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 .
Claims 1, 3-6, 8-12, 14-22, and 31-37 are currently pending.
Claim Objections
The claims are objected to because of the following informalities: claim 11, line 3, “plurality of moveable carts” should read “plurality of translatable carts” as recited in claim 1 for consistency. Appropriate correction is required.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3, 5-6, 9, 11-12, 14-20, 22, 32, and 35-37 are rejected under 35 U.S.C. 103 as obvious over Creechley (US 20180014486), hereinafter referred to as “Creechley” in view of Itzhaky et al. (US 20160148104 A1), hereafter referred to as “Itzhaky”.
Regarding claim 1, Creechley teaches an autonomous farming system (1000, 1001; see also paragraphs [0002],[0043],[0071],[0081] & figs. 1A-3) comprising:
at least one indoor farming module (100, 104; paragraphs [0008],[0076],[0081]) including one or more plant sensors (615, 625, paragraphs [0075],[0082],[0097], figs. 10A-B) and one or more environmental sensors (615, 625, paragraph [0076]), the one or more plant sensors comprising at least one imaging device (paragraphs [0075], [0126]);
an irrigation system (300, 310) configured to deliver water to one or more plants within the indoor farming module, the irrigation system further configured to deliver nutrients to the one or more plants at various positions within the indoor farming module (paragraphs [0010], [0086], fig. 9);
an air circulation system (400) configured to deliver conditioned air to the one or more plants withing the indoor farming module at a humidity level (paragraphs [0043],[0088]):
a lighting system (108) configured to deliver light energy to the one or more plants within the indoor farming module at an intensity and light spectrum level (paragraphs [0042]-[0043]): and
a support structure (101, 103; fig. 3) positioned in the at least one indoor farming module (fig. 3), the support structure comprising a plurality of horizontal and vertical frame members (fig. 3);
a plurality of horizontally translatable carts (202, 204 are carriers for elements 102 and are horizontally driven; figs. 8A-8B and paragraph [0153]) supported on at least one of the plurality of horizontal frame members (fig. 8B, e.g., showing 202, 204 supported on 103) each cart carrying a tray with a plurality of plants (each 202, 204, carrying multiple elements 104);
a tray-handling system (102; fig. 4) configured to transfer the one or more plants into or out of the at least one indoor farming module (fig. 4 showing 102 unloads and loads elements 104); and
a computing device (635, paragraphs [0008],[0076],[0096], figs. 10A-C) coupled to the at least one indoor farming module and the tray-handling system (paragraph [0076]), the computing device configured to:
obtain, via the one or more plant sensors, plant characteristic data indicative of growth and health of the one or more plants (20) growing in the at least one indoor farming module (paragraphs [0014], [0075], [0082], [0126]; claims 8-10) wherein the plant characteristic data comprises image data collected by a vision system in the growing module (paragraph [0075]);
obtain, via the one or more environmental sensors, environmental data characteristic of environmental conditions in the at least one indoor farming module (paragraphs [0071] and [0076]);
designate the one or more plants with a plant health category (paragraphs [0075], [0081], [0109], e.g., teaching that the plant is either categorized as deficient or not deficient) and determine a deficiency (paragraph [0075], i.e., measuring leaf area index, weight, sugar content, water content, acidity, or other properties of the crop plant & paragraph [0109] teaching generating instructions for adjustment of growing conditions based on the measured sensor data, teaching that the controller is configured to first determine a deficiency) of the one or more plants based on the plant characteristic data using a farming engine when the plant health category is determined to be a deficient plant health (paragraphs [0008], [0042], [0076]-[0079], [0081]);
determine at least one farming action based on the determined deficiency and the environmental data, the at least one farming action comprising an action to improve the determined deficiency (paragraphs [0077]-[0079], [0081], e.g., teaching determining and sending instructions to improve a deficiency based on sensor data);
send the at least one farming action to a farming controller (600) operatively coupled to the at least one indoor farming module (paragraphs [0078]-[0079], [0081]),
determine an effectiveness of the remedial action (paragraphs [0078]-[0079], [0081]).
As shown above, Creechley teaches determining a deficiency (paragraphs [0075], [0078]-[0079], [0109]), but does not explicitly teach that the deficiency is a nutrient deficiency, and the computing device is configured to determine at least one nutrient deficiency of the one or more plants using the farming engine. Itzhaky teaches an autonomous faming system (100, abstract, claim 1) including a computing device (150, paragraphs [0038], [0081]) configured to determine at least one nutrient deficiency of one or more plants (paragraphs [0021], [0043], [0045]) using a farming engine (110, 130, paragraph [0021], [0032], [0052]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Creechley such that the computing device is configured to determine at least one nutrient deficiency of the one or more plants using the farming engine, as taught by Itzhaky, in order to further optimize the system by avoiding crop loss and disease and to improve crop outcomes.
Regarding claim 3, Creechley as modified by Itzhaky teaches the autonomous farming system of claim 1, and Itzhaky further teaches wherein the nutrient deficiency is a potassium deficiency (paragraph [0021]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Creechley as modified by Itzhaky to have the nutrient deficiency comprise a potassium deficiency, as further taught by Itzhaky, in order to mitigate poor development of plant crop, as it is known in the art that potassium is a vital nutrient for plant growth and affects photosynthesis and water regulation of the plant.
Regarding claim 5, Creechley as modified by Itzhaky teaches the autonomous farming system of claim 1, and Creechley further teaches wherein the farming engine comprises a machine learning model trained using historical plant characteristic data and historical environmental data (claim 15, paragraphs [0014]-[0015],[0114]).
Regarding claim 6, Creechley as modified by Itzhaky teaches the autonomous farming system of claim 1, and Creechley further teaches wherein the farming controller (600) is operatively coupled to the air circulation system (paragraph [0009]), the lighting system (paragraph [0009]), the irrigation system (paragraph [0009]), a vision system (paragraph [0075], [0127]-[0128]) and a liquid circulation system (paragraph [0009]) in the at least one indoor farming module (see also paragraphs [0110]-[0112] & claim 3) and the at least one farming action includes instructions that cause at least one of the air circulation system, the lighting system, the irrigation system, the vision system and the liquid circulation system to change the environmental conditions in the at least one indoor farming module (paragraphs [0108],[0110]-[0112], [0127]-[0128]).
Regarding claim 9, the combined teachings of Creechley as modified by Itzhaky teaches the autonomous farming system of claim 1, and further teaches wherein the farming engine automatically determines the nutrient deficiency and a completion of the growing process of the plant based on the plant characteristic data (paragraphs [0015], [0126]-[0128] of Creechley and paragraphs [0021] and [0024] of Itzhaky).
Regarding claim 11, Creechley as modified by Itzhaky teaches the autonomous farming system of claim 1, and Creechley further teaches wherein:
each of the plurality of moveable carts (202, 204) is configured to releasably link to an adjacent cart (figs. 8A-8B and paragraphs [0152]-[0153]); and
the plant health category is designated to each tray in the plurality of moveable carts (paragraphs [0071], [0075], [0109], [0129]). As discussed in the analysis of claim 1, Creechley further teaches that the computing device is configured to designate one or more plants to the plant health category (e.g., teaching that the plant is either categorized as deficient or not deficient; paragraphs [0075], [0081], [0109]), and that the plant health category is designated to each tray of a plurality of trays (paragraphs [0113]-[00118], e.g., teaching that the computing device implicitly determines if a tray or module is deficient or not deficient based on sensor data in order to apply a recommended growing condition), but does not explicitly teach that the plant health category comprises an excellent health category, a satisfactory health category, and a deficient health category.
Regarding claim 12, Creechley teaches a method of autonomous farming (abstract) comprising:
providing at least one indoor farming module (100, 104; paragraphs [0008], [0076], [0081]) comprising:
a support structure (101, 103; fig. 3) comprising a plurality of tiers (figs. 2-3);
a plurality of movable carts (202, 204 are carriers for elements 102 and are horizontally driven; figs. 8A-8B and paragraph [0153]) supported horizontally on at least one of the plurality of tiers (figs. 8A-8B), each cart carrying a tray (102) with a plurality of plants (fig. 3, 8A);
an air circulation system (paragraph [0009]), a lighting system (paragraph [0009]), an irrigation system (paragraph [0009]), a vision system (paragraph [0075], [0127]-[0128]) and a liquid circulation system (paragraph [0009]); and
one or more plant sensors (615, 625, paragraphs [0075], [0082],[0097], figs. 10A-B) and one or more environmental sensors (615, 625, paragraph [0076]) each positioned in the indoor farming module and configured to collect information regarding of one or more plants growing in the plurality of trays (paragraphs [0075]-[0076]);
obtaining plant characteristic data (paragraphs [0014],[0075],[0082],[0126]; claims 8-10) from the one or more plant sensors using a computing device (635, paragraphs [0008],[0076],[0096], figs. 10A-C) coupled to the at least one indoor farming module (paragraph [0076]), the plant characteristic data indicative growth and health of the one or more plants growing in at least one indoor farming module (paragraph [0075]);
obtaining, via the one or more environmental sensors, environmental data characteristic of environmental conditions in the at least one indoor farming module (paragraphs [0071] and [0076]);
designate the one or more plants with a plant health category (paragraphs [0075], [0081], [0109], e.g., teaching that the plant is either categorized as deficient or not deficient) and determining at least one deficiency of the one or more plants based on the plant characteristic data (i.e., measuring leaf area index, weight, sugar content, water content, acidity, or other properties of the crop plant & paragraph [0109] teaching generating instructions for adjustment of growing conditions based on the measured sensor data, teaching that the controller is configured to first determine a deficiency) using a farming engine when the plant health category is determined to be a deficient plant health (paragraphs [0008], [0042], [0076]-[0079], [0081]);
determining at least one farming action based on the determined deficiency and the environmental data, the at least one farming action comprising an action to improve the determined deficiency (paragraphs [0077]-[0079], [0081], e.g., teaching determining and sending instructions to improve a deficiency based on sensor data);
sending the at least one farming action to a farming controller (600) operatively coupled to the at least one indoor farming module (paragraphs [0077]-[0079], [0081]); and
determining an effectiveness of the remedial action (paragraphs [0078]-[0079], [0081]).
As shown above, Creechley teaches determining a deficiency (paragraphs [0075], [0078]-[0079], [0109]), but does not explicitly teach that the deficiency is a nutrient deficiency, and determining at least one nutrient deficiency of the one or more plants based on the plant characteristic data using the farming engine.
Itzhaky teaches an autonomous faming method (100, abstract, claim 1) that includes a computing device (150, paragraphs [0038], [0081]) determining at least one nutrient deficiency of one or more plants (paragraphs [0021], [0043], [0045]) based on plant characteristic data using a farming engine (110, 130, paragraph [0021], [0032], [0052]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Creechley to include determining at least one nutrient deficiency of the one or more plants based on the plant characteristic data using the farming engine, as taught by Itzhaky, in order to further optimize the system by avoiding crop loss and disease and improving crop outcomes.
Regarding claim 14, Creechley as modified by Itzhaky teaches the method of claim 12, and Itzhaky further teaches wherein the at least one nutrient deficiency is a potassium deficiency (paragraph [0021]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Creechley as modified by Itzhaky to have the nutrient deficiency comprise a potassium deficiency, as further taught by Itzhaky, in order to mitigate poor development of plant crop, as it is known in the art that potassium is a vital nutrient for plant growth and affects photosynthesis and water regulation of the plant.
Regarding claim 15, Creechley as modified by Itzhaky teaches the method of claim 12, and Creechley further teaches wherein the plant characteristic data comprises a visually observable condition of the one or more plants collected at a plurality of times (paragraph [0075], [0127]-[0128]), but does not explicitly teach that the method includes determining a growth of the at least one nutrient deficiency by comparing the plant characteristic data for each of the plurality of times.
Itzhaky further teaches a method including determining a growth of the at least one nutrient deficiency by comparing the plant characteristic data for each of the plurality of times (paragraphs [0026], [0042]-[0043] and [0070]-[0072]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Creechley and Itzhaky, to include determining a growth of the at least one nutrient deficiency by comparing the plant characteristic data for each of the plurality of times, as further taught by Itzhaky, in order to further improve the health of the plant by monitoring indicators of deficiency over time (paragraphs [0070]-[0072] of Itzhaky).
Regarding claim 16, Creechley as modified by Itzhaky teaches the method of claim 12, and Creechley further teaches wherein the farming engine comprises a machine learning model trained using historical plant characteristic data and historical environmental data (claim 15, paragraphs [0014]-[0015],[0114]).
Regarding claim 17, Creechley as modified by Itzhaky teaches the method of claim 12, and Creechley further teaches wherein the farming controller (600) is operatively coupled to the air circulation system (paragraph [0009]), the lighting system (paragraph [0009]), the irrigation system (paragraph [0009]), the vision system (paragraph [0075]), and the liquid circulation system (paragraph [0009]) in the at least one indoor farming module (see also paragraphs [0110]-[0112] & claim 3).
Regarding claim 18, Creechley as modified by Itzhaky teaches the method of claim 17, and Creechley further teaches wherein the at least one farming action includes instructions that cause at least one of the air circulation system, the lighting system, the irrigation system, the vision system and the liquid circulation system to change the one or more environmental conditions in the at least one indoor farming module (paragraphs [0108],[0110]-[0112], [0127]-[0128]).
Regarding claim 19, Creechley as modified by Itzhaky teaches the method of claim 12, and Creechley further teaches wherein the plant characteristic data comprises image data collected by a vision system in the growing module, the image data including images of the plants in the growing module (paragraphs [0075],[0108],[0126]-[0128]).
Regarding claim 20, Creechley as modified by Itzhaky teaches the method of claim 19, and further teaches wherein the farming engine automatically determines the at least one nutrient deficiency of the plant based on historical image data (paragraphs [0015],[0126]-[0128] of Creechley; see also paragraph [0021] of Itzhaky).
Regarding claim 22, Creechley as modified by Itzhaky teaches the method of claim 12, and Creechley further teaches wherein:
each of the plurality of moveable carts (202, 204) is configured to releasably link to an adjacent cart (figs. 8A-8B and paragraphs [0152]-[0153]); and
the plant health category is designated to each tray in the plurality of moveable carts (paragraphs [0071], [0075], [0109], [0129]). As discussed in the analysis of claim 1, Creechley further teaches that the computing device is configured to designate one or more plants to the plant health category (e.g., teaching that the plant is either categorized as deficient or not deficient; paragraphs [0075], [0081], [0109]), and that the plant health category is designated to each tray of a plurality of trays (paragraphs [0113]-[00118], e.g., teaching that the computing device implicitly determines if a tray or module is deficient or not deficient based on sensor data in order to apply a recommended growing condition), but does not explicitly teach that the plant health category comprises an excellent health category, a satisfactory health category, and a deficient health category.
Regarding claim 32, Creechley as modified by Itzhaky teaches the autonomous farming system of claim 1, and Creechley further teaches wherein the image data is collected at a plurality of times, and the computing device is configured to compare the plant characteristic data for each of the plurality of times (paragraph [0081]), but does not explicitly teach that the computing device is configured to determine a growth of the at least one nutrient deficiency.
Itzhaky further teaches a system including a computing device configured to determine a growth of the at least one nutrient deficiency (paragraphs [0026], [0042]-[0043] and [0070]-[0072]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Creechley and Itzhaky, such that the computing device is configured to determine a growth of the at least one nutrient deficiency, as further taught by Itzhaky, in order to further improve the health of the plant by monitoring indicators of deficiency over time (paragraphs [0070]-[0072] of Itzhaky).
Regarding claim 35, Creechley as modified by Itzhaky teaches the autonomous farming system of claim 1, and Creechley further teaches that the at least one farming action comprises modifying a current growing recipe (paragraphs [0006], [0042], [0078]-[0081], e.g., teaching automatically adjusting growth conditions based on received and analyzed data).
Regarding claim 36, Creechley as modified by Itzhaky teaches the autonomous farming system of claim 1, and Creechley further teaches that the at least one farming action comprises continuing a current growing recipe and current environmental schedule (paragraphs [0078]-[0079] and [0081], teaching that the system necessarily continues a current growing recipe until further data suggest a different action is needed).
Regarding claim 37, Creechley as modified by Itzhaky teaches the autonomous farming system of claim 1, and Creechley further teaches that the at least one farming action comprises acquiring additional plant characteristic data and environmental data (paragraphs [0073], [0081], e.g., teaching that the system continuously acquires sensor data to asses plant progress and growth).
Claims 4 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Creechley in view of Itzhaky, as applied to claims 1 above, and further in view of May et al. (US 20100042234 A1), hereafter referred to as “May.”
Regarding claim 4, Creechley as modified by Itzhaky teaches the autonomous farming system of claim 1, but does not explicitly teach wherein the plant characteristic data comprises a combination of multispectral, hyperspectral, and thermal imaging data.
May teaches an autonomous farming system (abstract) including plant characteristic data comprising a combination of multispectral, hyperspectral, and thermal imaging data (paragraphs [0017]-[0018], [0038], and [0040]; see also claim 1).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Creechley as modified by Itzhaky such that the plant characteristic data comprises a combination of multispectral, hyperspectral, and thermal imaging data, as taught by May, in order to improve the accuracy of detecting nutrient deficiencies and to expand the type of nutrient detection of the plants.
Regarding claim 8, Creechley as modified by Itzhaky teaches the autonomous farming system of claim 1, but does not explicitly teach comprising hyperspectral imaging data for nutrient detection.
May teaches an autonomous farming system (abstract) including hyperspectral imaging data for nutrient detection (paragraphs [0017]-[0018], [0038], and [0040]; see also claim 1).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Creechley as modified by Itzhaky to include hyperspectral imaging data for nutrient detection, as taught by May, in order to improve the accuracy of detecting nutrient deficiencies as hyperspectral imaging allows for precise identification of multiple nutrient deficiencies and diseases.
Claims 10 & 21 are rejected under 35 U.S.C. 103 as being unpatentable over Creechley in view of Itzhaky, as applied to claims 1 & 12 above, and further in view of Mewes et al. (US 20170038749 A1), hereafter referred to as “Mewes”.
Regarding claim 10, Creechley as modified by Itzhaky teaches the autonomous farming system of claim 1, and Creechley further teaches a computing device with machine learning capabilities (paragraphs [0006]-[0007]), but does not explicitly teach wherein the computing device is configured to re-train a machine learning model of the farming engine and replace an initial machine learning model with a re-trained machine learning model when the computing device determines that a performance of the re-trained machine learning model exceeds a performance of the initial machine learning model by using a common data set of plant characteristic data and environmental data and comparing a performance of the re-trained machine learning model and the initial machine learning model to accurately identify known nutrient deficiencies.
Mewes teaches an autonomous farming system wherein the computing device is configured to re-train a machine learning model of the farming engine and replace an initial machine learning model with a re-trained machine learning model when the computing device determines that a performance of the re-trained machine learning model exceeds a performance of the initial machine learning model (paragraphs [0060], [0075]) by using a common data set of plant characteristic data and environmental data and comparing a performance of the re-trained machine learning model and the initial machine learning model to accurately identify known deficiencies (paragraph [0075]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Creechley as modified by Itzhaky to have the computing device is configured to re-train a machine learning model of the farming engine and replace an initial machine learning model with a re-trained machine learning model when the computing device determines that a performance of the re-trained machine learning model exceeds a performance of the initial machine learning model by using a common data set of plant characteristic data and environmental data and comparing a performance of the re-trained machine learning model and the initial machine learning model to accurately identify known nutrient deficiencies, as taught by Mewes, in order to continuously improve the model for better optimization of the farming system as taught by Mewes (paragraph [0075]).
Regarding claim 21, Creechley as modified by Itzhaky teaches the method of claim 12, and Creechley further teaches a computing device with machine learning capabilities (paragraphs [0006]-[0007]), but does not explicitly teach further comprising re-training an initial machine learning model of the farming engine and replacing the initial machine learning model with a re-trained machine learning model when the computing device determines that a performance of the re-trained machine learning model exceeds a performance of the initial machine learning model by using a common data set of plant characteristic data and environmental data and comparing a performance of the re-trained machine learning model and the initial machine learning model to accurately identify known nutrient deficiencies.
Mewes teaches a method comprising re-training an initial machine learning model of the farming engine and replacing the initial machine learning model with a re-trained machine learning model when the computing device determines that a performance of the re-trained machine learning model exceeds a performance of the initial machine learning model (paragraphs [0060], [0075]) by using a common data set of plant characteristic data and environmental data and comparing a performance of the re-trained machine learning model and the initial machine learning model to accurately identify known deficiencies (paragraph [0075]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Creechley as modified by Itzhaky to comprise re-training an initial machine learning model of the farming engine and replacing the initial machine learning model with a re-trained machine learning model when the computing device determines that a performance of the re-trained machine learning model exceeds a performance of the initial machine learning model by using a common data set of plant characteristic data and environmental data and comparing a performance of the re-trained machine learning model and the initial machine learning model to accurately identify known nutrient deficiencies, as taught by Mewes, in order to continuously improve the model for better optimization of the farming system as taught by Mewes (paragraph [0075]).
Claim 31 is rejected under 35 U.S.C. 103 as being unpatentable over Creechley in view of Itzhaky, as applied to claim 1 above, and further in view of Martin (US 20180007845 A1), hereafter referred to as “Martin”.
Regarding claim 31, Creechley as modified by Itzhaky teaches the autonomous farming system of claim 1, but does not explicitly teach wherein the image data comprises information regarding a root system of the one or more plants.
Martin teaches an autonomous farming system (figs. 1-27D) including image data comprises information regarding a root system of one or more plants (claim 33).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Creechley as modified by Itzhaky to have the image data comprise information regarding a root system of the one or more plants, as taught by Martin, in order to gain further insight regarding the plant’s development.
Claim 33 is rejected under 35 U.S.C. 103 as being unpatentable over Creechley in view of Itzhaky, as applied to claim 1 above, and further in view of “Boron Deficiency Precisely Identified on Growth Stage V4 of Maize Crop Using Texture Image Analysis”, hereafter referred to as “Luz.”
Regarding claim 33, Creechley as modified by Itzhaky teaches the autonomous farming system of claim 1, but does not explicitly teach wherein the computing device is configured to determine if the at least one nutrient deficiency is a Boron deficiency by determining if the plant has distorted lateral branching based on the plant characteristic data.
Luz teaches a system (pages 1-32) including a computing device configured to determine if at least one nutrient deficiency is a Boron deficiency by determining if a plant has distorted texture based on plant characteristic data (page 10, line 15-page 14, line 8).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Creechley as modified by Itzhaky such that the computing device is configured to determine if the at least one nutrient deficiency is a Boron deficiency by determining if the plant has distorted texture based on the plant characteristic data, as taught by Luz, in order to mitigate a Boron deficiency in the plants to facilitated essential cell wall structure for increased plant yield and quality (Luz, page 7, lines 1-18).
Luz further teaches that an increase in lateral branching is an indicator for Boron deficiency (page 14, line 21-page 15, line 3). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Creechley as modified by Itzhaky such that the computing device is configured to determine if the at least one nutrient deficiency is a Boron deficiency by determining if the plant has distorted lateral branching on the plant characteristic data, in order to further improve the detection of a Boron deficiency by including a known Boron deficiency indicator in the determination (page 14, line 21-page 15, line 3 of Luz).
Claim 34 is rejected under 35 U.S.C. 103 as being unpatentable over Creechley in view of Itzhaky, as applied to claim 1 above, and further in view of “Detection of Nutrient Deficiencies in Plant Leaves using Image Processing”, hereafter referred to as “Minni.”
Regarding claim 34, Creechley as modified by Itzhaky teaches the autonomous farming system of claim 1, but does not explicitly teach wherein the farming engine is configured to designate the one or more plants with the plant health category comprising an excellent health category, a satisfactory health category, and a deficient health category based on the plant characteristic data using clustering or binning techniques.
Minni teaches a method (pages 84-87) including a farming engine configured to configured to designate the one or more plants with a plant health category based on the plant characteristic data using clustering techniques (abstract, pages 85-86).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Creechley as modified by Itzhaky such that the farming engine is configured to designate the one or more plants with the plant health category based on the plant characteristic data using clustering techniques, as taught by Minni, in order to further improve the yield and quality of the plants by allowing the identification of multiple deficiencies based on the plant characteristic data (pages 85-86 of Minni).
Itzhaky further teaches a system including plant health categories (313) directed to a degree of a plant’s health comprising an excellent health category (paragraph [0052], i.e., “healthy”), a satisfactory health category paragraph [0052], i.e., “early indicators”), and a deficient health category (paragraph [0052], e.g., “has disease A”).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Creechley as modified by Itzhaky and Minni, such that the plant health category comprises an excellent health category, a satisfactory health category, and a deficient health category, as further taught by Itzhaky, in order to account for varying degrees of a plant’s health to improve the ability to anticipate treatments.
Response to Arguments
Applicant's arguments filed 05/11/2026 have been fully considered but they are not persuasive and/or have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. It is noted that there are two sets of Remarks dated 5/11/2026, and the document with 4 pages appears to be identical to the Remarks dated 02/11/2026, which were addressed in the Advisory Action dated 03/19/2026.
It is further noted that there are also two sets of claim amendments dated 5/11/2026, where the amendment, in independent claims 1 and 12 positively recites a plurality of horizontally translatable/moveable carts supported on at least one of the plurality of horizontal frame members. As shown in the rejection of claims 1 and 12 above, Creechley teaches a plurality of horizontally translatable/moveable carts at 202 and 204, which serve as carriers for tray elements 102 and 104.
Conclusion
The cited prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure. The references have many of the elements in the applicant’s disclosure and claims. For example, US-20200352113-A1 teaches a similar system and method with a plurality of horizontally translatable carts carrying plants.
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/H.J.B./Examiner, Art Unit 3643
/MARISA V CONLON/Primary Examiner, Art Unit 3643
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