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 Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. The amended limitation of “wherein the second set of spectral values does not include the thermal infrared band” is not supported by the applicant specification.
Any negative limitation or exclusionary proviso must have basis in the original disclosure. If alternative elements are positively recited in the specification, they may be explicitly excluded in the claims. See In re Johnson, 558 F.2d 1008, 1019, 194 USPQ 187, 196 (CCPA 1977) (“[the] specification, having described the whole, necessarily described the part remaining.”). See also Ex parte Grasselli, 231 USPQ 393 (Bd. App. 1983), aff’d mem., 738 F.2d 453 (Fed. Cir. 1984). The mere absence of a positive recitation is not basis for an exclusion. Any claim containing a negative limitation which does not have basis in the original disclosure should be rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, as failing to comply with the written description requirement. Note that a lack of literal basis in the specification for a negative limitation may not be sufficient to establish a prima facie case for lack of descriptive support. Ex parte Parks, 30 USPQ2d 1234, 1236 (Bd. Pat. App. & Inter. 1993). See MPEP § 2163 - § 2163.07(b) for a discussion of the written description requirement of 35 U.S.C. 112(a) and pre-AIA 35 U.S.C. 112, first paragraph.
Claim Rejections - 35 USC § 103
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(s) 1, 5-6, 8-10, 14-15, and 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu (PGPUB: CN 114049562 A) in view of Chen (CN 117197678 A), in view of Liu (CN 112215127 A), and further in view of Geng (CN 116580318 A).
Regarding claims 1, 10, and 19. Xu teaches a method for determining a metric at a second resolution at a location, the method comprising:
gathering a first set of spectral values from a first satellite at a first resolution for the location in a first time period, the first set of spectral values (see page 6, lines 31-33, obtaining a fine spatial resolution multi-source data non-uniform area, and eliminating the outlier value in the area of the coarse space resolution multi-source data; see page 11 and 12, lines 30-32 and 1-6, the remote sensing image characteristic of the area to be processed in 1 year period value comprises remote sensing image data in each band median synthesis of 4 periods in one year and the corresponding spectrum index, wherein 4 periods is respectively-3 months, 4-6 months, 7-9 months, 10-12 months);
determining a set of indices at the first resolution and the metric at the first resolution from the first set of spectral values, wherein the set of indices comprises: NDVI; NDWI; NDBI; NDSI; (see page 14, lines 20-27, setting the preset training sample filter each land covered by type, calculating the fine spatial resolution multi-source data in each grid area of each land covered by the corresponding spectrum index, specifically, obtaining each grid corresponding to the land covered by the type, the corresponding spectrum index of each land covered by the type is as follows: land is vegetation index NDVI, woodland, the grassland and the shrub are the green chlorophyll vegetation index GCVI, the water body and the wetland are the water index NDWI, the construction site is the building index NDBI, the bare soil index BSI, the glacier Asiatic and the moss original are the snow index NDSI), and
wherein the metric is a continuous-valued physical quantity derived from a thermal infrared band (see page 11, lines 4-6, extracting and according to the high quality multi-band data in the remote sensing image data, calculating the value and the standard difference of the spectrum index and the preset year, generating remote sensing image data feature set according to the multi-band data and spectrum data. wherein the high quality multi-band data in the remote sensing image data comprises the first wave band of Landsat 7 to the fifth wave band, the seventh wave band is 6 wave bands, and the second wave band of Landsat 8 to the 7 wave band is 6 wave bands; see page 6, lines 8-9, band 5-near infrared, band 6-short wave infrared 1, band 7-short wave infrared 2);
determining a predictive model linking the metric at the first resolution with the set of indices at the first resolution (see page 16, lines 5-8, according to the space resolution is 30 m remote sensing image wave band and the spectrum index of 75 layer remote sensing image feature set and terrain auxiliary data, through machine learning algorithm for each grid corresponding to the remote sensing image interpretation training sample set to construct classifier, decoding the multi-source data inconsistency area, obtaining the fine spatial resolution multi-source data classification result with the modified spatial resolution of 30 m. wherein the terrain auxiliary data comprises plucking and gradient data; The machine learning algorithm is a random forest algorithm);
acquiring a second set of spectral values from a second satellite at the second resolution for the location for the first time period, the second resolution finer than the first resolution (see page 10, lines 25-29, according to the designated year of land covered product, obtaining the area to be processed in the designated year of mass remote sensing image data, and obtaining the area to be processed in the designated year of the mass remote sensing image data of two years, wherein the mass remote sensing image data from the Google Earth engine platform Landsat 7 ETM + image and Landsat 8 OLI image), and
wherein the second set of spectral values does not include the thermal infrared band (see page 11, lines 25-26, Blue, Green, Red, Nir, Swir1, Swir2 respectively Landsat 7ETM + image band 1-blue, wave band 2-green, wave band 3-red);
determining the set of indices at the second resolution from the second set of spectral values (see page 10, lines 3-6, as one example of the present embodiment, the unified each land covered by the product of the reference coordinate system is WGS84 coordinate system, by comparing the selected resolution of the CCI-LC product of each land covered by the product resolution as the reference, the each land covered by the product grid re-sampling is 300m); and
applying the second set of indices to the model to determine the metric at the second resolution (see page 14, lines 9-16, for the data space resolution selection, usually selecting all product resolution of the maximum one as the target of the re-sampling, so as to control the product precision, therefore, step 101 the multi-source land covered by product pre-processing space resolution is 300m. performing fusion correction to the data set, finally realizing the result of the rough resolution. In this embodiment, combined with the Landsat image of 30m space resolution, the initial obtained thick resolution consistency area for dividing, and filter, obtaining the 30 m spatial resolution of the multi-source data effective information).
However, Xu does not expressly teach indices comprises NBRI.
Chen teaches that in the sub-step 2-4, the normalized vegetation index, the normalized farming index and the normalized combustion rate index of the buffer area are obtained by the following formula (1): NBRI represents the normalized combustion rate index for representing the burning condition of the cultivated land straw (see page 8, lines 4-6 and 12-13).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Xu by Chen to obtain NBRI represents the normalized combustion rate index for representing the burning condition of the cultivated land straw, in order to provide that indices comprises NBRI. Therefore, combining the elements from prior arts according to known methods and technique would yield predictable results.
However, the combination does not expressly teach spectral values comprising a value from a thermal band.
Liu teaches that using the full color image data with high resolution to perform image fusion for the processed data (such as multi-spectral remote sensing data); so as to obtain the fusion result of the multi-spectral data. Aiming at the surface temperature inversion classification, from the remote sensing image inversion surface temperature of the main method is as follows: atmosphere correction method, single channel algorithm and split window algorithm. taking the atmospheric correction method as an example, using the atmospheric correction method (formula (4)): First, the influence of the atmosphere on the surface heat radiation is estimated, and then the part of the atmosphere is subtracted from the total amount of the heat radiation observed from the satellite sensor, thereby obtaining the surface heat radiation intensity, and then converting the heat radiation intensity into the corresponding surface temperature. formula (4) is as follows: wherein L is the atmosphere upward radiation brightness, L is the energy of the atmosphere downward radiation to the ground, ε is the surface specific radiation rate, TS is the ground real temperature (K), B (TS) is black heat radiation brightness, τ is the transmission rate of the atmosphere in the thermal infrared band (see page 11, lines 9-24).
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 combination by Liu to obtain using the full color image data with high resolution to perform image fusion for the processed data (such as multi-spectral remote sensing data); so as to obtain the fusion result of the multi-spectral data and heat radiation brightness in the thermal infrared band to provide spectral values comprising a value from a thermal band. Therefore, combining the elements from prior arts according to known methods and technique would yield predictable results.
However, the combination does not expressly teach determining a predictive model.
Gang teaches that obtaining the original multi-spectral satellite image data and pre-processing it to obtain the pre-processed original multi-spectral satellite image data;
fusing the pre-processed original multi-spectrum satellite image data based on the expanded super-resolution convolution neural network, obtaining the fusion remote sensing data set of the target space-time resolution, wherein the target space-time resolution is higher than the space-time resolution of the original multi-spectrum satellite image data;
extracting and calculating the vegetation variable data in the fusion remote sensing data set of the target space-time resolution;
obtaining the soil sample nutrient actual measurement data, and based on the soil sample nutrient actual measurement data and the vegetation variable data, using the extreme gradient lifting tree algorithm and genetic algorithm to determine the target prediction model based on the vegetation variable inversion soil nutrient;
The soil nutrient of the crop coverage area is predicted based on the target prediction model (see page 5, lines 14-27).
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 combination by Gang to obtain extracting and calculating the vegetation variable data in the fusion remote sensing data set of the target space-time resolution and obtaining the soil sample nutrient actual measurement data, and based on the soil sample nutrient actual measurement data and the vegetation variable data, using the extreme gradient lifting tree algorithm and genetic algorithm to determine the target prediction model based on the vegetation variable inversion soil nutrient, in order to provide determining a predictive model. Therefore, combining the elements from prior arts according to known methods and technique would yield predictable results.
Regarding claims 5 and 14. The combination teaches the method of claim 1, wherein the set of indices further comprises at least one from a list, the list comprising: BSI; (see Xu, page 11, lines 19-21, The overall vegetation index for soil adjustment: Naked soil index: BSI = ((Swir2 + Red)-(NIR + Blue))/((Swir2 + Red) + (NIR + Blue))).
However, the combination does not expressly teach the list comprising: EVI; SAVI; NDMI; MSI; GCI; and ARVI.
The examiner is taking "Official Notice" that the limitation about the list comprising: EVI; SAVI; NDMI; MSI; GCI; and ARVI is well known in the art.
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the invention was made to have modified the combination so that the list comprising: EVI; SAVI; NDMI; MSI; GCI; and ARVI would be available.
Regarding claims 6 and 15. The combination does not expressly teach the method of claim 5, wherein the method further comprises: choosing a list item based on analyzing climate of the location.
The examiner is taking "Official Notice" that the limitation about choosing a list item based on analyzing climate of the location is well known in the art.
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the invention was made to have modified the combination so that choosing a list item based on analyzing climate of the location would be available.
Regarding claims 8 and 17. The combination teaches the method of claim 1, wherein determining a model comprises applying a random forest algorithm (see Xu, page 16, lines 13-19, based on Google Earth engine platform, generating a remote sensing image land is translated feature set, applying random forest algorithm, the fine space resolution multi-source data consistent area purifying sample as the training sample set of algorithm, setting parameter tree number is 300. the characteristic number selected by each tree is the square root of the total number of the wave band and the remote sensing index; the training number input by each tree is 63 % of the total sample amount, the multi-source data inconsistency area is modified).
Regarding claims 9 and 18. The combination teaches the method of claim 1, further comprising generating an image based on the metric at the second resolution (see Liu, page 13, lines 15-19, the large field of view high sensitive luminous remote sensing camera, comprising 130m resolution, the luminous imaging capability of the 260km, the macroeconomic analysis based on luminous remote sensing and providing data. at the same time, satellite carrying navigation enhanced scientific test load, capable of developing Beidou navigation system positioning precision lifting test).
Claim(s) 2-3, 11-12, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu (PGPUB: CN 114049562 A) in view of Chen (CN 117197678 A), in view of Liu (CN 112215127 A), in view of Geng (CN 116580318 A), and further in view of CHEN’248 (CN 117314248 A).
Regarding claims 2, 11, and 20. The combination teaches the method of claim 1, wherein the first satellite comprises a Landsat satellite (see Xu, page 18, lines 16-19, extracting the first wave band of the Landsat 7 in the remote sensing image data to the fifth wave band, the seventh wave band is 6 wave bands, and the second wave band of the Landsat 8 to the seventh wave band is 6 wave bands).
However, the combination does not expressly teach that the second satellite comprises a Sentinel satellite.
CHEN’248 teaches that the surface distortion information extraction uses 58 SAR images of the C wave band obtained by the Sentinel-1A satellite planned to be launched by the European Space Agency (see page 10, lines 1-3).
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 combination by CHEN’248 to obtain Sentinel-1A satellite, in order to provide the second satellite comprises a Sentinel satellite. Therefore, combining the elements from prior arts according to known methods and technique would yield predictable results.
Regarding claims 3 and 12. The method of claim 2, wherein the metric comprises Land Surface Temperature, and the thermal band comprises Landsat band 10 (see CHEN’248, page 17, lines , obtaining several Landsat-8 image data with cloud amount less than 5 % from May 2015 to May 2021, the spatial resolution of visible light (wave band 1 to 7 and 9) and thermal infrared (wave band 10 to 11) (TIRS) is 30m and 100m respectively).
Claim(s) 4 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu (PGPUB: CN 114049562 A) in view of Chen (CN 117197678 A), in view of Liu (CN 112215127 A), in view of Geng (CN 116580318 A), in view of CHEN’248 (CN 117314248 A), and further in view of Kingwill (PGPUB: 20240193925).
Regarding claims 4 and 13. The method of claim 2, wherein the metric comprises the thermal band comprises Landsat band 11 (see CHEN’248, page 17, lines , obtaining several Landsat-8 image data with cloud amount less than 5 % from May 2015 to May 2021, the spatial resolution of visible light (wave band 1 to 7 and 9) and thermal infrared (wave band 10 to 11) (TIRS) is 30m and 100m respectively).
However, the combination does not expressly teach the metric comprises CO2.
Kingwill teaches that The satellite imagery may take the form of imagery from the United States Geological Survey/NASA Landsat-8 and/or Landsat-9 constellation. The top of atmosphere reflectance images may take the form of Landsat L1TP data corresponding to bands B7 and/or B6. When used, the bottom of atmosphere spectral reflectance images may take the form of Landsat L1TP data corresponding to bands B1 to B11 (see paragraph 23); using Sentinel level 2A imagery, a spectral land cover signature is created (for the wavelength range of B11 and B12) for each pixel. This is done, by selecting the spectral signature from a spectral library that is the closest match to the signature created with the Sentinel 2 level 2A bands. [0078] This signature is put through a series of spectral filters that compensate for H2O, CO2, inherent CH4 in the atmosphere, as well as for angle of illumination. The output product represents realistic noise from surface albedo and the atmosphere. [0079] The noise is added to the B11 and B12 transmittance using the correction factor in the diagram above (see paragraph 77); HAPI Atmospheric Data: HAPI contains a range of atmospheric data, for the pipeline we need a table that relates wavelength to the % of absorption for methane, water vapor and carbon dioxide) (see paragraph 90) .
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 combination by Kingwill to obtain HAPI Atmospheric Data: HAPI contains a range of atmospheric data, for the pipeline we need a table that relates wavelength to the % of absorption for methane, water vapor and carbon dioxide), in order to provide the metric comprises CO2. Therefore, combining the elements from prior arts according to known methods and technique would yield predictable results.
Claim(s) 7 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu (PGPUB: CN 114049562 A) in view of Chen (CN 117197678 A), in view of Liu (CN 112215127 A), in view of Geng (CN 116580318 A), and further in view of Sun (CN 115630567 A).
Regarding claims 7 and 16. The combination does not expressly teach the method of claim 1, wherein determining a model comprises applying a multivariable regression line.
Sun teaches that step 5: Construction of coastal zone soil organic carbon reserves estimation and prediction enhanced regression tree model
using the coastal zone space distribution data and soil organic carbon sample data, combining the model training sample data set obtained in step 3, constructing the coastal zone soil organic carbon reserves estimation and prediction enhanced regression tree model; step 6: Organic carbon reserve simulation of coastal zone soil
using the optimal model variable data obtained in the step 2 as the model input, the coastal zone soil organic carbon reserves estimation and prediction enhanced regression tree model constructed by step 5, realizing 10 m space resolution coast with soil organic carbon storage simulation estimation (see page 5, lines 17-27).
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 combination by Sun to obtain Construction of coastal zone soil organic carbon reserves estimation and prediction enhanced regression tree model using the coastal zone space distribution data and soil organic carbon sample data, combining the model training sample data set obtained in step 3, constructing the coastal zone soil organic carbon reserves estimation and prediction enhanced regression tree model, in order to provide wherein determining a model comprises applying a multivariable regression line. Therefore, combining the elements from prior arts according to known methods and technique would yield predictable results.
Response to Arguments
Regarding limitations of Claims of the instant case in view of the amended claims and upon further consideration, a new ground(s) of rejection,
necessitated by the amendments is made in view of different interpretation of the previously applied references and new prior art as presented in this Office action.
Therefore, Applicant’s arguments are moot. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XIN JIA whose telephone number is (571)270-5536. The examiner can normally be reached 9:00 am-7:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Gregory Morse can be reached at (571)272-3838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/XIN JIA/Primary Examiner, Art Unit 2663
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