DETAILED ACTION
Notice of Pre-AIA or AIA Status
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/16/26 has been entered.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1, 3-4, 6-7, 9, 13-15 and 17-22 are pending in the application. Claims 1, 3, 7, 9 13-15 and 17-20 have been amended, claims 2, 5, 8, 10-12 and 16 have been canceled, and claims 21-22 have been added.
Response to Arguments
Applicant's arguments filed 4/16/26 have been fully considered but are moot in view of new ground(s) of rejections.
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.
Claims 1, 3-4, 6-7, 9, 15, 17 and 19-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over NAKAGAWA et al. (US Publication 2021/0174080 A1, hereafter NAKAGAWA), in view of Wheeler et al. (US 11,308,595 B1, hereafter Wheeler).
As per claim 1, NAKAGAWA teaches the invention substantially as claimed including a data acquisition apparatus (FIG. 1-2), comprising at least one processor (FIG. 2 #11), wherein
the at least one processor acquires a plurality of first image data showing a plurality of first images obtained by photographing a target area at a plurality of points of time (FIG. 1 #20; FIG. 2 #15; FIG. 5 shows a wavelike path B1 that a drone 20 flies above a field A1. The drone 20 captures images region by region, each region being regarded as a first image. When the ratio of the overlapped area between current shooting range and previous shooting range is less than a threshold value, the drone performs next shooting (para. [0060]). That says the image corresponding to each region corresponds to a time point. Therefore a plurality of first images captured at a plurality of points of time.);
the at least one processor individually calculates vegetation indexes at the plurality of points of time in the target area by using the plurality of first image data (FIG. 6 shows a pixel-wise NDVI index map comprising NDVI index at each pixel in each region. That says NDVI index is calculated individually for each point of time; para. [0066]-[0071]);
the at least one processor determines whether statistics of the vegetation indexes at the plurality of points of time satisfies a predetermined evaluation criterion (FIG. 7 shows a NDVI index map in units of regions, the value in each region representing an average value of pixels in the region, and the pattern in each region reflecting the NDVI index value ranges (para. [0074]). Note average pixel value in a region representing a statistics of the region, which in turn represents a time point. NAKAGAWA then sets low index regions by comparing the values of each region with a threshold. FIG. 7 show an example of detecting low index regions E3-E6, E12-E13 and E27 when the values are less than a predetermined threshold (say 0.2); para. [0081] “In the example in FIG. 7, the NDVI average values of the sectional regions E3, E4, E5, E6, E12, E13, and E27 are less than 0.2, and therefore flight instruction unit 105 determines that low index regions are present in the crop region”); and
the at least one processor acquires second image data showing a second image obtained by photographing the target area at a higher resolution than the plurality of first images if the statistics of the vegetation indexes at the plurality of points of time do not satisfy the predetermined evaluation criterion (FIG. 10 S22-S26; para. [0078] “Specifically, if a portion regarding which the index calculated by index calculation unit 102 is less than a predetermined threshold (index threshold) (this portion is also referred to as a “low index region” in the following) is present in the crop region, flight instruction unit 105 instructs drone 20 to shoot the low index region while increasing the resolution of the crop image”; para. [0095] “If it is determined that a low index region is present in step S21 (YES), server apparatus 10 (flight instruction unit 105) transmits instruction data for making an instruction to shoot the low index region while increasing the resolution to drone 20 (step S22)”).
NAKAGAWA teaches every limitation recited in claim 1 except that the plurality of first image data is obtained from a satellite.
Wheeler in an analogous field discloses a method of detecting thermal anomaly in satellite imagery (Abstract; FIG. 8A; col. 17 ln 44-54). The method includes generating a composite image of an area using data associated with each of the “best” pixels from a sequence of images covering the area. The “best” pixel can be considered the one in a set (e.g., a time series) for which given spectral information associated with that pixel was least obscured by atmospheric obstruction. For example, NDVI value associated with a pixel can be used to select the best pixel in a time series (col. 17 ln 54-col. 18 ln 8).
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 teaching of NAKAGAWA to incorporate the teaching of Wheeler to obtain image data by photographing a target area from a satellite. Doing so would allow multispectral imagery from various sources to be available as recognized by Wheeler (col. 3 ln 50 -col. 4 ln 9).
As per claim 3, dependent upon claim 1, NAKAGAWA in view of Wheeler teaches the statistics of the vegetation indexes is a maximum value of the vegetation indexes at the plurality of points of time (Wheeler FIG. 10; col. 19 ln 36-63).
As per claim 4, dependent upon claim 1, NAKAGAWA in view of Wheeler further teaches outputting the second image data (NAKAGAWA The server apparatus shown in FIG. 2 comprises an “output apparatus 16”; FIG. 10 S23-S24).
Claim 6, dependent upon claim 3, is rejected as applied to claim 4 above.
As per claim 7, dependent upon claim 1, NAKAGAWA in view of Wheeler teaches the data acquisition apparatus according to claim 1, further comprising:
the at least one processor analyzes the second image data to generate analysis (NAKAGAWA FIG. 10 S26-S28; para. [0096]-[0097]); and
the at least one processor outputs a result of the analysis (NAKAGAWA The server apparatus shown in FIG. 2 comprises an “output apparatus 16”; FIG. 7-9; FIG. 10 S29; para. [0090]).
Claim 9, dependent upon claim 3, is rejected as applied to claim 7 above.
As per claim 15, dependent upon claim 1, NAKAGAWA in view of Wheeler further teaches the data acquisition apparatus according to claim 1, wherein the at least one processor designates a resolution of the second image in accordance with the vegetation indexes at the plurality of points of time (NAKAGAWA para. [0081] “Accordingly, making an instruction to perform a flight at a low altitude is also making an instruction to shoot the low index region while increasing the resolution”; para. [0095] “server apparatus 10 (flight instruction unit 105) transmits instruction data for making an instruction to shoot the low index region while increasing the resolution to drone 20 (step S22)”).
Claim 17, dependent upon claim 3, is rejected as applied to claim 15 above.
Claim 19, an independent method claim, recites steps corresponding to the steps recited in claim 1. Therefore, the recited elements of this claim are mapped to NAKAGAWA in view of Wheeler in the same manner as the corresponding steps in its corresponding apparatus claim, claim 1. Additionally, the rationale and motivation to combine NAKAGAWA and Wheeler presented in rejection of claim 1 apply to this claim.
Claim 20, an independent medium claim, recites steps corresponding to the steps recited in claim 1. Claim 20 further recites medium and computer. Therefore, the recited elements of this claim are mapped to NAKAGAWA in view of Wheeler in the same manner as the corresponding steps and computer components in its corresponding apparatus claim, claim 1. Additionally, the rationale and motivation to combine NAKAGAWA and Wheeler presented in rejection of claim 1 apply to this claim.
Claim 21, dependent upon claim 1, NAKAGAWA in view of Wheeler teaches the at least one processor sets the predetermined evaluation criterion based on a track record of the vegetation indexes at the plurality of points of time (NAKAGAWA FIG. 11-14; para. [0105]-[0116]).
Claim 22, dependent upon claim 3, is rejected as applied to claim 21 above.
Claims 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over NAKAGAWA in view of Wheeler, as applied above to claim 1, and further in view of Kelly et al. (US Publication 2024/0407288 A1, hereafter Kelly).
As per claim 13, NAKAGAWA in view of Wheeler does not teach the recited limitations.
Kelly in an analogous field discloses a system for monitoring vegetation health in a garden (Abstract). Specifically, vegetation index, such as NDVI, is calculated using captured image data (para. [0004]). The vegetation index is compared with a threshold to determine if a pruning activity should be performed. The threshold can be determined by various methods. For example, it is conceivable that the specific threshold value for the vegetation index for carrying out the pruning activity is determined by means of the machine learning method and/or the machine learning system (para. [0061]). Kelly further discloses environmental data acquisition unit (FIG. 1; para. [0012]) for acquiring environmental data associated with a garden area in which image data is acquired, such as a temperature, an air pressure, a humidity, a brightness, a light intensity, an irradiated energy etc. (FIG. 1; para. [0007]). Kelly further teaches training the machine learning system by using a plurality of values of the vegetation index from a plurality of different garden areas and/or from a plurality of different gardens, as well as a plurality of values for each environmental parameter (para. [0135]).
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 teaching of NAKAGAWA and Wheeler to incorporate the teaching of Kelly to set the evaluation criterion by using the environmental data. Doing so would allow measures to maintain the vegetation's health can be adapted precisely and specifically to the environment as recognized by Kelly (para.[0020]).
Claim 14, dependent upon claim 13, NAKAGAWA in view of Wheeler and Kelly teaches setting the evaluation criterion by using a learning model that is machine-learned such that the evaluation criterion is output in response to inputting the environmental data (Kelly in an analogous field discloses a system for monitoring vegetation health in a garden (Abstract). Specifically, vegetation index, such as NDVI, is calculated using captured image data (para. [0004]). The vegetation index is compared with a threshold to determine if a pruning activity should be performed. The threshold can be determined by various methods. For example, it is conceivable that the specific threshold value for the vegetation index for carrying out the pruning activity is determined by means of the machine learning method and/or the machine learning system (para. [0061]). Kelly further discloses environmental data acquisition unit (FIG. 1; para. [0012]) for acquiring environmental data associated with a garden area in which image data is acquired, such as a temperature, an air pressure, a humidity, a brightness, a light intensity, an irradiated energy etc. (FIG. 1; para. [0007]). Kelly further teaches training the machine learning system by using a plurality of values of the vegetation index from a plurality of different garden areas and/or from a plurality of different gardens, as well as a plurality of values for each environmental parameter (para. [0135])).
Claim 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over NAKAGAWA in view of Wheeler, as applied above to claim 15, and further in view of PRIBBLE et al. (US 2020/0125882 A1, hereafter PRIBBLE).
As per claim 18, NAKAGAWA in view of Wheeler teaches NDVI accuracy is associated with the difference between NDVI and a threshold, i.e., the larger the difference, the worse the accuracy (NAKAGAWA FIG. 12; FIG. 14). NAKAGAWA further set a threshold according to the accuracy (FIG. 13), i.e., the higher the accuracy, the smaller the threshold. That is to say, the larger the difference, the larger the threshold. NAKAGAWA in view of Wheeler, however, does not specify the relationship between the difference and the resolution.
PRIBBLE discloses a system for dynamically optimizing photo capture for multiple subjects (Abstract). Specifically, PRIBBLE teaches capturing a first image by determining a first resolution according to a first quality, and capturing a second image by determining a second resolution according to a second quality, the first quality and the second quality being different (FIG. 4; para. [0071]-[0080]). Note although PRIBBLE does not mention a difference, the quality value functions similarly by offsetting a fixed value (a threshold).
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 teaching of NAKAGAWA and Wheeler to incorporate the teaching of PRIBBLE to designate a resolution of the second image based on a difference between the vegetation index and the evaluation criterion. Doing so would allow photo capture for multiple subjects is dynamically optimized as recognized by PRIBBLE (para.[0058]).
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XUEMEI G CHEN whose telephone number is (571)270-3480. The examiner can normally be reached Monday-Friday 9am-6pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, John M Villecco can be reached at (571) 272-7319. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/XUEMEI G CHEN/Primary Examiner, Art Unit 2661
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