Prosecution Insights
Last updated: April 19, 2026
Application No. 17/625,287

CROP YIELD FORECASTING MODELS

Non-Final OA §103
Filed
Jan 06, 2022
Examiner
NGUYEN, LAM S
Art Unit
2853
Tech Center
2800 — Semiconductors & Electrical Systems
Assignee
Indigo AG Inc.
OA Round
3 (Non-Final)
79%
Grant Probability
Favorable
3-4
OA Rounds
2y 9m
To Grant
79%
With Interview

Examiner Intelligence

Grants 79% — above average
79%
Career Allow Rate
1093 granted / 1391 resolved
+10.6% vs TC avg
Minimal +1% lift
Without
With
+0.7%
Interview Lift
resolved cases with interview
Typical timeline
2y 9m
Avg Prosecution
61 currently pending
Career history
1452
Total Applications
across all art units

Statute-Specific Performance

§101
2.5%
-37.5% vs TC avg
§103
45.9%
+5.9% vs TC avg
§102
33.7%
-6.3% vs TC avg
§112
8.1%
-31.9% vs TC avg
Black line = Tech Center average estimate • Based on career data from 1391 resolved cases

Office Action

§103
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, and 55 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. Freitag et al. discloses a method for predicting crop growth comprising obtaining satellite imagery (FIG. 5, step 502: Obtain data including satellite data), sampling comprises sampling a plurality of pixels of the time series of satellite imagery (Abstract: Extracting data from pixels of satellite images), determining a time series of vegetation indices based on the series of satellite imagery (Abstract: Generating temporal sequences of vegetation indices). 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). Conclusion 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. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, DOUGLAS RODRIGUEZ, can be reached on 571-431-0716. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /LAM S NGUYEN/ Primary Examiner, Art Unit 2853
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Prosecution Timeline

Jan 06, 2022
Application Filed
Jul 16, 2024
Non-Final Rejection — §103
Oct 07, 2024
Response Filed
Feb 25, 2025
Request for Continued Examination
Feb 26, 2025
Response after Non-Final Action
Mar 11, 2025
Non-Final Rejection — §103
Aug 12, 2025
Applicant Interview (Telephonic)
Aug 12, 2025
Examiner Interview Summary
Aug 13, 2025
Response Filed
Oct 10, 2025
Request for Continued Examination
Oct 20, 2025
Response after Non-Final Action
Oct 28, 2025
Non-Final Rejection — §103 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

3-4
Expected OA Rounds
79%
Grant Probability
79%
With Interview (+0.7%)
2y 9m
Median Time to Grant
High
PTA Risk
Based on 1391 resolved cases by this examiner. Grant probability derived from career allow rate.

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