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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 07/24/2026 has been entered.
Response to Arguments
In response to the amendments and remarks, filed 07/24/2026, the objection to the claims have been withdrawn.
Applicant's arguments, filed 07/24/2026, have been fully considered but they are not persuasive. Applicant argues, on page 100 of the remarks, that “
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Although Examiner agrees with Applicant’s summary of secondary reference Wang, Examiner disagrees with Applicant’s assertion that Wang fails to teach generate correction information based on a number of crops in each grid area of a plurality of grid areas, and the corrected NDVI value of the each grid area of the plurality of grid areas in the corrected NDVI image is based on the number of crops in the respective grid area; as described in the Final Office Action, dated 03/25/2026, spatial distribution maps for crops are heavily dependent on the number and diversity of crops present in each section of grid, and FIG. 5 of Wang shows the corrected values for each area of the grid in a remote sending image:
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Further, each “plot” is a section of the grid shown in the spectral remote-sensing image of FIG. 5 above that has a NDVI value calculated for it using the corrected grid boundaries that accurately reflects the spatial distribution of the target crops; the boundaries of each grid is corrected based on the correct spatial distribution that is based on the number of crops in each grid. Applicant fails to address this explanation and mapping in the remarks, and appears to make the argument that because Wang teaches a spatial distribution and not explicitly the number of cops in each section/grid in FIG. 5, then Wang fails to teach generate correction information based on a number of crops in each grid area of a plurality of grid areas; however, Wang teaches spatial distribution maps; Spatial distribution maps made from remote sensing images do depend on the number of crops in each grid section even if the number of crops is not explicitly discussed or calculated; the independent claims simply state the correction is done based on the number of crops and Wang does this indirectly through the spatial distribution map of the crops in the remote sending image.
Therefore, the rejection of the claims under 35 U.S.C. 103 is maintained.
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 1-3, 7-9, and 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over International Patent Application Publication No.: WO 2021002280 A1 (Ogawa) (paragraph citations and drawings throughout this Office Action will be in reference to U.S. equivalent Patent Application Publication No.: 2022/0299433), in view of Chinese Patent Publication No.: CN 101699315 A (Wang et al.) (hereinafter Wang).
Regarding claim 1, Ogawa teaches an information processing device comprising: (Ogawa, para. [0075]; FIG. 2: “An example of a sensing system using the macro measurement section 2 and the micro measurement section 3 as described above is a system that senses, for example, a vegetation state of a farm field 300 as illustrated in FIG. 2”;
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a central processing unit configured to: (Ogawa, para. [144]: “FIG. 7 illustrates a hardware configuration of the information processing apparatus 1. The information processing apparatus 1 includes a CPU (Central Processing Unit) 51, a ROM (Read Only Memory) 52, and a RAM (Random Access Memory) 53.”)
generate correction information in an area; and correct, based on the generated correction information, evaluation information associated with the area to generate a corrected normalized difference vegetation index (NDVI) image; the corrected NDVI image comprises the area; the corrected evaluation information comprises a corrected NDVI value of the area (Ogawa, para. [0083]; para. [0120]; para. [0222]-[0224]: “Additionally, the micro measurement sensor may be a multi spectrum camera performing imaging in a plurality of wavelength bands, capturing NIR images and R (red) images, for example, and being capable of calculating an NDVI (Normalized Difference Vegetation Index) on the basis of an image obtained, as long as the sensor has a device size at which the sensor can be operatively mounted in the flying body 200. The NDVI is an index indicating the distribution status and activity of vegetation.”; “SAVI (Soil-adjusted Vegetation Index)” is a vegetation index used to correct a fluctuation caused by the reflectance of the soil. When LAI is represented as “L,” L=0 (equal to NDVI) is used for a high LAI and L=1 is used for a low LAI SAVI=((NIR−RED)/(NIR+RED+L))×(1+L). However, an assumed value may need to be used as a value for the LAI, precluding precise correction in a case where the LAI may vary with location as in an agricultural field”; “In step S202, the micro measurement analysis calculation section 23 calculates the LAI, the average leaf angle, and the sun leaf ratio on a division unit basis. The LAI can be determined from a plant coverage. The plant coverage can be determined by dividing the number of pixels corresponding to an NDVI of a certain value or larger, by the number of measurement points (the number of pixels) in the relevant division unit. Note that the NDVI can be determined from the R image and the NIR image. That is, the value of the NDVI is determined by: NDVI=(NIR−R)/(NIR+R) where “R” is a reflectance of a visible region red, and “NIR” is a reflectance in a near infrared region. The NDVI has a numerical value normalized within a range of “−1” to “1,” and larger positive values of the NDVI indicate denser vegetation.”; SAVI corrects the NDVI (evaluation information) on the basis of LAI (average leaf angle) (correction information); Ogawa teaches using Soil-adjusted Vegetation Index (SAVI) to correct the Normalized Difference Vegetation Index (NDVI) using the average leaf angle (LAI).
Ogawa fails to teach
generate correction information based on a number of crops in each grid area of a plurality of grid areas; and
correct, based on the generated correction information, evaluation information associated with the each grid area, to generate a corrected normalized difference vegetation index (NDVI) image, wherein
the corrected NDVI image comprises the plurality of grid areas,
the corrected evaluation information comprises a corrected NDVI value of the each grid area, and
the corrected NDVI value of the each grid area of the plurality of grid areas in the corrected NDVI image is based on the number of crops in the respective grid area.
Wang teaches
generate correction information based on a number of crops in each grid area of a plurality of grid areas (Wang, para. [0073]-[0077]; para. [0088]-[0092]; FIG. 5: “A remote sensing image processing module, the remote sensing image processing module performs radiometric correction, atmospheric correction and geometric correction on the remote sensing image according to the obtained remote sensing image; A plot vector data processing module, the plot vector data processing module classifies the crops in the remote sensing images to obtain the spatial distribution map of the target crops; and transforms the raster classification results in the classified remote sensing images It is area vector data; then the plot boundaries of the spatial distribution map are corrected”; “A spatial distribution map processing submodule, the spatial distribution map processing submodule classifies the crops in the remote sensing image to obtain a spatial distribution map of the target crops; and converts the grid classification of the spatial distribution map into a planar vector data; The land use data processing submodule … A plot boundary processing sub-module, the plot boundary processing sub-module superimposes the surface vector data and the reference phase land use thematic data, and extracts the plot boundary after cutting through the vector layer Intersect algorithm; and using the satellite remote sensing images of this year, the land boundary correction is carried out through visual interpretation, and the final crop land boundary data are obtained … Among them, the "reference time-phase land use thematic data" refers to the farmland plot data obtained by using the high-resolution remote sensing images over the years or the historical land use data of the research area. Since the historical data may be slightly different from the latest land use conditions, using historical data combined with current data to correct the plot boundary can improve the accuracy of the plot boundary data.”; Fig. 3 is a spatial distribution diagram of the extracted target crops in a specific embodiment of the present invention; Fig. 4 is the plot image after converting the raster data in Fig. 3 into vector data; Figure 5 is the corrected plot image of Figure 4.”; spatial distribution maps for crops are heavily dependent on the number and diversity of crops present in each section of grid;
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correct, based on the generated correction information, evaluation information associated with the each grid area, to generate a corrected normalized difference vegetation index (NDVI) image, wherein the corrected NDVI image comprises the plurality of grid areas, the corrected evaluation information comprises a corrected NDVI value of the each grid area, and the corrected NDVI value of the each grid area of the plurality of grid areas in the corrected NDVI image is based on the number of crops in the respective grid area (Wang, para. [0078]-[0087]; para. [0094]-[0101]: “Vegetation index processing module, the vegetation parameter processing module calculates the vegetation index NDVI of the plot according to the spectral features in the plot in the remote sensing image:
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Among them, Rnir refers to the reflectance of the near-infrared band of the remote sensing image; Rred refers to the reflectance of the red band of the remote sensing image; The growth uniformity processing module, the growth uniformity processing module calculates the growth uniformity index GUI of the plot according to the vegetation index NDVI:
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NDVICV is the coefficient of variation of NDVI corresponding to each plot; NDVICVmin is the minimum value of the NDVI coefficient of variation of all plots in the same phase; NDVICVmax is the maximum value of the NDVI coefficient of variation of all plots in the same period. In the first preferred embodiment of the present invention, the plot of the target crop can be determined through the remote sensing image, and the vegetation index NDVI and the growth uniformity index GUI of the plot can be calculated through the spectral characteristics in the plot in the remote sensing image.”; “In the third preferred embodiment of the present invention, the spectral feature information in the plot is obtained by using the remote sensing image and the accurately corrected plot boundary, and the vegetation parameter NDVI is calculated according to the spectral feature information, which can improve the accuracy of NDVI.”); each “plot” is a section of the grid shown in the spectral remote-sensing image of FIG. 5 above that has a NDVI value calculated for it using the corrected grid boundaries that accurately reflects the spatial distribution of the target crops; as discussed above, the boundaries of each grid is corrected based on the correct spatial distribution that is based on the number of crops in each grid).
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify: 1) the step to generate correction information in an area, as taught by Ogawa, to be based on a number of crops in each grid area of a plurality of grid areas, as taught by Wang, 2) the step to correct, based on the generated correction information, evaluation information associated with the area to generate a corrected normalized difference vegetation index (NDVI) image, as taught by Ogawa, to be associated with the each grid area, as taught by Wang, 3) the corrected NDVI image comprising the area, as taught by Ogawa, to include the plurality of grid areas, and 4) the corrected evaluation information comprising the NDVI value of the area, as taught by Ogawa, to include a corrected NDVI value of the each grid area, wherein the corrected NDVI value is based on the number of crops in the respective grid area, as taught by Wang.
The suggestion/motivation for doing so would have been to “realize[s] real-time, rapid and accurate remote sensing monitoring of the uniformity of crop growth, and improve[s] the precision of investigation of the uniformity of crop growth” while also “solve[ing] the disadvantages of the prior art, such as heavy workload, low degree of automation, long update cycle, etc., and improve work efficiency” (Wang, para. [0012]).
Therefore, it would have been obvious to combine Ogawa with Wang to obtain the invention as specified in claim 1.
Regarding claim 2, Ogawa, in view of Wang, teaches the information processing device according to claim 1, wherein the CPU is further configured to obtain a vegetation cover rate from the number of crops in the each grid area; and generate the correction information based on the vegetation cover rate (Ogawa, para. [0032]: “Note that the LAI (Leaf Area Index) is an index of leaf area representing the amount of leaves of plants such that larger values indicate more leaves”; The Leaf Area Index (LAI) measures the total one-sided green leaf area per unit of ground area, serving as a crucial indicator of canopy density and health, and thus maps to “vegetation coverage rate”; see FIG. 5 above of Wang for discussion of each grid area).
Regarding claim 3, Ogawa, in view of Wang, teaches the information processing device according to claim 2, wherein the CPU is further configured to: specify a theoretical value of the evaluation information from the vegetation cover rate; and generate the correction information on a basis of the theoretical value (Ogawa, para. [0083]; para. [0120]; para. [0222]-[0224]; see rejection of claim 1 above; Soil-adjusted Vegetation Index (SAVI) maps to the “theoretical value” of the evaluation information from the cover rate to modify the NDVI).
Regarding claim 7, Ogawa, in view of Wang, teaches the information processing device according to claim 1, wherein the each grid area includes a partial area of a farm field, and the CPU is further configured to correct the evaluation information associated with the plurality of grid areas in the farm field (Ogawa, para. [0195]: “The clustering calculation section 28 performs a clustering calculation. For example, the clustering calculation section 28 performs clustering corresponding to division, into areas, of the farm field 300 or the like to be measured, on the basis of user input by the operation input section 7. The user specifies, for example, boundaries in the field across which different crops or the same crops at different developing stages are planted. This allows the user to perform optional cluster division.”; “FIG. 6 schematically illustrates a case where sensing is performed in each of certain areas H10 and H20.”;
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Regarding claim 8, Ogawa, In view of Wang, teaches the information processing device according to claim 1, wherein the CPU is further configured to obtain a vegetation cover rate from the number of crops for each grid area of the plurality of grid areas; classify the plurality of grid areas into a first cluster having a first vegetation cover rate and a second cluster having a second vegetation cover rate, wherein the first vegetation cover rate is higher than the second vegetation cover rate; and correct the evaluation information associated with a first grid area of the plurality of grid areas classified into the second cluster (Ogawa, para. [0232]-[0237]; FIG. 13A-13C: “FIG. 13A schematically illustrates the farm field 300 corresponding to the measurement target. Note that areas AR1 to AR6 are defined for convenience of description and do not necessarily correspond to areas of different types of vegetation. However, the hatched area AR3 is assumed to be an area where crops different from the crops in the other areas are cultivated … The clustering calculation section 28 performs the cluster division as indicated by the thick lines, reflecting input based on such information preliminarily known by the user. In step S302, the clustering calculation section 28 performs automatic clustering using information obtained from the macro measurement analysis calculation section 21 and using information obtained from the micro measurement analysis calculation section 23. The clustering is performed using, for example, the SIF amount, the LAI, the average leaf angle, the sun leaf ratio, or the like. FIG. 13B illustrates measurement ranges a, b, c, and d as the micro measurement ranges RZ3 related to a plurality of measurements. In this case, the measurement range a corresponds to the micro measurement range RZ3 for the measurement of an area AR3, the measurement range b corresponds to the micro measurement range RZ3 for the measurement of an area AR4, the measurement range c corresponds to the micro measurement range RZ3 for the measurement of an area AR5, and the measurement range d corresponds to the micro measurement range RZ3 for the measurement of an area AR6. The automatic clustering is assumed to involve executing, for example, processing of dividing the areas into clusters with different LAIs. It is assumed the value of the LAI varies between the measurement ranges a, b, and c but is substantially the same between the measurement ranges c and d. The area AR3 differs from the area AR4 in crops and the LAI. It is assumed that the areas AR4, AR5, and AR6 are the same in crops but that only the area AR4 involves a different growth situation. Then, setting the area AR4 as a separate cluster is appropriate.”;
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Regarding claim 9, Ogawa, in view of Wang, teaches the information processing device according to claim 8, wherein the CPU is further configured to correct the evaluation information associated with a second grid area of the plurality of grid areas classified into the first cluster (Ogawa, para. [0247]; FIG. 15A-B: “Note that FIG. 15A schematically illustrates the areas of the SIF calculation based on the macro measurement. The SIF is determined in units of cells each illustrated as the macro measurement resolution (macro resolution units W1 to Wn). In step S401, the inverse model calculation section 27 reads, for one macro resolution unit, the SIF calculated by the macro measurement analysis calculation section 21. For example, the inverse model calculation section 27 first reads the SIF of the macro resolution unit W1. In step S402, the inverse model calculation section 27 acquires, for the cluster corresponding to the macro resolution unit, the parameters determined by the micro measurement analysis calculation section 23, that is, the LAI, the average leaf angle, and the sun leaf ratio. FIG. 15B illustrates the LAI, the average leaf angle, and the sun leaf ratio for the above-described measurement ranges a, b, and c (=d). In other words, the LAI, the average leaf angle, and the sun leaf ratio are the model parameters for the cluster CL3 for the area AR3, the cluster CL4 for the area AR4, and the cluster CL1 for the areas AR1+AR2+AR5+A6 as illustrated in FIG. 13C … In step S403, the inverse model calculation section 27 performs the inverse model calculation. That is, a desired physical property value (for example, the character of the measurement target) is determined from the SIF obtained on the basis of the macro measurement.”;
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Regarding claim 12, Ogawa. In view of wang, teaches the information processing device according to claim 1, wherein the number of crops in the each grid is obtained from image data of the each grid area (Wang, para. [0073]-[0077]; para. [0088]-[0092]; FIG. 5; see rejection of claim 1 above discussing spatial distribution of crops which relies on the number of crops found from the spectral remote sensing images).
Regarding claim 13, Ogawa, in view of Wang teaches the information processing device according to claim 1, wherein the evaluation information associated with the each grid includes a vegetation index (Ogawa, para. [0083]; para. [0120]; para. [0222]-[0224]; see rejection of claim 1 above; Soil-adjusted vegetation index (SAVI) is used in evaluating Normalized Difference Vegetation Index (NDVI)).
With regards to claim 14, it recites the apparatus of claim 1 as a process. Thus, the analysis in rejecting claim 1 is equally applicable to claim 14.
Regarding claim 15, Ogawa teaches a non-transitory computer-readable medium having stored thereon computer-executable instructions which, when executed by a computer, cause the computer to execute operations (Ogawa, para. [0163]: “A program included in the software is downloaded from the network or read from the storage device 6 (for example, a removable storage medium), and installed in the information processing apparatus 1 in FIG. 7. Alternatively, the program may be prestored in the storage section 59 or the like. Then, the CPU 51 initiates the program to activate the function of each section as described above.”).
With regards to the remaining limitations of claim 15, they recite the apparatus of claim 1, as operations executed by a computer executing instructions stored on a non-transitory computer-readable medium. Thus, the analysis in rejecting claim 1 is equally applicable to claim 15.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Ogawa, in view of Wang, and in further view of Japanese Patent Application Publication No.: JP 2020149201 A (Aisaka et al.)
Regarding claim 4, Ogawa, in view of Wang, teaches the information processing device according to claim 3.
Ogawa, in view of Wang, fails to teach
wherein the CPU is further configured to specify the theoretical value from the vegetation cover rate based on reference data corresponding to a type of crops in the each grid area.
Aisaka teaches
wherein the CPU is further configured to specify the theoretical value from the vegetation cover rate based on reference data corresponding to a type of crops in the each grid area (Aisaka, page 13, para. 4; FIG. 10; FIG. 6: “The obtained growth map (NDVI image) may be converted into a plant height map and output as needed. As mentioned above, there is a correlation between NDVI and plant height, but it differs depending on the crop and variety. For example, FIG. 10 is a graph showing the correlation between NDVI and plant height for a certain variety of a certain crop PL. By actually measuring the plant height of the crop PL in regions where the NDVI is different in the field FD, the linear equation (y = ax + b) shown in FIG. 10 can be obtained. Once the straight line is obtained, the growth map shown in FIG. 6 can be converted into the plant height map shown in FIG. 10 In this case, the user can also diagnose the risk of lodging by looking at the output plant height map. From the same viewpoint, the growth map (NDVI image) may be converted into a leaf color map or a stem number map and output.”; Aisaka teaches the concept of creating reference data corresponding to crop variety”;
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It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the central processing unit, as taught by Ogawa, in view of Wang, to specify the theoretical value from the vegetation cover rate based on reference data corresponding to a type of crops in the each grid area, as taught by Aisaka.
The suggestion/motivation for doing so would have been that “by measuring the minimum growth parameters, the lodging risk diagnosis can be performed accurately, and the measurement recommended spots with stable growth can be presented to the user to improve the accuracy of the lodging risk diagnosis based on the measured growth parameters” (Aisaka, page 10, para. 2).
Therefore, it would have been obvious to combine Ogawa and Wang, with Aisaka, to obtain the invention as specified in claim 4.
Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Ogawa, in view of Wang, and in further view of Japanese Patent Application Publication No.: JP 2017046639 A (Kobayashi et al.) (hereinafter Kobayashi).
Regarding claim 5, Ogawa, in view of Wang teaches the information processing device according to claim 3.
Ogawa, in view of Wang, fails to teach
wherein the CPU is further configured to specify the theoretical value from the vegetation cover rate based on previous data in the each grid area.
Kobayashi teaches
wherein the CPU is further configured to specify the theoretical value from the vegetation cover rate based on previous data in the each grid area (Kobayashi, page 5, para. 4; page 8, para. 3; page 4, para. 1; FIG. 13: “When the vegetation coverage in the analysis area is obtained, the estimated growth calculation unit calculates the vegetation coverage in the analysis area based on the relational expression between the estimated coverage and the vegetation coverage obtained from the past growth of the crop. The estimated growth is calculated from the vegetation coverage rate. The relationship between the vegetation coverage rate and the estimated growth amount varies depending on the type and variety of the crop, or the region and environment of the field, but can be obtained in advance as a relational expression of a linear function from past growth results”; “Here, a graph as shown in FIG. 13 is obtained for the relationship between the planting coverage of rice (in this embodiment, Koshihikari in the target paddy field) and the growth amount (actual growth amount) based on the accumulation of past growth results. Based on this graph, the relational expression “y = 311.23x−71.561” (x is the planting rate, y is the estimated growth rate, and the determination coefficient R 2 is 0.8882, which is good, based on this graph. Value). In the server 1, at least the estimated growth amount calculation unit 17 is programmed with this relational expression, or a graph as shown in FIG. 13 Therefore, the server 1 gives the planting rate obtained in the previous processing step (St17) to this relational expression, and calculates the estimated growth amount of rice in the analysis region ROI (St18)”;
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It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the central processing unit, as taught by Ogawa, in view of Wang, to specify the theoretical value from the vegetation cover rate based on previous data in the each grid area, as taught by Kobayashi.
The suggestion/motivation for doing so would have been to “optimize the growth of crops and can greatly contribute to the improvement of quality and the income of farmers” (Kobayashi, page 10, para. 1).
Therefore, it would have been obvious to combine Ogawa and Wang, with Kobayashi, to obtain the invention as specified in claim 5.
Regarding claim 6, Ogawa, in view of Wang, and in view of Kobayashi, teaches the information processing device according to claim 5, wherein the CPU is further configured to specify the theoretical value from the vegetation cover rate based on the previous data corresponding to a condition of the each grid area (Kobayashi, page 5, para. 4; page 8, para. 3; page 4, para. 1; FIG. 13; see rejection of claim 5 above; the condition of the each grid area is how fertile the land is for rice growing).
Allowable Subject Matter
Claims 10-11 and 16 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Non-patent literature "Monitoring land cover changes during different growth stages of semi-arid cropping systems of wheat and sunflower by NDVI and LAI"; 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics); IEEE, 2019 (Pinar et al.) that teaches reference data including correlation data indicating a correlation between a leaf area index (LAI) and a vegetation index (NDVI).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL ADAM SHARIFF whose telephone number is 571-272-9741. The examiner can normally be reached M-F 8:30-5PM.
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/MICHAEL ADAM SHARIFF/
Examiner, Art Unit 2672
Prosecution Insights