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 § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Miresmailli et al. (2021/0072210).
With respect to claim 1, Miresmailli et al. teaches a computer-implemented method, comprising: receiving sensor data (at 140) from a number of sensors (112) within a grow operation (i.e. a crop site); determining current data values (i.e. sensor data related to plants in the crop field; [0059]) for a number of attributes based on the sensor data (for example specific chemical volatile indicators, plant health, disease, etc. [0073]), the current data values associated with locations within the grow operation (as the sensor platform tags location data with the sensor measurements; [0064]); identifying, based at least in part on one or more pre-trained machine learning (ML) models, one or more regions within the grow operation potentially associated with a condition (as Miresmailli et al. teaches using data mining and machine learning technique to generate predictive models and using the ML models’ predictions to determine the plants health and intervention actions at that plants location; [0099]); providing instructions (data gathering actions to intervention actions; [0099-0100]) to at least one robotic device (510) to perform a data gathering operation (using the onboard sensors) of the one or more regions (determined by the models to have certain conditions occurring); and determining, based at least in part on information collected during the data gathering operation (from the sensors on board robot 510) and the one or more pre-trained ML models (found in the DPU), whether the condition is present in the one or more regions (as based on the classification occurring in the DPU using the ML models and collected data, further screening was requested; [0103]).
With respect to claim 18, Miresmailli et al. teaches a non-transitory computer-readable media (as indirectly taught in [0065]) collectively storing computer- executable instructions (i.e. software; 146) that upon execution cause one or more computing devices (140) to collectively perform acts, i.e. the steps rejected in the method of claim 1.
With respect to claim 2, Miresmailli et al. teaches the computer-implemented method wherein the robotic device comprises an unmanned aerial vehicle (UAV) or is incorporated into the UAV (as Miresmailli et al. teaches 510 is a drone; [0079]).
With respect to claim 3, Miresmailli et al. teaches the computer-implemented method wherein the data gathering operation comprises at least one of: (i) a scouting operation (as [00545] describes a system that provides an automated scouting operation without the need for human scouts; [0054]).
With respect to claim 4, Miresmailli et al. teaches the computer-implemented method wherein the number of sensors (120) comprises a light sensor (i.e. a full light spectrum sensor; [0011]), humidity sensor (i.e. a humidity sensor module; [0012]), or image sensors (i.e. for examiner thermal image sensing; [0012]).
With respect to claim 5, Miresmailli et al. teaches the computer-implemented method wherein the number of sensors (120) comprise at least one image capture device (i.e. either thermal imaging or imaging devices so a user can inspect a video image; [0136]).
With respect to claim 6, Miresmailli et al. teaches the computer-implemented method wherein the at least one image capture device is installed within a second robotic device (as Fig. 11 shows other drones carrying sensors used to collected data around a crop site and to implement corrective actions; [0070]) configured to traverse the grow operation [0110].
With respect to claim 7, Miresmailli et al. teaches the computer-implemented method wherein the condition comprises a disease [0073], infection (viral infection; [0135]), infestation (pest infestations; [0073], or a lifecycle stage of a plant [0119].
With respect to claim 8, Miresmailli et al. teaches the computer-implemented method wherein the condition (like disease) is determined to be present in the one or more regions (i.e. crop sites) upon detecting at least one symptom of the condition (i.e. for a chemical volatile indicator) within one or more images collected by the robotic device (510) during the data gathering operation [0073] [0106].
With respect to claim 9, Miresmailli et al. teaches the computer-implemented method wherein the detecting is further based at least in part on an external weather condition including at least one of: hours of sunlight, time of sunrise, time of sunset, cloudiness, average temperature, average low temperature and average high temperature (insofar as how the recited “detecting is further based” structurally defines the computer-implemented method over the prior art, Miresmailli et al. taught detecting is based on external weather conditions like hours of sunlight, as the hours of sunlight influence a plant’s physiology and thereby a plant stress which will be detected by the sensors 120).
With respect to claim 10, Miresmailli et al. teaches a computing device (140) comprising: a processor (142); and a memory (as indirectly taught for storing the taught software, 146 of 140) including instructions (i.e. software, 146) that, when executed with the processor (142), cause the computing device (140) to, at least: receive sensor data (at 140) from a number of sensors (112) within a grow operation (i.e. a crop environment); determine current data values (i.e. sensor data related to plants in the crop field; [0059]) for a number of attributes based on the sensor data (for example specific chemical volatile indicators, plant health, disease, etc. [0073]), the current data values associated with locations within the grow operation (as the sensor platform 510 tags location data with the sensor measurements; [0064]); identify, based at least in part on one or more pre-trained machine learning (ML) models, one or more regions within the grow operation potentially associated with a condition (as Miresmailli et al. teaches using data mining and machine learning technique to generate predictive models and using the ML models’ predictions to determine the plants health and intervention actions at that plants location; [0099]); provide instructions (data gathering actions to intervention actions; [0099-0100]) to at least one robotic device (510) to perform a data gathering operation (using the onboard sensors) of the one or more regions (determined by the models to have certain conditions occurring); and determine, based at least in part on information collected during the data gathering operation (from the sensors on board robot 510) and the one or more pre-trained ML models (found in the DPU), whether the condition is present in the one or more regions (as based on the classification occurring in the DPU using the ML models and collected data, further screening was requested; [0103]).
With respect to claim 11, Miresmailli et al. teaches the computing device (140) wherein the instructions further cause the computing device (140) to, upon determining that the condition is present (as Miresmailli et al. teaches the information includes crop or plant conditions; [0070]), provide a notification (i.e. an alert; [0070]) of the condition to one or more user device (170a).
With respect to claim 12, Miresmailli et al. teaches the computing device (140) wherein the notification (i.e. alert) comprises a recommended treatment for the condition (i.e. suggested treatment; [0070]).
With respect to claim 13, Miresmailli et al. teaches the computing device (140) wherein the instructions further cause the computing device (140) to, upon determining that the condition is present, initiate a treatment procedure for the condition (as Miresmailli et al. teaches deploying irrigation systems or pesticides; [0070]).
With respect to claim 14, Miresmailli et al. teaches the computing device (140) wherein initiating the treatment procedure for the condition comprises activating one or more automated systems (for example irrigation or pesticides systems; [0070]).
With respect to claim 15, Miresmailli et al. teaches the computing device (140) wherein the one or more automated systems comprise at least one of a sprinkler system (irrigation system; [0070]) or a temperature control system [0070].
With respect to claim 16, Miresmailli et al. teaches the computing device (140) wherein initiating the treatment procedure for the condition comprises providing instructions to a second robotic device (as Fig. 11 shows other sensor carrying drones; [0110]) to cause it to administer a remedy to plants in the one or more regions (for example Miresmailli et al. using the drones to administer bio-control agent; [0070]).
With respect to claim 17, Miresmailli et al. teaches the computing device (140) wherein the instructions further cause the computing device (140) to, following the administer of the remedy (at s1260, Fig. 12), perform an additional data gathering operation of the one or more regions to determine whether the condition is still present (as at s1270, the crops are assed to see how the corrective actions performed to update trends to aid in improving corrective actions in the future; s1290).
With respect to claim 19, Miresmailli et al. teaches the non-transitory computer-readable media [0065], wherein the one or more regions (i.e. crop sites) are identified (i.e. classified) if the current data values (as collected by the sensors 120) have remained within respective attribute data value ranges of a set of attribute data value ranges (i.e. thresholds for difference classifications of attribute data vales ranges from the classifications; [0076]) for at least a predetermined amount of time (during the crop growing life cycle).
With respect to claim 20, Miresmailli et al. teaches the non-transitory computer-readable media [0065] wherein the one or more regions (i.e. crop sites) are identified if a plant type located within the one or more regions is a plant type that is affected by the condition (as Miresmailli et al. is teaches the system is “trained” based on correlating an assessment of the health of individual plants with sensor data captured for the same plants; [0076], therefore it is the position of the Office that Miresmailli et al. teaches if a plant type is identified in a location is a plant type affected by the condition, i.e. disease, corrective actions are implemented; see [0089] [0138]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Richt (2018/007555) which teaches using agronomic models and drones for remote sensing crops.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW G MARINI whose telephone number is (571)272-2676. The examiner can normally be reached Monday-Friday 8am-5pm.
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, Stephen Meier can be reached at 571-272-2149. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MATTHEW G MARINI/Primary Examiner, Art Unit 2853