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 .
Claim Objections
Claim 6 is objected to because of the following:
in line 22, “a cycle pattern converter to” should be deleted to be consistent with the changes made to the rest of the claim. 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.
Claims 1-5, 7 and 11-21 are rejected under 35 U.S.C. 103 as being unpatentable over CN 110991332 A to Wang et al (hereinafter ‘Wang’) (Please refer to the attached USPTO translation version) in view of CN 106803059 B to Fan et al (hereinafter ‘Fan’) (Please see the attached USPTO translation version).
Regarding claim 1, Wang discloses an image processing device (Page 4, Para 2, wherein as shown in FIG. 2, this embodiment vegetation index early warning method comprises the followings steps: step S100: obtaining the remote sensing image data of the target area, and pre-processing to the remote sensing image data, as including device inherently, calculating the NDVI value), comprising: processing circuity to acquire a target image (Page 4, Para 2, wherein obtaining the remote sensing image data of the target area, and pre-processing to the remote sensing image data), calculate a target index value for a subregion that is a portion of the acquired target image (Page 4, Para 2, wherein pre-processing the remote sensing image data, calculating the NDVI value, as index value), acquire a plurality of material images arranged in chronological order, calculate, in chronological order, an index value for a material subregion that is a portion of each of the acquired plurality of material images (Page para 7, wherein calculating 2000 years now, the average value of each period year vegetation index grid data, generating vegetation index mean grid pattern, as the material images), generate a cycle pattern fitting a chronological change in the calculated index value (Page 5, para 6, wherein Step 201: using ArcGIS Raster Calculator tool (grid calculator), calculating the year data in the grid, the vegetation index of each year in the grid, . . . the target area forming the time sequence of many years of vegetation index, vegetation index analysis to the change characteristic of the year, as including cycle pattern of change and cycles), and calculate a correction index value that is a value in the generated cycle pattern at a capturing date and time at which the target image is captured (Page 5, Para 5, wherein calculating the initial year average vegetation index of the target year, as the index in the captured data and time, generating annual vegetation index mean grid pattern, and according to the vegetation index average value calculating the vegetation index standard deviation), and determine that a change occurs in the material subregion (Page 5, Para 11-13 and Page 6, para 3, wherein ArcGIS Raster using calculator tool, firstly calculating the vegetation index mean and vegetation index, as including the corrected/mean of the target year, including and standard difference; then the target year of vegetation index minus mean vegetation index and vegetation index standard deviation and the difference data to obtain the target year, according to the obtained target year of difference data plotted as the difference grid image, and wherein the difference in the difference grid image is less than the preset threshold value, marked as early warning, is greater than a preset threshold, marked as normal, the preset is preset to -0.15, as determining change), the change occurs when a difference between the target index value and the correction index value is greater than a threshold (Page 6, Para 4, wherein the threshold value is -0.15, can be manually adjusted or system built-in is set in advance, for example, if the difference value is greater than -0.15, it indicates the year vegetation index NDVI is increased compared to the average of past N years, as the change, marked as normal). Wang des not specifically disclose a pattern fitting a chronological change. Fan discloses generating by fitting chronological change pattern (Page 2, para 6, wherein establishing an initial remotely sensed vegetation index time sequence based on Gaussian equation of each effective point fitting pattern). Wang and Fan are combinable because they both disclose image surface property detection. Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the pattern fitting a chronological change of Fan’s device and method with Wang’s so that to generate real and reliable a series of cosine (sine) sinusoid of the data (Page 2, para 3).
Regarding claim 2, in the combination of Wang and Fan, Wang discloses wherein the target image is one of a plurality of pieces of image data that are each obtained by image capturing of a same spatial region and that are arranged in chronological order, and the plurality of material images are included in the plurality of pieces of image data, other than the target image, that are each obtained by the image capturing of the same spatial region and that are arranged in chronological order (Page 5, para 5 and 6, wherein step S200: the obtained projection to obtain vegetation index NDVI value grid pattern of different period according to the vegetation index grid pattern, calculating the initial year average vegetation index of the target year, generating annual vegetation index mean grid pattern, and according to the vegetation index average, as the material image index, value calculating the vegetation index standard deviation; Step 201: using ArcGIS Raster Calculator tool (grid calculator), calculating the year data in the grid, the vegetation index of each year in the grid, for addition and removal of the total year, calculating the vegetation index average value of each period of grid data. the target area forming the time sequence of many years of vegetation index, inherently as chronological order, vegetation index analysis to the change characteristic of the year.).
Regarding claim 3, in the combination of Wang and Fan, Wang discloses wherein the target index value includes at least one of a mean value of scattering intensity of the target image, a standard deviation of the scattering intensity, a mean value of luminance of the target image, or a standard deviation of the luminance (Page 3, para 1, wherein Further, the grid pattern, according to the vegetation index calculation start year average vegetation index of the target year, generating mean grid pattern of vegetation index, specifically comprising the following steps: using the ArcGIS grid calculator in the vegetation index of each year in the grid, for addition and removal of the total year, calculating the vegetation index average value of each period of grid data).
Regarding claim 4, in the combination of Wang and Fan, Fan discloses wherein the processing circuitry cycle pattern generator generates the cycle pattern expressed by superposition of a linear expression of time and a sine wave (Page 2, para 1-3, wherein sensing data time sequence fitting method is a remote sensing image de-noising method currently used. classical timing method of remote sensing: a Savitzky-Golay (S-G) filtering method, Fourier filtering method and Gaussian filtering method. . . .. Fourier filtering method to curve the time expressed as a linear superposition, by screening important band information and the fitting result is real and reliable a series of cosine (sine) sinusoid).
Regarding claim 5, in the combination of Wang and Fan, Fan discloses wherein the processing circuitry cycle pattern generator determines a parameter for the cycle pattern by least squares fitting (Page 2, Para 3, wherein Gaussian filtering method non-linear Gaussian function fitting by least squares, overcomes the subjectivity of extracting the information in the long time range in the obvious advantage).
Regarding claim 7, please refer to the corresponding device claim 1 for further teachings.
Regarding claim 11, in the combination of Wang and Fan, Wang discloses wherein the target index value includes at least one of a mean value of scattering intensity of the target image, a standard deviation of the scattering intensity, a mean value of luminance of the target image, or a standard deviation of the luminance (Page 3, para 1, wherein Further, the grid pattern, according to the vegetation index calculation start year average vegetation index of the target year, generating mean grid pattern of vegetation index, specifically comprising the following steps: using the ArcGIS grid calculator in the vegetation index of each year in the grid, for addition and removal of the total year, calculating the vegetation index average value of each period of grid data).
Regarding claims 12-14, in the combination of Wang and Fan, Fan discloses wherein the processing circuitry generates the cycle pattern expressed by superposition of a linear expression of time and a sine wave (Page 2, para 1-3, wherein sensing data time sequence fitting method is a remote sensing image de-noising method currently used. classical timing method of remote sensing: a Savitzky-Golay (S-G) filtering method, Fourier filtering method and Gaussian filtering method. . . .. Fourier filtering method to curve the time expressed as a linear superposition, by screening important band information and the fitting result is real and reliable a series of cosine (sine) sinusoid).
Regarding claims 15-21, in the combination of Wang and Fan, Fan discloses wherein the processing circuitry determines a parameter for the cycle pattern by least squares fitting (Page 2, Para 3, wherein Gaussian filtering method non-linear Gaussian function fitting by least squares, overcomes the subjectivity of extracting the information in the long time range in the obvious advantage).
Allowable Subject Matter
Claim 6 would be allowed if updated per the claim objection noted above. The following is a statement of reasons for the indication of allowable subject matter: The prior art or the prior art of record specifically, Wang and Fan, does not disclose:
. . . . calculate, in chronological order, a comparative index value for a comparative material subregion that is a portion of each of the acquired plurality of comparative material images, generate a comparative cycle pattern fitting a chronological change in the calculated comparative index value, convert the generated comparative cycle pattern, and generate an integrated cycle pattern from the comparative cycle pattern resulting from the conversion and the cycle pattern, and . . . . and determine that a change occurs in the comparative material subregion when a difference between the target index value and the integrated correction index value is greater than a threshold, of claim 6 combined with other features and elements of the claim.
Contact Information
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/SHERVIN K NAKHJAVAN/ Primary Examiner, Art Unit 2672