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 § 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, 28, 55, and 82-83 is/are rejected under 35 U.S.C. 103 as being unpatentable over Osborne et al. (US 9880140) in view of Freitag et al. (US 11151379) and Logie et al. (US 2020/0005038).
Regarding to claims 1, 28, 55:
Osborne et al. discloses a method for predicting crop yield of a geographic region, the method comprising:
receiving a time series of satellite imagery, the time series of satellite imagery covering at least the geographic region during a predetermined time period, the predetermined time period comprising one or more phenology periods (FIG. 2, element 113: Remotely-sensed imagery. Column 7, lines 45-52: The remotely-sensed imagery 113 is satellite systems. Column 7, lines 65-67: The remotely-sensed imagery 113 is used to map the crop field and generate a time-series profile of crop development and vitality);
receiving a time series of weather data, the time series of weather data covering at least the geographic region during the predetermined time period (FIG. 2, elements 116-117: Observed weather data and Current field-level weather data. Column 6, lines 9-15: Meteorological data is collected for the specific geographical area which a crop is located by using fine-resolution analyses of weather derived from sensors across the area);
generating from the time series of satellite imagery at least one surface feature of the geographic region during each of the one or more phenology periods (Column 7, lines 65-67: The remotely-sensed imagery 113 is used to map the crop field and generate a time-series profile of crop development and vitality);
generating from the time series of weather data at least one weather feature of the geographic region during each of the one or more phenology periods (column 5, lines 48-51: The observed weather data 116 during the current growing season and the currently-experienced field-level weather data 117 construct the current weather conditions);
providing the at least one surface feature and the at least one weather feature to a trained model; and receiving from the trained model a prediction of crop yield for the geographical region (Abstract: The modeling framework applies extended range weather forecast and remotely-sensed imagery to improve crop growth and development estimation, validation and projection).
Osborne et al. however does not teach dividing the predetermined time period into the one or more phenology periods based on the time series of satellite imagery, wherein the predetermined time period comprises sampling a plurality of pixels of the time series of satellite imagery, determining a time series of vegetation indices based on the series of satellite imagery, and locating peaks in the time series of vegetation indices, wherein the dividing uses the located peaks in the time series of vegetation indices. Osborne et al. also does not teach generating a set of surface features and a set of weather features, selecting at least one surface feature from the set of surface features and at least one weather feature based from the set of weather features based on the one or more phenology periods, wherein selecting the at least one surface feature and the at least one weather feature comprises determining a performance gain attributable to each of the at least one surface feature and the at least one weather feature each of the one or more phenology periods, wherein the trained model comprises a linear mixed-effects model or a decision tree ensemble, and wherein determining the performance gain comprises applying a decision tree ensemble.
Freitag et al. discloses a method for predicting crop growth comprising obtaining data including satellite imagery data, weather data (FIG. 5, step 502), sampling the obtained data comprising sampling a plurality of pixels of the time series of satellite imagery (Abstract: Extracting data from pixels of satellite images) and the weather data to generate a set of vegetation indices/features (FIG. 5, step 504) based on the obtained data (Abstract: Generating temporal sequences of vegetation indices), wherein the vegetation indices/features are selected by applying smoothing function (FIG. 5, step 506) and using a threshold vegetation index for a decision on the selection of the vegetation indices (FIG. 5, step 510) to gain the performance attribution.
Therefore, it would have been obvious for one having ordinary skill in the art at the time of the filing date to modify Osborne’s method to include extracting pixels of satellite images in order to determine the vegetation indices as taught by Freitag et al. (Column 3, lines 1-10).
In addition, Logie et al. teaches a method for monitoring crop growth comprising locating peaks in the time series of vegetation indices and dividing the vegetation indices using the located peaks (FIG. 2 shows the plurality of phenology periods (green-up, peak, senescence) divided based on the peak value of the vegetation index value).
Therefore, it would have been obvious for one having ordinary skill in the art at the time of the filing date to modify Osborne’s method, as modified in view of Freitag et al., to obtain the phenology periods by using the peak of the vegetation index to monitor a change in crop growing as taught by Logie et al. (FIG. 2).
Response to Arguments
Applicant's arguments filed 4/28/2026 have been fully considered but they are not persuasive. Please see the rejection above for newly citations and explanations.
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 LAM S NGUYEN whose telephone number is (571)272-2151.
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/LAM S NGUYEN/ Primary Examiner, Art Unit 2853
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