated value of] a wind velocity value [the wind velocity vector] in the blind region, as disclosed by Stock-Williams in the system disclosed by Liu, for the advantage of providing a device that acquires and processes information from a plurality of laser radar devices, with the ability to increase device effectiveness and efficiency by closing the measurement gaps in the wind field measurements resulting from obstacles in the observation area and providing a consistent wind speed field even in these blind regions (See KSR [127 S Ct. at 1739] “The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
As per Claim 4 (2), Liu and Stock-Williams disclose a data table including at least seven types of fields of a length in a range direction, an azimuth of a beam, an elevation angle of the beam, a time, a wind velocity, a flag as to whether or not it is a structure, and a flag as to whether or not it is a blind region (The features according to claim 4 merely describe a trivial software data structure for storing the relevant measurement variables. The first five parameters (radial distance, azimuth, elevation, time, measured radial wind speed) form the universal, physically mandatory raw data set of every scanning Doppler lidar. The addition of two logical Boolean variables (flags for 'structure yes/no' and 'blind region yes/no') is the mandatory, purely technical implementation of the features of claim 1/2.).
As per Claim 5 (3), Liu and Stock-Williams disclose a data table including at least four types of fields of geographic coordinates for specifying an observation point, a wind velocity vector, a flag as to whether or not it is a structure, and a flag as to whether or not it is a blind region (Analogous to claim 4, this is the mandatory data structure for a multi-lidar system. If beams from several LiDAR systems are combined, a coordinate transformation into a uniform coordinate system or geographical grid must be carried out. The storage of coordinates, the resulting vector and the two status flags is a completely standard software architecture for mapping wind fields.).
As per Claim 6 (2), Liu and Stock-Williams disclose a structure position estimator to estimate a position of a structure by referring to an assumption that a shape of the structure viewed from above is a quadrangle (Since a LiDAR physically only detects the front side facing an obstacle, the system must make an assumption about the concealed depth and basic shape of the object in order to calculate the blind region behind it. When evaluating point clouds, the absolute standard procedure is to provide the object points recognized by clustering with an enveloping rectangle (2D bounding box / minimum bounding rectangle). Such a bounding box corresponds exactly to the claimed assumption that the shape of the structure "seen from above is a quadrilateral. The skilled person who is faced with the object of calculating the geometric shadow cast from the measured point cloud of an obstacle inevitably uses this standard tool of bounding box generation to approximate the base area of the obstacle. The implementation of this generally known measure customary in the art in the system of claim 2 or 3 cannot alone constitute an inventive step.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the PTO-892 Notice of References Cited. The Examiner suggests the applicant review this document before submitting any amendments.
Kotake et al. (US 20170307648 A1) – Kotake describes a laser radar device that “includes a multi-wavelength light oscillator to generate a plurality of light beams with different wavelengths, a plurality of modulation units to modulate each of the plurality of light beams with a modulation frequency altered according to a corresponding line of sight of emission, a transmitting/receiving optical system to emit each of the light beams modulated by the modulation units in a corresponding line of sight, and receive reflected light beams, an optical receiver to perform heterodyne detection by using the generated light beams and the received light beams corresponding to the generated light beams, and detect beat signals in the respective lines of sight, and a signal analyzing unit to calculate a value of Doppler wind speed in the respective lines of sight from the respective beat signals, and calculate a value of three-dimensional wind speed using the values of Doppler wind speed” (Abstract).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN P OUELLETTE whose telephone number is (571)272-6807. The examiner can normally be reached on M-F 8am-6pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynda C Jasmin, can be reached at telephone number (571) 272-6782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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August 11, 2026
/JONATHAN P OUELLETTE/Primary Examiner, Art Unit 3629
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