DETAILED ACTION
Claims 1-28 are pending in the present application and are under examination on the merits. This communication is the first action on the merits (FAOM).
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 .
Information Disclosure Statement
Applicant filed an Information Disclosure Statement (IDS) on 11/27/2024, 12/10/2024, and 12/11/2024. This filing is in compliance with 37 C.F.R. 1.97.
As required by M.P.E.P. 609(C), the applicant's submission of the Information Disclosure Statement is acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P. 609(C), a copy of the PTOL -1449 form, initialed and dated by the examiner, is attached to the instant office action.
Drawings
The drawings filed on 8/30/2024 are acceptable as filed.
Claim Rejections - 35 USC§ 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-10, 14-24, and 26-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Here, under considerations of the broadest reasonable interpretation of the claimed invention, Examiner finds that the Applicant invented a method and system for generating a thinning policy and associated model. Examiner formulates an abstract idea analysis, following the framework described in the MPEP, as follows:
Step 1: The claims are directed to a statutory category, namely a "method" (claims 16-24 and 26-28) and "system" (claims 1-10 and 14-15).
Step 2A - Prong 1: The claims are found to recite limitations that set forth the abstract idea(s), namely, regarding claim 1:
generate a thinning policy, wherein said policy is based on analysis of data selected from the group consisting of strain/cultivar, geographical area, topographic, soil, farming practice, climate, season, target market preferences, trade and combination thereof, wherein said processing unit generates output derived from said thinning policy..
Independent claim 16 recites substantially similar claim language.
The claims are further found to recite limitations that set forth the abstract idea(s), namely, regarding claim 14:
receiving input … generate at least one model of a structure of at least one hidden section of a plant part based on said input
Dependent claims 2-10, 15, 17-24, and 26-28 recite the same or similar abstract idea(s) as independent claims 1, 14, and 16 with merely a further narrowing of the abstract idea(s) to particular data characterization and/or additional data analyses performed as part of the abstract idea.
The limitations in claims 1-10, 14-24, and 26-28 above falling well-within the groupings of subject matter identified by the courts as being abstract concepts, specifically the claims are found to correspond to the category of:
"Certain methods of organizing human activity- fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)" as the limitations identified above are directed to generating a thinning policy and associated model and thus is a method of organizing human activity including at least commercial or business interactions or relations and/or a management of user personal behavior.
Step 2A - Prong 2: Claims 1-10, 14-24, and 26-28 are found to clearly be directed to the abstract idea identified above because the claims, as a whole, fail to integrate the claimed judicial exception into a practical application, specifically the claims recite the additional elements of:
"A system for controlling plant reproductive structures thinning, said system comprising: a processing unit; and a non-transitory media readable by said processing unit, the media storing instructions that when executed by said processing unit, causes said processing unit to / a pruning unit for hidden plant parts comprising: at least one plant parts detection unit; a processing unit receiving input from said plant parts detection unit; and a non-transitory media readable by said processing unit, the media storing instructions that when executed by said processing unit, causes said processing unit to … input from said plant parts detection unit" (claims 1, 14, and 16) however the aforementioned elements merely amount to generic components of a general purpose computer used to "apply" the abstract idea (MPEP 2106.0S(f)) and thus fails to integrate the recited abstract idea into a practical application, furthermore the high-level recitation of receiving data from a generic "processing unit" is at most an attempt to limit the abstract to a particular field of use (MPEP 2106.0S(h), e.g.: "For instance, a data gathering step that is limited to a particular data source (such as the Internet) or a particular type of data (such as power grid data or XML tags) could be considered to be both insignificant extra-solution activity and a field of use limitation. See, e.g., Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (limiting use of abstract idea to the Internet); Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data); Intellectual Ventures I LLC v. Erie lndem. Co., 850 F.3d 1315, 1328-29, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017) (limiting use of abstract idea to use with XML tags).") and/or merely insignificant extra-solution activity (MPE 2106.05(g)) and thus further fails to integrate the abstract idea into a practical application;
Step 2B: Claims 1-10, 14-24, and 26-28 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as described above with respect to Step 2A Prong 2 merely amount to a general purpose computer that attempts to apply the abstract idea in a technological environment (MPEP 2106.0S(f)), including merely limiting the abstract idea to a particular field of use of generating a thinning policy and associated model using a "processing unit", as explained above, and/or performs insignificant extra-solution activity, e.g. data gathering or output, (MPEP 2106.0S(g)), as identified above, which is further found under step 2B to be merely well-understood, routine, and conventional activities as evidenced by MPEP 2106.0S(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, electronically scanning or extracting data from a physical document, and a web browser's back and forward button functionality). Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea directed to generating a thinning policy and associated model.
Claims 1-10, 14-24, and 26-28 are accordingly rejected under 35 USC§ 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea(s)) without significantly more.
Note: The analysis above applies to all statutory categories of invention. As such, the presentment of any claim otherwise styled as a machine or manufacture, for example, would be subject to the same analysis.
Claims 11-13 and 25 have been considered under the same framework as implemented above and have been found to contain eligible subject matter.
For further authority and guidance, see:
MPEP § 2106
https://www.uspto.gov/patents/laws/examination-policy/subject-matter-eligibility
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102(A)(1) 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.
Claims 1-3 and 16-18 are rejected under 35 U.S.C. 102(A)(1) as being anticipated by U.S. Patent Application Publication Number 2020/0068807 to Gueret et al. (hereafter referred to as Gueret).
As per claim 1, Gueret teaches:
A system for controlling plant reproductive structures thinning, said system comprising: a processing unit; and a non-transitory media readable by said processing unit, the media storing instructions that when executed by said processing unit, causes said processing unit to (Paragraph Numbers [0005]-[0006] teach the present disclosure also provides a computer-readable storage medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations in accordance with implementations of the methods provided herein. The present disclosure further provides a system for implementing the methods provided herein. The system includes one or more processors, and a computer-readable storage medium coupled to the one or more processors having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations in accordance with implementations of the methods provided herein).
generate a thinning policy, wherein said policy is based on analysis of data selected from the group consisting of strain/cultivar, geographical area, topographic, soil, farming practice, climate, season, target market preferences, trade and combination thereof, wherein said processing unit generates output derived from said thinning policy. (Paragraph Numbers [0020]-[0029] teach the back-end system 108 hosts a horticulture support platform in accordance with implementations of the present disclosure. More particularly, and as described herein, a user 120 can use the computing device 102 to collect data on cultivar. Example cultivar can include, without limitation, plants, trees, and vines. Example data can include, without limitation, images, video, location, terrain context, and terrain shape. In some implementations, the data is processed by the horticulture support platform to provide a multi-dimensional model of the cultivar, and determine an encoding of the cultivar based on the multi-dimensional model. In some implementations, the encoding is processed to predict future growth of the cultivar, and one or more actions are recommended for acting on the cultivar. Example actions can include identifying portions, and locations of the cultivar for pruning. It is contemplated, however, that implementations of the present disclosure can be realized with any appropriate cultivar. The data is processed by the horticulture support platform to provide a multi-dimensional model of the cultivar. In some examples, the multi-dimensional model is provided as a three-dimensional (3D) model of the cultivar. In some implementations, the multi-dimensional model is provided based on stereoscopic images of the cultivar. FIG. 3A depicts example stereoscopic images of an example cultivar over time. In the example of FIG. 3A, an image set 302 is provided, and includes images 302a, 302b. For example, image 302a can be a left-side image, and image 302b can be a right-side image of a cultivar. In some examples, the image set 302 is provided at a time (e.g., as the user 120 is viewing the cultivar 130), and is stored in computer-readable memory. Paragraph Numbers [0053]-[0057] teach one or more actions are recommended for acting on the cultivar. In some implementations, a database of actions can be provided (e.g., the database 212 of FIG. 2), and can include a set of actions that can be performed. In some examples, the set of actions correspond to the particular cultivar (e.g., the particular species of Vitis). In some examples, the actions can include, without limitation, pruning of particular sizes of branches (e.g., length, diameter), and pruning features depending on location (e.g., leaves that are within a threshold distance of the ground). It is contemplated that the set of actions can include any appropriate actions in view of the particular cultivar).
As per claim 16, claim 16 recites a method that is substantially similar to that found in claim 1 and is rejected for the same reasons put forth in regard to claim 1
As per claims 2 and 17, Gueret teaches each of the limitations of claims 1 and 16 respectively.
In addition, Gueret teaches:
wherein said thinning policy comprises determination of a desired reproductive structures density, wherein said non-transitory media readable by said processing unit, the media storing instructions that when executed by said processing unit, causes said processing unit to calculate temporal reproductive structures densities based on input received from said reproductive structures detection unit, and wherein said thinning policy comprises inferences based on a gap between said temporal densities and said desired reproductive structures density value. (Paragraph Numbers [0028]-[0033], [0037], [0045]-[0059] teach terrain context can include, without limitation, one or more environmental features. Example environmental features can include, without limitation, types of minerals in the ground, sparsity/density of minerals, direction of sunlight, duration of sunlight, and average rainfall. In some examples, terrain shape can include, without limitation, topography of the location. For example, the terrain shape can indicate whether the cultivar is on a slope, and if so, the degree of slope, and/or location along the slope (e.g., a cultivar higher up on a steep slope may have less water available than a cultivar at a bottom of the slope) In accordance with implementations of the present disclosure, an encoding of the cultivar is determined based on the multi-dimensional model. In some examples, an initial string (“axiom”) is provided, from which iterations of growth modeling begin. In some examples, the L-system includes a mechanism for translating the generated strings into geometric structures. One or more actions are recommended for acting on the cultivar. In some implementations, a database of actions can be provided (e.g., the database 212 of FIG. 2), and can include a set of actions that can be performed. In some examples, the set of actions correspond to the particular cultivar (e.g., the particular species of Vitis). In some examples, the actions can include, without limitation, pruning of particular sizes of branches (e.g., length, diameter), and pruning features depending on location (e.g., leaves that are within a threshold distance of the ground). It is contemplated that the set of actions can include any appropriate actions in view of the particular cultivar).
As per claims 3 and 18, Gueret teaches each of the limitations of claims 1 and 2, and 16 and 17 respectively.
In addition, Gueret teaches:
comprising tree structure detection unit wherein said non-transitory media readable by said processing unit, the media storing instructions that when executed by said processing unit, causes said processing unit to generate temporal models of at least one part of each tree based on input received from said reproductive structures detection unit and said tree structure detection unit (Paragraph Numbers [0028]-[0033], [0045], [0052]-[0056], and [0061] teach terrain context can include, without limitation, one or more environmental features. Example environmental features can include, without limitation, types of minerals in the ground, sparsity/density of minerals, direction of sunlight, duration of sunlight, and average rainfall. In some examples, terrain shape can include, without limitation, topography of the location. For example, the terrain shape can indicate whether the cultivar is on a slope, and if so, the degree of slope, and/or location along the slope (e.g., a cultivar higher up on a steep slope may have less water available than a cultivar at a bottom of the slope) In accordance with implementations of the present disclosure, an encoding of the cultivar is determined based on the multi-dimensional model. In some examples, an initial string (“axiom”) is provided, from which iterations of growth modeling begin. In some examples, the L-system includes a mechanism for translating the generated strings into geometric structures. FIG. 4 depicts the example baseline multi-dimensional model 304, and an example target multi-dimensional model 400. In some implementations, a similarity score can be determined, which represents a difference between the example baseline multi-dimensional model 304, and an example target multi-dimensional model 400 (e.g., the degree of overlap)).
Claims 14 and 15 are rejected under 35 U.S.C. 102(A)(1) as being anticipated by WIPO Patent Application Publication Number 2021/181371 to Viewnetic (hereafter referred to as Viewnetic).
As per claim 1, Viewnetic teaches:
a pruning unit for hidden plant parts comprising: at least one plant parts detection unit; a processing unit receiving input from said plant parts detection unit; and a non-transitory media readable by said processing unit, the media storing instructions that when executed by said processing unit, causes said processing unit to generate at least one model of a structure of at least one hidden section of a plant part based on said input from said plant parts detection unit (Page 3 lines 12-28 teach the first type data collection module comprises one or more first type imaging devices of predetermined first field of view and first resolution and the second type data collection module comprises one or more second type imaging devices of predetermined second field of view narrower than the first field of view and second resolution higher than the first resolution, the characterization data provided by at least one of the one or more first type imaging devices comprising first type image data indicative of one or more plants in the plant growing area and of location of at least one device of the second type imaging devices with respect to said one or more plants in the plant growing area. Page 24 lines 6-10 teach a full 3-dimensional model of all the plants in a crop area may be generated. This model enables analysis of multiple characteristics of the plant for analyzing plant health status and also enables recognizing semi-hidden elements such as fruits and stalks of the plants. Page 51 lines 12-20 teach an improved quality 3 -dimensional model of the plants may be built using multiple images from multiple angles and with multiple levels of exposure. Page 52 lines 1-30 teach the information for pruning and trimming may be transferred to a plant growth control system, as will be described further below, or to a separate system for autonomous branch and leaf pruning, where the system utilizes the information for pruning specific branches and leaves. In some embodiments, the system for autonomous pruning may receive the 3 -dimensional layout of the branch topology and define the optimal trimming locations independently. Page 57 lines 18-30 teach the analyzer 106 is generally a data processing utility and includes functional modules configured and operable to analyze the characterization data and provide as an output the operational, navigation and recommendation data described above. The analyzer 106 includes a communication module 106A, a characterization data processor module 106B, operational data generator 106C, and a treatment plan generator 106D, and includes or is in data communication with a database 106E).
As per claim 15, Viewnetic teaches each of the limitations of claim 14.
In addition, Viewnetic teaches:
wherein said non-transitory media readable by said processing unit, the media storing instructions that when executed by said processing unit, causes said processing unit to define an optimal cutting point in reproductive structures pruning based on said model of a structure of at least one hidden section and said input from said plant parts detection unit (Page 33 lines 1-27 teach the output may include recommendations to the user on changes in irrigation, fertilization, humidity, temperature, lighting, shading, branch pruning, fruit picking, fruit thinning, irrigation and fertilization plan changes and others. These recommended changes may be regarding adjustments to the parameter for the whole cultivated area or to a specific location in the inspected area. The output may also include recommendations for additional data collection by modules including but not limited to high-resolution imaging device, wide-area imaging device, multi- spectral imaging device, UV fluorescence imaging device and others. Page 52 lines 1-30 teach the information for pruning and trimming may be transferred to a plant growth control system, as will be described further below, or to a separate system for autonomous branch and leaf pruning, where the system utilizes the information for pruning specific branches and leaves. In some embodiments, the system for autonomous pruning may receive the 3 -dimensional layout of the branch topology and define the optimal trimming locations independently. Page 57 lines 18-30 teach the analyzer 106 is generally a data processing utility and includes functional modules configured and operable to analyze the characterization data and provide as an output the operational, navigation and recommendation data described above. The analyzer 106 includes a communication module 106A, a characterization data processor module 106B, operational data generator 106C, and a treatment plan generator 106D, and includes or is in data communication with a database 106E).
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 4, 19, 27, and 28 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication Number 2020/0068807 to Gueret et al. (hereafter referred to as Gueret) in view of WIPO Patent Application Publication Number 2021/181371 to Viewnetic (hereafter referred to as Viewnetic).
As per claims 4 and 19, Gueret teaches each of the limitations of claims 1 and 16 respectively.
Gueret teaches generating a thinning policy but does not explicitly teach where the thinning policy takes into consideration size of the fruits and hidden structures on the plants as described by the following citations from Viewnetic:
wherein said policy comprises determination of required fruit size at harvest. (Page 6 line 32 - Page 7 line 4 teaches characterization of the plant status may include one or more of the following, but is not limited to, detection and/or measurement of plant shape, plant height, leaf shape, leaf color, leaf discoloring, leaf orientation and linearity, pest insects, beneficial insects, fungi, insect generated liquid drops, insect webs, flower pollination, fruit size, fruit location and height from the ground, fruit orientation, fruit shape, fruit color and fruit ripeness.).
Both Gueret and Viewnetic are directed to plant pruning. Gueret discloses a generating a thinning policy. Viewnetic improves upon Gueret by disclosing where the thinning policy takes into consideration size of the fruits and hidden structures on the plants. One of ordinary skill in the art would be motivated to further include where the thinning policy takes into consideration size of the fruits and hidden structures on the plants, to efficiently take desired plant characteristics into account for making a planning model. Accordingly, 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 system and method of a generating a thinning policy in Gueret to further utilize where the thinning policy takes into consideration size of the fruits and hidden structures on the plants as disclosed in Viewnetic, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 27, the combination of Gueret and Viewnetic teaches each of the limitations of claim 16.
Gueret teaches generating a thinning policy but does not explicitly teach where the thinning policy takes into consideration size of the fruits and hidden structures on the plants as described by the following citations from Viewnetic:
comprising detecting at least one plant part and generating at least one model of a structure of at least one hidden section of at least one plant part based on said detecting plant part. (Page 3 lines 12-28 teach the first type data collection module comprises one or more first type imaging devices of predetermined first field of view and first resolution and the second type data collection module comprises one or more second type imaging devices of predetermined second field of view narrower than the first field of view and second resolution higher than the first resolution, the characterization data provided by at least one of the one or more first type imaging devices comprising first type image data indicative of one or more plants in the plant growing area and of location of at least one device of the second type imaging devices with respect to said one or more plants in the plant growing area. Page 24 lines 6-10 teach a full 3-dimensional model of all the plants in a crop area may be generated. This model enables analysis of multiple characteristics of the plant for analyzing plant health status and also enables recognizing semi-hidden elements such as fruits and stalks of the plants. Page 33 lines 1-27 teach The output may include recommendations to the user on changes in irrigation, fertilization, humidity, temperature, lighting, shading, branch pruning, fruit picking, fruit thinning, irrigation and fertilization plan changes and others. These recommended changes may be regarding adjustments to the parameter for the whole cultivated area or to a specific location in the inspected area. The output may also include recommendations for additional data collection by modules including but not limited to high-resolution imaging device, wide-area imaging device, multi- spectral imaging device, UV fluorescence imaging device and others. Page 51 lines 12-20 teach an improved quality 3 -dimensional model of the plants may be built using multiple images from multiple angles and with multiple levels of exposure. Page 52 lines 1-30 teach the information for pruning and trimming may be transferred to a plant growth control system, as will be described further below, or to a separate system for autonomous branch and leaf pruning, where the system utilizes the information for pruning specific branches and leaves. In some embodiments, the system for autonomous pruning may receive the 3 -dimensional layout of the branch topology and define the optimal trimming locations independently).
A person of ordinary skill would have been motivated to combine these references for the same reasons put forth in regard to claim 4.
As per claim 28, the combination of Gueret and Viewnetic teaches each of the limitations of claims 16 and 27.
Gueret teaches generating a thinning policy but does not explicitly teach where the thinning policy takes into consideration size of the fruits and hidden structures on the plants as described by the following citations from Viewnetic:
comprising defining an optimal cutting point in reproductive structures pruning based on said model of a structure of at least one hidden section and said detecting at least one plant part (Page 3 lines 12-28 teach the first type data collection module comprises one or more first type imaging devices of predetermined first field of view and first resolution and the second type data collection module comprises one or more second type imaging devices of predetermined second field of view narrower than the first field of view and second resolution higher than the first resolution, the characterization data provided by at least one of the one or more first type imaging devices comprising first type image data indicative of one or more plants in the plant growing area and of location of at least one device of the second type imaging devices with respect to said one or more plants in the plant growing area. Page 24 lines 6-10 teach a full 3-dimensional model of all the plants in a crop area may be generated. This model enables analysis of multiple characteristics of the plant for analyzing plant health status and also enables recognizing semi-hidden elements such as fruits and stalks of the plants. Page 33 lines 1-27 teach The output may include recommendations to the user on changes in irrigation, fertilization, humidity, temperature, lighting, shading, branch pruning, fruit picking, fruit thinning, irrigation and fertilization plan changes and others. These recommended changes may be regarding adjustments to the parameter for the whole cultivated area or to a specific location in the inspected area. The output may also include recommendations for additional data collection by modules including but not limited to high-resolution imaging device, wide-area imaging device, multi- spectral imaging device, UV fluorescence imaging device and others. Page 51 lines 12-20 teach an improved quality 3 -dimensional model of the plants may be built using multiple images from multiple angles and with multiple levels of exposure. Page 52 lines 1-30 teach the information for pruning and trimming may be transferred to a plant growth control system, as will be described further below, or to a separate system for autonomous branch and leaf pruning, where the system utilizes the information for pruning specific branches and leaves. In some embodiments, the system for autonomous pruning may receive the 3 -dimensional layout of the branch topology and define the optimal trimming locations independently).
A person of ordinary skill would have been motivated to combine these references for the same reasons put forth in regard to claim 4.
Claims 5, 6, 8-10, 12, 20, 21, 23 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication Number 2020/0068807 to Gueret et al. (hereafter referred to as Gueret) in view of U.S. Patent Application Publication Number 2021/0307227 to Redden (hereafter referred to as Redden).
As per claims 5 and 20, Gueret teaches each of the limitations of claims 1 and 16 respectively.
Gueret teaches generating a thinning policy but does not explicitly teach implementing a pruning policy via a mobile pruning system as described by the following citations from Redden:
wherein said policy comprises determination of fruit size to be pruned at thinning (Paragraph Number [0017] teaches the method of automated plant necrosis includes capturing an image of a plant field section, identifying individual plants within the image S100, selecting plants for retention from the image S200, removing plants from the plant field section S300, and repeating the aforementioned steps for a following plant field section S400. The plants removed by the method preferably include crops, but can alternatively include weeds or any other suitable plant. Likewise, the plant field sections are preferably crop rows, but can alternatively be weed rows, weed sections, or any other suitable portion of an area containing plants. Plant removal preferably includes inducing plant necrosis, but can alternatively include eliminating the plant or any other suitable method of killing given plants. The method is preferably performed by a system including a detection mechanism and a elimination mechanism. This method affords several benefits over conventional systems. By automating the plant removal process, this method allows for faster plant removal over that of manual methods. Furthermore, automation allows for optimization across the entire field of the retained plants for space, size, density, health, or any other suitable parameter. Automation also allows for quality control by removing the subjectivity of the human that was thinning the plants and improving the consistency of the retained plants. By identifying individual plants, this method allows for individual plant targeting for removal or retention, making the crop thinning process more reliable and the crop thinning results more predictable over that of conventional systems. Paragraph Number [0050] teaches the system 100 can additionally include a processor that functions to select plants for retention, or conversely, select plants for removal. The processor can additionally function to process the image received from the detection mechanism 200 to identify individual, contiguous plants. The processor can additionally function to generate removal instructions for the plants selected for removal. The processor can additionally include memory, wherein the memory can store retained plant information (e.g. position, size, shape, etc.), removed plant information, user preferences, target plant parameters (e.g. target yield, target plant density, target size, target uniformity, etc.), or any other suitable information).
Both Gueret and Redden are directed to plant pruning. Gueret discloses generating a thinning policy. Redden improves upon Gueret by disclosing implementing a pruning policy via a mobile pruning system. One of ordinary skill in the art would be motivated to further include implementing a pruning policy via a mobile pruning system, to efficiently prune plants according to a specific optimized policy. Accordingly, 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 system and method of generating a thinning policy in Gueret to further utilize implementing a pruning policy via a mobile pruning system as disclosed in Redden, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claims 6 and 21, Gueret teaches each of the limitations of claims 1 and 16 respectively.
Gueret teaches generating a thinning policy but does not explicitly teach implementing a pruning policy via a mobile pruning system as described by the following citations from Redden:
wherein said policy comprises inferences derived from input received from at least one characterization and recommendation system, based on user preferences (Paragraph Number [0017] teaches the method of automated plant necrosis includes capturing an image of a plant field section, identifying individual plants within the image S100, selecting plants for retention from the image S200, removing plants from the plant field section S300, and repeating the aforementioned steps for a following plant field section S400. The plants removed by the method preferably include crops, but can alternatively include weeds or any other suitable plant. Likewise, the plant field sections are preferably crop rows, but can alternatively be weed rows, weed sections, or any other suitable portion of an area containing plants. Plant removal preferably includes inducing plant necrosis, but can alternatively include eliminating the plant or any other suitable method of killing given plants. The method is preferably performed by a system including a detection mechanism and a elimination mechanism. This method affords several benefits over conventional systems. By automating the plant removal process, this method allows for faster plant removal over that of manual methods. Furthermore, automation allows for optimization across the entire field of the retained plants for space, size, density, health, or any other suitable parameter. Automation also allows for quality control by removing the subjectivity of the human that was thinning the plants and improving the consistency of the retained plants. By identifying individual plants, this method allows for individual plant targeting for removal or retention, making the crop thinning process more reliable and the crop thinning results more predictable over that of conventional systems. Paragraph Number [0050] teaches the system 100 can additionally include a processor that functions to select plants for retention, or conversely, select plants for removal. The processor can additionally function to process the image received from the detection mechanism 200 to identify individual, contiguous plants. The processor can additionally function to generate removal instructions for the plants selected for removal. The processor can additionally include memory, wherein the memory can store retained plant information (e.g. position, size, shape, etc.), removed plant information, user preferences, target plant parameters (e.g. target yield, target plant density, target size, target uniformity, etc.), or any other suitable information).
A person of ordinary skill would have been motivated to combine these references for the same reasons put forth in regard to claim 5.
As per claim 8, Gueret teaches each of the limitations of claim 1.
Gueret teaches generating a thinning policy but does not explicitly teach implementing a pruning policy via a mobile pruning system as described by the following citations from Redden:
wherein said output comprises output for controlling at least one pruning system (Paragraph Numbers [0036]-[0040] and [0049]-[0050] teach the plant removal instructions are preferably sent by the processor to the elimination mechanism before the elimination mechanism encounters the plant to be removed or retained. The plant removal instructions are preferably sent prior to the estimated future time point, but can alternatively be sent to the elimination mechanism prior to the crop thinning system travelling a distance equivalent to the distance between the detection mechanism and the elimination mechanism. Removing the plants with the elimination mechanism preferably includes operating the elimination mechanism in the plant removal mode at the instructed time point or location. Removing the plants with the elimination mechanism can additionally include operating the elimination mechanism in the standby mode at the respective instructed time point or location. Operating the crop thinning system in plant removal mode can include spraying a removal fluid at a predetermined concentration, operating a cutting mechanism (e.g. a hoe or a scythe), operating an uprooting mechanism, generating directional heat, generating directional electricity, or include any other suitable means of facilitating plant necrosis).
A person of ordinary skill would have been motivated to combine these references for the same reasons put forth in regard to claim 5.
As per claim 9, the combination of Gueret and Redden teaches each of the limitations of claims 1 and 8.
Gueret teaches generating a thinning policy but does not explicitly teach implementing a pruning policy via a mobile pruning system as described by the following citations from Redden:
The system of claim 8, comprising said pruning system (Paragraph Numbers [0036]-[0040] and [0049]-[0050] teach the plant removal instructions are preferably sent by the processor to the elimination mechanism before the elimination mechanism encounters the plant to be removed or retained. The plant removal instructions are preferably sent prior to the estimated future time point, but can alternatively be sent to the elimination mechanism prior to the crop thinning system travelling a distance equivalent to the distance between the detection mechanism and the elimination mechanism. Removing the plants with the elimination mechanism preferably includes operating the elimination mechanism in the plant removal mode at the instructed time point or location. Removing the plants with the elimination mechanism can additionally include operating the elimination mechanism in the standby mode at the respective instructed time point or location. Operating the crop thinning system in plant removal mode can include spraying a removal fluid at a predetermined concentration, operating a cutting mechanism (e.g. a hoe or a scythe), operating an uprooting mechanism, generating directional heat, generating directional electricity, or include any other suitable means of facilitating plant necrosis).
A person of ordinary skill would have been motivated to combine these references for the same reasons put forth in regard to claim 5.
As per claims 10 and 24, Gueret teaches each of the limitations of claims 1 and 2, and 16-18 respectively.
Gueret teaches generating a thinning policy but does not explicitly teach implementing a pruning policy via a mobile pruning system as described by the following citations from Redden:
a computerized controller receiving said output derived from said thinning policy and said temporal reproductive structures densities; at least one preliminary pruning unit controlled by said controller; and at least one primary pruning unit controlled by said controller (Paragraph Numbers [0036]-[0040] and [0049]-[0050] teach the plant removal instructions are preferably sent by the processor to the elimination mechanism before the elimination mechanism encounters the plant to be removed or retained. The plant removal instructions are preferably sent prior to the estimated future time point, but can alternatively be sent to the elimination mechanism prior to the crop thinning system travelling a distance equivalent to the distance between the detection mechanism and the elimination mechanism. Removing the plants with the elimination mechanism preferably includes operating the elimination mechanism in the plant removal mode at the instructed time point or location. Removing the plants with the elimination mechanism can additionally include operating the elimination mechanism in the standby mode at the respective instructed time point or location. Operating the crop thinning system in plant removal mode can include spraying a removal fluid at a predetermined concentration, operating a cutting mechanism (e.g. a hoe or a scythe), operating an uprooting mechanism, generating directional heat, generating directional electricity, or include any other suitable means of facilitating plant necrosis).
wherein said thinning policy comprises determination of a preliminary pruning reproductive structures density threshold, said preliminary pruning unit is activated if said temporal density is above said threshold and operates until said temporal density is below said threshold below which said primary pruning unit is activated and runs until said temporal density reaches said reproductive structures desired density value (Paragraph Number [0050] teaches the system 100 can additionally include a processor that functions to select plants for retention, or conversely, select plants for removal. The processor can additionally function to process the image received from the detection mechanism 200 to identify individual, contiguous plants. The processor can additionally function to generate removal instructions for the plants selected for removal. The processor can additionally include memory, wherein the memory can store retained plant information (e.g. position, size, shape, etc.), removed plant information, user preferences, target plant parameters (e.g. target yield, target plant density, target size, target uniformity, etc.), or any other suitable information. (Examiner notes that the specific kind of density or target size of particular components of a plant are easily chosen and act as a design decision by a person of ordinary skill)).
A person of ordinary skill would have been motivated to combine these references for the same reasons put forth in regard to claim 5.
As per claim 12, the combination of Gueret and Redden teaches each of the limitations of claims 1, 2, and 10.
Gueret teaches generating a thinning policy but does not explicitly teach implementing a pruning policy via a mobile pruning system as described by the following citations from Redden:
The system of claim 10 mounted on a vehicle (Paragraph Number [0051] teaches the systems and methods of the preferred embodiment can be embodied and/or implemented at least in part as a machine configured to receive a computer-readable medium storing computer-readable instructions. The instructions are preferably executed by computer-executable components preferably integrated within a towable vehicle plant-thinning vehicle, a mobile plant-thinning vehicle, an autonomous plant-thinning vehicle, or any other suitable machine or vehicle).
A person of ordinary skill would have been motivated to combine these references for the same reasons put forth in regard to claim 5.
As per claim 23, Gueret teaches each of the limitations of claim 16. Additionally, claim 23 recites claim limitations substantially similar to those found in claims 8 and 9 and is rejected for the same reasons put forth in regard to claims 8 and 9.
Claims 7 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication Number 2020/0068807 to Gueret et al. (hereafter referred to as Gueret) in view of U.S. Patent Application Publication Number 2021/0307227 to Redden (hereafter referred to as Redden) and in further view of U.S. Patent Application Publication Number 2023/0281924 to Trim et al. (hereafter referred to as Trim).
As per claims 7 and 22, the combination of Gueret and Redden teaches each of the limitations of claims 1 and 6, and 16 and 21 respectively.
In addition, Gueret teaches:
wherein said characterization and recommendation system for assisting said processing unit in generating customized thinning policy, takes into account input received ... when filled comprise data selected from the group consisting of strain, geographies, pests, pollination, farming approach, soil/crop sampling, yield, farming practice, target market, and past performance, wherein said characterization and recommendation system further utilizes data input selected from the group consisting of trade, weather, season, topographic, IoT, telemetry, location, aerial/satellite imaging, albedo, market preferences, and crop price forecasts (Paragraph Numbers [0023]-[0030], [0045], [0053]-[0059], and [0063]-[0065] teach a predicted harvest can be determined from each of the predicted multi-dimensional models. In this manner, each predicted harvest represents a respective yield of the cultivar at harvest time, if the action(s) in the respective group of actions were to be performed. In some implementations, the baseline harvest can be compared to each of the expected harvests, and respective impacts can be determined. In some examples, an impact represents an increase, or a decrease in yield as a result of a respective group of actions. In some examples, the group of actions associated with the best impact can be provided as a recommended set of actions. That is, the group of actions are provided as a recommended set of actions to take with respect to the cultivar at the current time (and/or date) in an effort to provide an improved yield at harvest time (e.g., improved with respect to a yield that would result taking no action) (Examiner notes that this teaches at least yield)).
Gueret teaches generating a thinning policy but does not explicitly teach providing a survey to farmers as to how to design the policy as described by the following citations from Trim:
input received from questionnaires disseminated among farmers, wherein said questionnaires (Paragraph Number [0079] teaches in step 250, program 122 requests feedback from the user for further training. In an embodiment, responsive to outputting the finalized rendering of the production plan to the user, program 122 requests feedback from the user for further training.