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 .
Application Status
Claims 1-20 are pending, claims 1-14 are original and have been examined in this application. Claims 15-20 are withdrawn as being drawn to a non-elected invention.
Information Disclosure Statement
As of the date of this action, an information disclosure statement (IDS) has been filed on 10/27/2025, 11/17/2025 and reviewed by the Examiner.
Election/Restrictions
Applicant’s election without traverse of Invention I Claims 1-14 in the reply filed on 02/16/2026 is acknowledged.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 11-14 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by (US 20150351336 A1) to Gilbert.
In regards to claim 1, Gilbert anticipates a field analysis system for surveying a field, comprising: at least one sensor station (Gilbert; sensor stations being the insect detection modules I), wherein the at least one sensor station comprises: an imaging device configured to capture image data of a field under analysis (Gilbert; CMOS Camera 38 which takes images of insects within a specific location in a field); at least one agricultural sensor configured to generate sensor data representative of a condition of the field under analysis (Gilbert; sensors for condition of the field: ambient light 42, ambient temperature 44, GPS 48); and a first wireless transmission device (Gilbert; radio 46); a field data processor receiving and responsive to the captured image data and the sensor data from the at least one sensor station (Gilbert; insect management server 14 connected to gateway GW); and a second wireless transmission device coupled to the field data processor; wherein the first and second wireless transmission devices are configured to: enable communication between the field data processor and the at least one sensor station; and enable communication between the field data processor and a remote access device (Gilbert; the transmission device such which allows for 14 to receive signals from the gateway GW via antenna 152, 154 from the processor 52; see FIG 1 and [0111] and [0072-0075]; where the camera delivers images to the processor 52 which causes the radio 46 to broadcast packets through the gateway GW and are propagated through the network to the insect management server 14, where images are assessed and upon detection of a particular insect type or reaching a threshold parameter, message server 20 may deliver messages by text/email/other means to alert a designated user; texts and email accessed on a remote device).
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In regards to claim 2, Gilbert anticipates the field analysis system of claim 1, wherein the image data comprises images of insects within the field (Gilbert; the camera 38 imaging a grid 36 of trapped insects within the position of insect detection module I in the field), wherein the at least one sensor station comprises a sensor station processor and a sensor station memory device communicatively coupled thereto (Gilbert; processor 52, memory 54).
In regards to claim 11, Gilbert anticipates the field analysis system of claim 2, wherein the sensor station processor and sensor station memory device are part of a single-board computer (Gilbert; such as in FIG 4 being part of the single board with 56, and see FIG 2 with processor and memory being together as part of a single camera radio module).
In regards to claim 12, Gilbert anticipates the field analysis system of claim 1, wherein the at least one agricultural sensor includes a global positioning system (GPS) (Gilbert; GPS 48), a soil pH sensor, a soil moisture sensor, a crop moisture sensor, a humidity sensor, or a temperature sensor (Gilbert; ambient temperature sensor 44).
In regards to claim 13, Gilbert anticipates the field analysis system of claim 1, further comprising a sticky trap in proximity to and in view of the imaging device, the sticky trap being configured to trap insects thereon (Gilbert; grid 36, see FIG 6, having a sticky substance applied to the surface [0053]).
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In regards to claim 14, Gilbert anticipates the field analysis system of claim 1, wherein the at least one sensor station comprises a plurality of sensor stations spaced apart within the field (Gilbert; see FIG 1 with a plurality of sensor stations or insect detection modules I).
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) 3-4, 6, 8, 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over (US 20150351336 A1) to Gilbert in view of (WO 2012054397 A1) to Park.
In regards to claim 3, Gilbert teaches the field analysis system of claim 2, wherein the sensor station memory device stores processor executable instructions that, when executed, configure the sensor station processor to: execute an object detection module for detecting the insects in the captured images (Gilbert; [0070-0071] detection of insects).
Gilbert fails to teach the sensor station processor is configured to: generate insect population data therefrom.
Park teaches herein the sensor station memory device stores processor executable instructions that, when executed, configure the sensor station processor to: execute an object detection module for detecting the insects (Park; EIMD 12 identifies the species of an insect 56 present in the trap, counts the number of different target insects detected, and associates the detection with a time and location), and generate insect population data therefrom (Park; generates a population map 140 based on the insect population monitored in a specific area of the EIMDs).
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 device of Gilbert such that it identifies the species of insect collected in the trap and associates the detection with a time and location, and generates an insect population map for the identified insect based on the locations of the insect detection modules, such as taught by Park. The motivation for doing so would be to allow for the user to estimate and quantify the degree of potential infestation in order to determine an appropriate response, where a denser population may require more pesticides than an area with lesser pesticides.
In regards to claim 4, Gilbert as modified by Park teach the field analysis system of claim 3, further comprising a field data memory device coupled to the field data processor, wherein the field data memory device stores a prescription module that, when executed, configures the field data processor for prescribing a field action which is a function of at least the insect population data (Gilbert; [0133] database 16 associated with server 14 storing configuration data and schedules for sensing and actuation; [0131] data processing application enables the adjustment of pheromone dispersal activity from dispersal module D, [0095] dispersal of the pheromone is according to commands received from the insect management server 14) (Park; where the population is important for estimating treatment; where without population estimation a grower may not apply pesticide if a population of an insect is underestimated, or overapply and waste money and resources if a population is overestimated; therefore using insect populations to inform pest management decisions when/where/how much pesticide treatment to apply).
In regards to claim 6, Gilbert as modified by Park teach the field analysis system of claim 4, wherein the remote access device is a computer or smartphone, and wherein the remote access device is configured to communicate with the field data processor through the cloud server (Gilbert; via web server 18 or messaging server 20 which sends emails and text messages accessible on computers or smartphones, and communications going through the internet 12 or “cloud” as in [0038]).
In regards to claim 8, Gilbert as modified by Park teach the field analysis system of claim 4, wherein the field action includes a chemical type, a chemical quantity, and an application location (Gilbert; [0015] pheromones used to disrupt the insect mating cycle; trap counts used to adjust placement (location), compounds used (type)) (Park; populations being used to inform when to initiate pesticide treatment, where and how much pesticide to apply).
In regards to claim 10, Gilbert as modified by Park teach the field analysis system of claim 3, wherein the insect population data includes an insect count for at least two species of insect (Park; investigates and identifies multiple species of insect, which is at least two).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over (US 20150351336 A1) to Gilbert as modified by (WO 2012054397 A1) to Park as applied to claim 4 above, in further view of (US 20190104715 A1) to Ben Hamozeg.
In regards to claim 5, Gilbert as modified by Park teach the field analysis system of claim 4, wherein the field data processor is configured to access a cloud server (Gilbert; [0038] the internet or the “cloud”), wherein the object detection module is downloaded to the sensor station memory device via the field data processor, and wherein the prescription module is downloaded to the field data memory device (Gilbert; [0095] dispersal according to the schedule is downloaded to each module).
Gilbert fails to explicitly teach the cloud server comprising a training module and a cloud storage module, wherein the object detection module and the prescription module are trained at the training module.
Ben Hamozeg teaches wherein the field data processor is configured to access a cloud server (Ben Hamozeg; [0180] computation performed in the cloud), the cloud server comprising a training module and a cloud storage module (Ben Hamozeg; cloud for processing and having a training database), wherein the object detection module and the prescription module are trained at the training module, wherein the object detection module is downloaded to the sensor station memory device via the field data processor, and wherein the prescription module is downloaded to the field data memory device (Ben Hamozeg; [0168] retraining providing new thresholds which are then signaled back to the main processing unit (downloaded to update the threshold over time)).
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 device of Gilbert as modified by Park such that the cloud server comprises a training and storage module for training the object detection and prescription module of Gilbert, updating the thresholds and downloading them back to their processing units such as taught by Ben Hamozeg. The motivation for doing so would be to utilize a cloud-based machine learning system which allows for the system to automatically update its algorithm and thresholds based on information gathered via the sensors and their locations, and then adjust the systems to be more optimized and more accurate.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over (US 20150351336 A1) to Gilbert as modified by (WO 2012054397 A1) to Park as applied to claim 4 above, in further view of (US 20210279639 A1) to Singh.
In regards to claim 7, Gilbert as modified by Park teach the field analysis stem of claim 4, but fail to explicitly teach wherein the prescription module includes a machine-learning algorithm configured to optimize the field action.
Singh teaches wherein the prescription module includes a machine-learning algorithm configured to optimize the field action (Singh; [0074] where the system uses machine learning to generate a treatment plan based on future and past pressures of treatments; therefore optimizing for a geographic location).
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 device of Gilbert as modified by Park such that the prescription module utilizes a machine-learning algorithm such as taught by Singh. The motivation for doing so would be to improve the system over time as data on the field is collected in order to decrease and mitigate pest pressure on a certain location.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over (US 20150351336 A1) to Gilbert as modified by (WO 2012054397 A1) to Park as applied to claim 3 above, in further view of (US 20200281164 A1) to Lepek.
In regards to claim 9, Gilbert as modifed by Park teach the field analysis system of claim 3, but fail to teach wherein the object detection module comprises a You Only Look Once (YOLO) algorithm.
Lepek teaches wherein the object detection module comprises a You Only Look Once (YOLO) algorithm (Lepek; see chart in [0284] which discusses detection can use a YOLO algorithm to identify an insect).
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 device of Gilbert as modified by Park such that the object detection module utilizes a YOLO algorithm such as taught by Lepek. The motivation for doing so would be to use a preexisting algorithm with high speed and efficiency to process an entire image to identify insects in an image.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20140311014 A1 to Feugier teaches an insect detection and destruction device which uses an imaging system to take images of positions in a field to detect insects, and then killing them. The system uses a pest control statistical data and predicts or calculates the density of insects to find nests and populations, and this information is sent to the system to the predicted areas to destroy insects and prevent emergence of a problem.
US 20170287160 A1 to Freudenberg teaches the use of a mobile device for imaging and analyzing images of pests.
US 20180299842 A1 to Reid teaches a system for analyzing pests, where from a central location, the type and number or density of pests is known. The system also uses past data to adjust the decisions of placement of traps, chemical or pest control agents, dosage rates, and system status. The system uses machine learning to drive future pest control management in combination with external/environmental factors.
US 20190364871 A1 to Hazell uses a trap with a sticky adhesive grid, where trapped insects within the trap are used to estimate population density which can lead to pesticide application.
CN 112715502 B to Bian teaches prediction related information for the population of insects.
US 20230210102 A1 to Gan teaches a YOLO detection framework technology to identify insects.
US 20230210101 A1 to Nguyen teaches a stationary insect detection system which images insects in a field and estimates populations of insects which enables the device to provide a response to select insecticides to control insect populations.
US 20230247978 A1 to Azaria teaches the use of the YOLO algorithm in the cloud.
US 20240020965 A1 to Johns teaches a machine learning algorithm for application of pesticide, training on past data to optimize crop yield and minimize insects.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KATELYN T TRUONG whose telephone number is (571)272-0023. The examiner can normally be reached Monday - Friday: 8-6.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, KIMBERLY BERONA can be reached at (571) 272-6909. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KATELYN T TRUONG/Primary Examiner, Art Unit 3647