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 .
DETAILED ACTION
Priority
Acknowledgment is made of applicant's claim for domestic benefit based on parent application 18/449,303 filed on August 14, 2023; however, this is a continuation-in-part application and the previous filed application(s) may not teach some elements of the claims.
DETAILED ACTION
Claims 1 - 20 are pending in the application.
Claims 1, 11, and 16 are independent.
This action is Final based on a new 35 U.S.C. §103 prior art reference(s) that was/were necessitated by the applicant’s amendment; see MPEP §706.07(a).
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.
Claims 1 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Young (US PG Pub. No. 2021/0103728), herein “Young,” in view of Kastl et al. (PG Pub. No. 20230375988), herein “Kastl,” if further view of Fujiyama (US PG Pub. No. 20200309687), herein “Fujiyama,” in view of Korean patent application Hyeon et al. (
KR 20210074931 A), herein “Hyeon.”
Regarding claim 1,
Young teaches a computing device (computer) for processing geospatial data associated with an irrigation system, the computing device comprising: (Par. 0056: “…allowing the user computer to input data pertaining to nitrogen applications, planting procedures, soil application, tillage procedures, irrigation practices, or other information relating to the particular field.” Par. 0075: “In one embodiment, script generation instructions 205 are programmed to provide an interface for generating scripts, including variable rate (VR) fertility scripts. The interface enables growers to create scripts for field implements, such as nutrient applications, planting, and irrigation. See also Abstract, Par. 0076 and 0098.)
a processing element in electronic communication with a memory element, the processing element (processor or processing, Par. 0108) configured or programmed to receive a plurality of image data files, each encoding an image, of a crop in one or more geolocations, (Par. 0152: “In an embodiment, digital image camera 814 comprises one or more digital image cameras with low-resolution capability and one or more digital image cameras with high-resolution capability. Or, camera 814 may comprise a single digital camera that is capable of capturing images at high resolution or low resolution in response to signals or instructions from a stored control program. Digital image camera 814 captures digital images of objects, resulting in creating and storing captured field digital image data of objects, as they are positioned for viewing as vehicle 810 travels the field. In an embodiment, vehicle 810 is programmed to retrieve GPS data for the then-current geo-location of the vehicle at the time that camera 814 captures images and is programmed to generate GPS encoded digital images 816 of specific spaces of a field. In an embodiment, digital image camera 814 captures low-resolution or a combination of low and high-resolution digital images of a plant object 812.” See also Par. 0033 - 0037, Par 0155, and Abstract.)
Young does not teach water stress level of a plant or crop from images. However, Kastl does teach derive a water stress level of the crop from the images, (Par. 0048: “At a next step 1206, the system of the present invention may preferably analyze the imaging data and determine anomalies in a monitored area indicating an issue such as pests, diseases, nutrient stress, water stress, lack of emergence, lagging or leading crop development, etc. in an area of the field.” See also Young Par. 0027, 0133 – using images to determine conditions of the plant; and figure 8.)
receive or determine geospatial data regarding the water stress level of the crop in one or more geolocations, (Par. 0056: “…collect data, the drone may be programmed to hover and/or land in the area of each sensor, collect the data wirelessly and then return to the irrigation machine and forward the data to the cloud or irrigation machine for further analysis. Alternatively, the drone may preferably directly send the data wirelessly to the cloud, the irrigation or both. In response, various prescriptions may be adjusted. For example, data collected from soil moisture probes may cause the system to adjust the VRI prescription based on the collected custom-character. Still further, the system of the present invention may include a fleet of drones located on the machine that preferably may be programmed to conduct sorties in groups ahead of an operating machine using sensor arrays to measure field conditions (e.g., crop temperature, moisture, health) and sending data back to the analysis system (e.g., control panel on the pivot) wirelessly where the geolocated data would be processed. For example, the drone sorties may image and identify data the system may analyze to determine a modified crop water stress index which would then be used to modify a parameter of the application by the machine. In response, the system may adjust the amount of water applied (i.e., variable-rate irrigation) as the machine passes over the areas identified from drone acquired data. Still further, the drone may image and collect data for use in calculating and adjusting dynamic variable nutrient or crop protectant applications (e.g., VR chemigation or VR Fertigation).” See also Par. 0064 – Geo-tiff images.)
determine an amount of water to apply to the crop according to the geospatial data, and (Par. 0052: “Further, the imaging data may be used by an operator or AI/ML system (e.g., in combination with location data or the like) to decide whether to continue irrigating, stop the machine where it is, or to stop irrigating and move a given machine to an area in a given field…” See full paragraphs 0042, 0053, and 0056.)
output an electronic signal to adjust the amount of water applied to the crop, the electronic signal varying according to the geospatial data regarding the water stress level of the crop. (Par. 0056: “For example, the drone sorties may image and identify data the system may analyze to determine a modified crop water stress index which would then be used to modify a parameter of the application by the machine. In response, the system may adjust the amount of water applied (i.e., variable-rate irrigation) as the machine passes over the areas identified from drone acquired data. Still further, the drone may image and collect data for use in calculating and adjusting dynamic variable nutrient or crop protectant applications (e.g., VR chemigation or VR Fertigation).” Par. 0077)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have combined the method and system that uses a processor as part of a computer to analyze images and encode the images with geolocation data such as GPS location data and determine the condition of a plant as in Young with using image analysis for plants, determine water stress of the crop or plants and adjust the amount of water or irrigation to the crop/plants in an area as in Kastl in order to makes selected adjustments to the irrigation system and promote proper soil moisture, deter plant disease, and maintain rate of growth of crops. (Par. 0077 and 0036)
Young and Kastl do not teach the amended portion of a stress level determined from a temperature of a crop from thermal images and that there is positive correlation between the stress and temperature. However, Fujiyama teaches wherein the water stress level is determined from a temperature of the crop derived from thermal images of the crop, there being a positive correlation between the water stress level and the temperature of the crop, (Par. 0090: “First storage 207a stores the ambient temperature and the water content in association with each other according to a growth stage of plant PT. Second storage 207b stores the ambient temperature and the timing of water spraying in association with each other according to the growth stage of plant PT. Third storage 207c stores the number of days after plant PT is germinated and a height position of environment sensor 211 in association with each other.” Par. 0176: “Next, control experiment for the water potential contained in the leaf is performed as the observation of the water content contained in the leaf of plant PT by using plant detection camera 1 of the present embodiment, and the sugar content in the leaf due to the water stress obtained by the result of the experiment is considered.” Examiner’s Note – See figures 20A - 20D or 33A – 33D and many paragraphs (such as 0295) establish that crop/plant stress is the synonymous or correlated with water content when interpreting paragraph 0090. Par. 0283: “…plant detection camera 1 can instruct control device 200 to control the ambient temperature and the humidity so as to obtain a water stress profile in which the standardized pixel average water content index or the total sum of the standardized water content index is not increased and decreased in a certain period.” See also Abstract, Par. 0304 (and other paragraphs) that teaches control of temperature which is directly correlated to water content and stress of the plants/crops. )
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have combined the method and system that uses a processor as part of a computer to analyze images and encode the images with geolocation data such as GPS location data and determine the condition of a plant as in Young with using image analysis for plants, determine water stress of the crop or plants and adjust the amount of water or irrigation to the crop/plants in an area as in Kastl with a device and method of irrigation that determines water stress or water content of plants wherein the temperature is associated and has a positive correlation with the water content/stress as in Fujiyama in order to determine the timing of irrigation to the plant and the amount of irrigation. (Par. 0008)
The previous references do not teach a table that contains water stress and irrigation amounts. However, Hyeon does teach that the amount of water being determined from a lookup table that includes an entry for each water stress level and an associated amount of water to apply to the crop…” (Page 5, Par. 6: “The irrigation amount control module 131 corresponds to the water stress index in two or more water stress index sections, and determines the irrigation amount according to the irrigation amount mapping table based on the corresponding two or more water stress index sections. At this time, the determined irrigation amount corresponds to the moisture stress section, and the irrigation amount mapping table defines the relationship between the irrigation amount according to two or more moisture stress index sections.” See also Hyeon claim 7.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have combined the method and system that uses a processor as part of a computer to analyze images and encode the images with geolocation data such as GPS location data and determine the condition of a plant as in Young with using image analysis for plants, determine water stress of the crop or plants and adjust the amount of water or irrigation to the crop/plants in an area as in Kastl with a device and method of irrigation that determines water stress or water content of plants wherein the temperature is associated and has a positive correlation with the water content/stress as in Fujiyama with a table that lists an irrigation amount and corresponding with the water stress as in Hyeon in order to control the amount of water based on water stress of crops. (Page 2, Par. 2)
Regarding claim 2,
The previously cited reference(s) teach the limitations of claim 1 which claim 2 depends. Kastl also teaches that the processing element is further configured or programmed to determine a temperature of the crop from the images of the crop. (Par. 0057: “Further, the drone image analysis may identify visual and/or thermal evidence of disease indicators (e.g., raised temperatures…” See also Fujiyama that teaches determining temperature from images. (Par. 0304.)
Regarding claim 3,
The previously cited reference(s) teach the limitations of claim 2 which claim 3 depends. Kastl also teaches that the processing element is further configured or programmed to determine the water stress level of the crop from the temperature of the crop. (Par. 0056: “…measure field conditions (e.g., crop temperature, moisture, health) and sending data back to the analysis system (e.g., control panel on the pivot) wirelessly where the geolocated data would be processed. For example, the drone sorties may image and identify data the system may analyze to determine a modified crop water stress index which would then be used to modify a parameter of the application by the machine.” Par. 0048: “the system of the present invention may preferably analyze the imaging data and determine anomalies in a monitored area indicating an issue such as pests, diseases, nutrient stress, water stress, lack of emergence, lagging or leading crop development, etc. in an area of the field. At a next step 1208, the system may then determine where blind spots exist and/or where higher resolution imaging would benefit analyzing a detected anomaly. At a next step 1210, where such an area/anomaly is detected, the system of the present invention may initiate a drone sortie to scan the area with a higher resolution or specialized sensor (e.g., camera, thermal imager…”) Par. 0062: “…infrared heat signatures and data from a crop stress index model. Further, remote data may include climate data from climate stations sufficient to compute or estimate evapotranspiration such as temperature…”)
Regarding claim 4,
The previously cited reference(s) teach the limitations of claim 1 which claim 4 depends. Kastl also teaches that the processing element is further configured or programmed to determine one or more crop indices from the images. (Par. 0062: “Such data may include: Geo-tiff images, spectral data including RGB bands, NIR, IR (Thermal), weather-focused radar, radar-based terrain, active and passive microwave imagery for soil moisture and crop growth, and derived indices, such as NDVI, based on these and other individual spectral bands. Further, such data may include evapotranspiration data from satellite heat balance models including infrared heat signatures and data from a crop stress index model.”)
Regarding claim 5,
The previously cited reference(s) teach the limitations of claim 4 which claim 5 depends. Kastl also teaches that the processing element is further configured or programmed to derive the water stress level of the crop from at least one of the images and the indices. (Par. 0048, 0056, and 0064)
Regarding claim 6,
The previously cited reference(s) teach the limitations of claim 1 which claim 6 depends. Kastl also teaches that the electronic signal output by the processing element is received by one or more drive motors, each drive motor configured to propel one of a plurality of mobile towers. (Par. 0094: “The system preferably may also receive pressure transducer data at each tower and then adjust drive units and/or pump systems to maintain proper pressure across all towers.” Par. 0076: “With reference now to FIG. 6, a further example application of the present invention shall now be further discussed. As shown in FIG. 6, the example application concerns the adjustment of drive and VRI systems based on detected system data. As shown, the example data fed into the system may include positional data 602 for a given time (P.sub.1). Further, example data may include torque application data 604 from the drive system 605 (D.sub.1) indicating the amount of torque applied to a drive wheel over a given interval of time (i.e. T+1). With these data inputs, the system of the present invention may preferably calculate the expected position (P.sub.E) of the drive tower 610 after the given interval of time (i.e. T+1). Further, the system may preferably receive detected positional data 612 for the location of the drive tower after the given length of time (i.e. P.sub.2). At a next step 614, the predicted and detected locations are compared and if P.sub.2<P.sub.E, the system at a next step 615 may further calculate a slip ratio (i.e. P.sub.2/P.sub.E) which is then forwarded to the predictive model 624 for analysis.” Par. 0087. )
Regarding claim 7,
The previously cited reference(s) teach the limitations of claim 5 which claim 7 depends. Kastl also teaches that the electronic signal output by the processing element adjusts a speed of one or more drive motors to adjust the amount of water applied to the crop. (Par. 0077: “estimated moisture level of the given annular region may then be forwarded to a processing module 625 which then may use the estimated moisture level to make selected adjustments to the irrigation system. For example, the processing module may calculate a speed correction based on the measured slip ratio which is then outputted 622 to the drive system 605. The speed corrections may further include a comparison of speeds between towers and a calculation of alignments between towers. Further, the processing module may calculate a corrected watering rate 620 which may be outputted to the VRI controller 608. Further, the processing module 625 may output an updated moisture level 618 to be included in system notifications or other calculations.” Par. 0094: “The system preferably may also receive pressure transducer data at each tower and then adjust drive units and/or pump systems to maintain proper pressure across all towers.” Par. 0100 – variable rate prescription for speed or zone or individual sprinkler. Par. 0076. See also Kastl claims 1, 7, 9 and 10.)
Regarding claim 8,
The previously cited reference(s) teach the limitations of claim 1 which claim 8 depends. Young also teaches that the electronic signal output by the processing element is received by at least one of a plurality of valves to adjust a flow rate or a pressure of water through the irrigation system. (Par. 0091: “In an embodiment, examples of controllers 114 that may be used with such apparatus include pump speed controllers; valve controllers that are programmed to control pressure, flow, direction, PWM and the like; or position actuators, such as for boom height, subsoiler depth, or boom position.” See also Kastl Par. 0096: “…the system may react by reducing or turning off one or more valves on the machine to reduce overwatering.” Par. 0083, 0085, and 0088 – water pressure.)
Regarding claim 9,
The previously cited reference(s) teach the limitations of claim 1 which claim 9 depends. Young also teaches that the electronic signal output by the processing element is received by at least one of a plurality of pumps to adjust a flow rate or a pressure of water through the irrigation system. (Par. 0091: “In an embodiment, examples of controllers 114 that may be used with such apparatus include pump speed controllers; valve controllers that are programmed to control pressure, flow, direction, PWM and the like; or position actuators, such as for boom height, subsoiler depth, or boom position.” See also Kastl Par. 0094: “…adjust drive units and/or pump systems to
maintain proper pressure across all towers.” Par. 0083, 0085, and 0088 – water pressure.)
Regarding claim 10,
The previously cited reference(s) teach the limitations of claim 1 which claim 10 depends. Kastl also teaches that the images include thermal images of the crop. (Par. 0048: “thermal imager” Par. 0057: “may identify visual and/or thermal evidence of disease indicators ( e.g., raised temperatures for livestock, excess mucus, low movement activity, disease signs in custom-character). Other uses for drones may preferably include irrigation, monitoring, chemical application of areas not covered by the irrigation equipment. For example, corners of the field on pivots.” Claim 1: “thermal camera”)
Regarding claims 11 - 15, they are directed to a method of steps to implement the system or apparatus set forth in claims 1 – 9, respectively, (Claim 1 to claim 11; claims 2 and 3 to claim 12; claims 4 and 5 to claim 13; claims 6 and 7 to claim 14; and claims 8 and 9 to claim 15). Young and Kastl teach the claimed system or apparatus in claims 1 - 9. Therefore, Young and Kastl teach the method of steps in claims 11 – 15.
Regarding claims 16 and 17, they are directed to a device to implement the system or apparatus set forth in claim 1 with the additional elements regarding the growth stage and a mathematical model. Young and Kastl teach the claimed system or apparatus in claims 1. Young also teaches mathematical models determining growth. (Par. 0060: “…express models using mathematical equations…” Par. 0098: “the agricultural intelligence computer system 130 is programmed or configured to create an agronomic model. In this context, an agronomic model is a data structure in memory of the agricultural intelligence computer system 130 that comprises field data 106, such as identification data and harvest data for one or more fields. The agronomic model may also comprise calculated agronomic properties which describe either conditions which may affect the growth of one or more crops on a field…” Examiner’s Note – Kastl also teaches 50 instances of model(s).) Young and Kastl also teach a growth stage: (Young – Par. 181: “In some embodiments, image recognition and classification may generate output for evaluating plant growth stages, plant health, fungal infection, and the presence of diseases or pests. Other embodiments may use processing focused on weed eradication, physically disrupting weeds using electromechanical actuators or chemically treating fields.” Par. 0172 – determining growth stage using images; Par. 0128 – digital images including plant condition with geolocation data. Kastl – Par. 0062 - using images with geographical information for crop growth or growth stage.) Therefore, Young and Kastl teach the method of steps in claims 16 and 17.
Regarding claims 18 – 20, they are dependent on claim 16 and are directed to a device to implement the system or apparatus set forth in claims 6 – 9, respectively - (Claim 6 to claim 18; claim 7 to claim 19; and claims 8 and 9 to claim 20.) Young and Kastl teach the claimed system or apparatus in claims 16 and 6 – 9. Therefore, Young and Kastl teach the method of steps in claims 18 – 20.
Response to Arguments
Applicant’s arguments with respect to all claims have been considered but are moot because the arguments do not apply in light of the new reference being used in the current rejection necessitated by amendment. Examiner is persuaded by applicant’s argument that Young and Kastl do not teach the elements as amended by applicant on 07/13/2026. However, for claims 1 and 11 the new references of Fujiyama and Hyeon teach the amended elements of a temperature and water stress level correlation and a table that includes water stress and water amounts. The references are appropriately combinable as they teach concepts of water stress and relates the stress to elements in the instant application to determine the correct amount of irrigation to plants and crops.
For unamended claim 16, Examiner is not persuaded that Young and Kastl do not teach determining a growth stage of the crop from a mathematical model that models the crop. Young teaches in paragraph 0060: “…a crop model may include a digitally constructed model of the development of a crop on the one or more fields.” Young further defines the model as: “an electronic digitally stored set of executable instructions and data values, associated with one another, which are capable of receiving and responding to a programmatic or other digital call, invocation, or request for resolution based upon specified input values, to yield one or more stored or calculated output values that can serve as the basis of computer-implemented recommendations, output data displays, or machine control, among other things. Persons of skill in the field find it convenient to express models using mathematical equations…” Young also teaches in paragraph 0060 that the model(s) may be based on locations or fields as claimed. Clearly, as Young teaches using a mathematical crop model of the development of a crop is on point with the instant applications claimed elements of claim 16 of a mathematical model that models the crop. Paragraph 0155 of Young also teaches that images are applied to a model and that the model is used to detect differences in plants. And as cited previously, Young teaches that images are used to assess plant growth. See also paragraphs 0071, 0074, and 0098 of Young.
Kastl also teaches in paragraph 0007 that a predictive model is created for each object in a crop field and other paragraphs such as 0003, 0036, 0064, and 0068 teach that growth stages are monitored. The predictive model which is based on characteristic data as taught in Kastl clearly would be applied to the growth stage as implied and taught in paragraph 0007 and supported by paragraphs 0062 – 0064 which includes characteristic data as crop growth. Thus Young and Kastl clearly teach the model element of claim 16.
Even if one were not persuaded that Young and Kastl does not teach the elements of claim 16, Garg, cited below, teaches determining growth stages of crops from models in geographic regions as cited below.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Garg (US PG Pub. No. 20180330435) may also teach some elements of claim 16 where growth stages are modelled given geolocations. (Par. 0033 and 0034: “In the foregoing implementations, a crop identification model can also: take a date or time of year as an input; estimate a growth stage of crops in the geographic region based on this time of year and known seasonal variations in this location (e.g., based on whether the farm is in the northern or southern hemisphere); and output a strength of correspondence between a cluster of pixels in the region of interest and a particular crop type based on known features of this particular crop type at this growth stage. More specifically, the system can input the current date or time of year into a crop identification model in order to calculate a strength of correspondence—between a cluster of pixels and a particular crop type—that addresses changes in sizes and emission spectra (e.g., colors) of a crop throughout its growth cycle. [0034] Alternatively, the system can select and implement a temporal crop identification model—tailored to a certain subset of the growth stage of a particular crop type and configured to identify a particular crop within a particular stage of growth—based on the current date, time of year, or growth season in the geographic region in which the farm is located.” See also Par. 0100.)
O'Shaughnessy et al. (US Patent No. 9,866,768) is related to the instant application of using images to reduced false positive irrigation scheduling and increased efficiency and cost efficacy of irrigation as it relates to successfully increasing plant yields per unit of water applied. (Col. 4, lines 34 – 37). O'Shaughnessy discloses using image analysis to determine plant stress and water stress in the plants. (Abstract and Col. 3, line 52 – Col. 4, line 6).
Klemm et al. (US PG Pub. No. 20210345567) may also teach the amended portions of wherein the water stress level is determined from a temperature of the crop derived from thermal images of the crop, there being a positive correlation between the water stress level and the temperature of the crop… (Par. 0032: “A further aspect of the invention provides method of plant stress determination using a computer-based camera system having visible, near infrared, shortwave infrared, and thermal infrared imaging capability to capture foliage at close proximity of at least one plant to provide high resolution images/video thereof; analyzing selections of composites from said visible and infrared bands in images/video; determining the water stress, leaf water content, leaf pigment condition and photosynthetic activity from the composite image/video of said at least one plant; and deriving the plant stress from said determination.” Par. 0070: “In an additional embodiment, the camera system 84 may also include a near infrared and/or a shortwave infrared imaging device. Such a device may allow leaf water content information to be determined. The camera system 84 proposed takes advantages of both crop growth/vigour and water stress information measured simultaneously in the close range from the target crop. By combining visible, near infrared, and thermal infrared images along with on-site ancillary information such as local meteorological data, crop type/growth, and portable calibration target, the device can produce plant-by-plant estimates of crop water stress, crop vigour, and water consumption with a minimal reliance on empirical crop parameters.”)
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 CHAD G ERDMAN whose telephone number is (571)270-0177. The examiner can normally be reached Mon - Fri 7am - 3pm or 4pm EST.
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, Kenneth Lo can be reached at (571) 272-9774. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHAD G ERDMAN/Primary Examiner, Art Unit 2116
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