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 .
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
The disclosure is objected to because of the following informality:
On line 6 of paragraph [0007], “and a algorithm” should be “and an algorithm.”
Appropriate correction is required.
Claim Objections
Claim 1 is objected to because of the following informalities:
On line 5 of claim 1, “and a algorithm” should be “and an algorithm.”
On line 5 of claim 1, “algorithm learning AI” should be “algorithm learning Artificial Intelligence (AI).”
On line 5 of claim 1, wherein should be followed by a colon.
On line 6 of claim 1, “FFT” should be “Fast Fourier Transformation (FFT).”
On line 19 of claim 1, the comma after “and” should be removed.
Appropriate correction is required.
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.
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over Kotake et al. (US 2017/0307648) in view of Stock-Williams et al. (Wind Field Reconstruction from LiDAR Measurements at High-frequency Using Machine Learning- cited by Applicant). With respect to independent claim 1, Kotake discloses a laser radar device that scans a laser beam and measures a wind speed field of observation environment (Abstract and Fig. 3), further comprising a signal processor (Abstract, [0053], and Fig. 3; signal analyzing unit 12) including a spectrum conversion processor (Abstract, [0053]-[0055], and Fig. 3; fast Fourier transform (FFT) unit 122), an integration processor (Abstract, [0053], [0056], and Fig. 3; incoherent integration unit 123), and a wind speed field calculator (Abstract, [0053], [0057], and Fig. 3; line-of-sight wind velocity calculating unit 124), wherein:
the spectrum conversion processor performs Fast Fourier Transformation (FFT) processing on a beat signal that is a time-series digital signal, and generates spectrum data (Abstract, [0053]-[0055], and Fig. 3; fast Fourier transform (FFT) unit 122),
the integration processor performs integration processing on the spectrum data (Abstract, [0053], [0056], and Fig. 3; incoherent integration unit 123), and
the wind speed field calculator calculates the wind speed field by referring to information on data processed by the integration processor (Abstract, [0053], [0057], and Fig. 3; line-of-sight wind velocity calculating unit 124).
Regarding claim 1, Kotake discloses a laser radar device comprising a signal processor wherein the signal processor further comprises a wind velocity calculating unit which calculates 3D wind speed based on the Doppler speed calculated by the line-of-sight wind velocity calculating unit (Abstract, [0053], [0058], and Fig. 3; wind velocity calculating unit 125). However, Kotake fails to expressly disclose wherein the signal processor comprises an algorithm learning Artificial Intelligence (AI), as instantly claimed. Stock-Williams teaches a laser radar device comprising algorithm learning AI (Abstract). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to consider employing an algorithm learning AI for laser radar devices as taught by Stock-Williams in the laser radar device disclosed by Kotake since it amounts to nothing more than the combination of a known component of a laser radar device for measuring wind to a known laser radar device for measuring wind, as it has been taught "The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results... [W]hen a patent ‘simply arranges old elements with each performing the same function it had been known to perform’ and yields no more than one would expect from such an arrangement, the combination is obvious." KSR at 1395-66 (citing Sakraida v. AG Pro. Inc., 425 U.S. 273, 282 (1976)). Furthermore, the wind velocity calculating unit disclosed by Kotake depends upon line-of-sight data, which is limited by blind areas in the observation area. The algorithm learning AI taught by Stock-Williams solves this specific problem; wind velocity calculations are performed despite blind areas by interpolating the known, measured data (Abstract). As such, it would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to consider incorporating the algorithm learning AI taught by Stock-Williams in the laser radar device disclosed by Kotake since it would enable the laser radar device to measure wind speed even in blind areas.
Further regarding claim 1, the combination of Kotake and Stock-Williams teaches wherein: the algorithm learning AI includes a trained artificial intelligence, and interpolates an observation result by referring to information on the wind speed field (Abstract), the wind speed field calculator calculates information on a structure in the observation environment in addition to the information on the wind speed field (Abstract and Section 1), and
the algorithm learning AI interpolates an observation point, even if the point to be interpolated is a blind spot of the structure in the observation environment (Abstract).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Kotake et al. (US 2017/0307648- cited above) in view of Stock-Williams et al. (Wind Field Reconstruction from LiDAR Measurements at High-frequency Using Machine Learning- cited above), as evidenced by Wang et al. (US 2023/0135234).
With respect to depending claim 2, the combination of Kotake and Stock-Williams teaches a laser radar device comprises an algorithm learning AI which includes a trained artificial intelligence (Abstract). Although the combination fails to recite wherein the trained artificial intelligence is “a neural network,” as instantly claimed, the Office considers it standard and well-known in the art of algorithm learning AI that trained artificial intelligence may be a neural network, as evidenced by Wang (Abstract).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hu et al. (US 11,789,034) teaches a laser radar device for measuring wind speed comprising employing artificial intelligence such as a neural network.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AVI T. SKAIST whose telephone number is (571)272-9348. The examiner can normally be reached M-F 9:30-6.
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/AVI T SKAIST/Examiner, Art Unit 3674
/WILLIAM D HUTTON JR/Supervisory Patent Examiner, Art Unit 3674
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