DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim 1 is objected to because of the following informalities: claim term “the predetermined site” should recite “a predetermined site” for proper antecedent basis. Appropriate correction is required.
Claims 1 and 7-12 are objected because of the following informalities: all grammar for terms including: configure, generates, obtains, estimates, causes, predicts, determines, provides, adjust, etc. all need proper grammar regarding inflectional endings or suffixes (i.e. generates should be generate). Appropriate correction is required.
Claim 11 is objected to because of the following informalities: the claim term “output indicating” should recite “output an indication” for proper grammar. Appropriate correction is required.
Claim 12 is objected to because of the following informalities: the claim term “provides output indicating a nutrition state of the plant,” should recite “provides output indicating the nutrition state of the plant,” for proper antecedent basis. Appropriate correction is required.
Claim 13 is objected under 37 CFR 1.75 as being a substantial duplicate of claim 1 (See MPEP 608.01(m)). It appears that independent claim 13 is the same substantially as independent claim 1 (apparatus); Examiner recommends writing claim 13 as a non-transitory-computer readable medium storing instructions executed by a processor and stored in a memory, so each independent claim recites a different statutory category. Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f), because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) are: “operation mechanism” in claims 1, 8 11, and 13-14, and “imaging mechanism” in claims 6 and 9.
Because these claim limitation(s) are being interpreted under 35 U.S.C. 112(f), they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation(s) to avoid them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f).
Regarding claims 7, it recites claim language similar to “at least one of A, B, and C” which triggers a conjunctive claim interpretation under SuperGuide Corp. v. DirecTV Enters., Inc., 358 F.3d 870 (Fed. Cir. 2004) assuming there is specification support for the conjunctive; otherwise, a disjunctive interpretation is taken. For examination, Examiner will interpret the claim in the disjunctive because there is no evidence in the specification supporting a conjunctive interpretation.
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.
Claims 1-6, and 13-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Japanese Patent Application Publication No.: JP 2013150584 A (Kunigome).
Regarding claim 1, Kunigome teaches an agricultural assistance system comprising: (Kunigome, abstract, page 2, para. 5; FIG. 1: “The present invention provides a pollination apparatus capable of efficiently pollinating a plant with reduced manual labor. A pollination apparatus includes: a detection unit that detects a pollination position of a plant based on a captured image of a plant; and a pollination operation unit that performs a pollination operation to attach pollen to the detected pollination position.”; “FIG. 1 is a diagram showing an outline of a plant cultivation plant 4 according to an embodiment of the present invention. FIG. 1A shows a cross-sectional view thereof, and FIG. 1B shows a plan view thereof. The plant cultivation plant 4 includes a plant cultivation system 1 (FIG. 3) and is a facility for cultivating plants. As shown in FIG. 1, the plant cultivation plant 4 irradiates a plant floor 8 for plant cultivation, a plant enclosure 8 that surrounds the plant floor 6 to separate the outside air space 7, and a plant 9 that is vegetated on the plant floor 6. Is provided. The plant cultivation plant atmosphere adjusting device 2 adjusts the atmosphere of the inner space 13 of the plant enclosure 8. The growth floor 6 is a soil floor in the illustrated example, but may be a hydroponics floor for hydroponics provided with a liquid fertilizer circulation device (not shown).
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an operation mechanism that contacts a plant to perform a predetermined operation (Kunigome, page 2, para. 1-3; FIG. 2; page 36, para. 8-9; page 37, para. 1-2; Fig. 13A-B: “With reference to FIG. 2, the mobile detection apparatus 40 which detects the state of the plant in the plant cultivation plant 4 is shown. FIG. 2 is a diagram showing the mobile detection device 40 in the present embodiment. The mobile detection device 40 uses the mobile vehicle body 41 as a carriage … As shown in FIG. 2, the mobile detection device 40 includes a plurality of arm portions 42 (42-1, 42-2, 42-3, and 42-4) that are connected in series, and the arm portion 42. An imaging unit 300 is provided at the tip opposite to the movable vehicle body 41.”; “Fig.13 (a) is an image which imaged the pollination position from the position corresponding to the left eye, and FIG.13 (b) is an image which imaged the pollination position from the position corresponding to the right eye. In this figure, the symbol K15 indicates the region of the pollination position where the flower f10 is detected, and the symbol 63A indicates the injection unit 63A of the end effector unit 63. In this method, the position of one imaging unit 64 is moved to the left and right, and a region K15 of a pollination position captured as a two-dimensional image is extracted based on two pieces of image information obtained from each position. Then, the distance to the target pollination position is detected from the relationship between the two imaging positions in the three-dimensional space and the extracted pollination position region K15.”;
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a processing circuitry configure to: (Kunigome, page 5, para. 8: “The plant cultivation system 1 illustrated in FIG. 3 includes, for example, a control target unit 100, a determination unit 200, an imaging unit 300, an imaging position moving unit 400, a plant control unit 500, and a mobile pollination device 60 (pollination device).”)
generates a three-dimensional model of a plant based on a combination of predetermined generation conditions (Kunigome, page 7, para. 2-6; FIG. 7; page 41, para. 8, page 42, para. 1-3: FIG. 16: “In addition to the direct detection method for detecting the plant 9, the detection unit 210 shown above indirectly detects information that cannot be directly detected by creating a three-dimensional model based on the directly detected result. FIG. 7 is a diagram illustrating a state of a leaf to be detected. FIG. 7A shows a bird's-eye view of four stems with leaves. The image information captured from the side surface of the plant 9 can acquire the image information as shown in this figure, but it is difficult to detect a planar state. Therefore, four leaves are extracted as three-dimensional information. FIG. 7B shows the result of projecting on the horizontal plane based on the result of using the four leaves shown in FIG. 7A as the three-dimensional information.”;
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“FIG. 16B shows the stem (stem) B0 and the branches (stems) B1 to B3 to which the IC tags Tag0 to Tag3 correspond respectively by dashed arrows b0 to b3. For example, the IC tags Tag0 to Tag3 have IDs as identification information for specifying the positions of flowers or the pollination positions of the stems (stems) or branches (stems) in the sections indicated by broken arrows b0 to b3, respectively. Information is registered. The ID information is stored in the situation storage unit 230 in association with the branch structure (stem) of the plant 9. Moreover, the branch structure of the branch (stem) of the plant 9 is detected by image processing from, for example, the captured image of the plant 9. The branch structure of the branch (stem) of the plant 9 is detected as a three-dimensional position coordinate by using the three-dimensional measurement method for the detected branch, and is three-dimensionally modeled and stored in the situation storage unit 230. Thereby, the mobile pollination device 60 uses each branch (stem) of the plant 9 based on the ID information (identification information) detected by the identification information detection unit 670 and the branch structure of the branch (stem) of the plant 9. The pollination position can be detected every time.”;
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generates a first trained model trained with training data, the training data comprising: a value of a predetermined parameter in the three-dimensional model; and an image of a plant (Kunigome, page 7, para. 7-9; page 8, para. 1-4; page 9, para. 7; page 15, para. 1-3: “The detection unit 210 detects and detects the position of the feature point of the plant 9 or the position of the plant 9 included in the image information from the image information of the captured plant 9 and the position information of the image. The positional information and the captured time information are stored in association with the image information stored in the status storage unit 230. The training model unit 220 includes a plurality of training models. The plurality of breeding models set in the breeding model unit 220 are, for example, a breeding situation model 221 (first breeding model) based on an elapsed time that associates an elapsed time from a reference time (such as germination) with the breeding situation of the plant 9. An object to be detected using an actual arrangement model 222 (second breeding model) with respect to the position of the flower and a pattern based on the size, shape or color, in which the actual position when the flower has grown is estimated are extracted, and a harvest time determination model 223 (third breeding model) for determining the harvest time is included.”; “The training model generation unit 510 generates a training model based on the image information, and updates the training model held in the training model unit 220 … a second model that detects the position of the flower at the time of flowering from the image information and generates a second growth model.”; “the breeding model generation unit 510 generates a reference value by modeling a numerical value corresponding to the item selected as the determination item, and sets it as the initial value of the growth model unit 220. The growth model generation unit 510 generates and updates the generation of the growth model based on the image information. If there is a large discrepancy between the generated breeding model and the actual state, an unreasonable amount of control may be given. In that case, the growth model generation unit 510 corrects the growth model by making a determination based on a predetermined threshold, and updates the value held in the growth model unit. The update of the breeding model may be changed by a growth process determined in stages.”);
obtains an image of a target plant to be evaluated (Kunigome, page 20, para. 4: “The imaging unit 300 images the plant 9 (and the situation around the plant 9), and generates image information.”; see FIG. 7, FIG. 13A-B, and FIG. 2 above to see imaging mechanism) as well as images taken);
obtains the first trained model; estimates, based on the first trained model and the image of the target plant to be evaluated, a value of the predetermined parameter for the predetermined site in the target plant to be evaluated; and causes the operation mechanism to perform the predetermined operation based on the estimated value of the predetermined parameter (Kunigome, page 7, para. 7-9; page 8, para. 1-4; page 9, para. 7; page 15, para. 1-3; see rejection above for discussion of the second trained model that is trained to take an input an image of a target plant and output a position (predetermined parameter) for pollination; Kunigome, page 12, para. 1-3; page 24, para. 5-6; page 25, para. 1: “In the determination of the growth status of the plant 9 performed by the determination unit 200, a plurality of determination items can be selected according to the detection target. For example, in the determination of the growth status of the plant 9 performed by the determination unit 200, a leaf, a stem, a flower / fruit, or a root can be selected as a detection target, and a plurality of determinations are performed according to the selected detection target”; the flower, stem, or leaf is the predetermined site for the image analysis; “The pollination position detection unit 650, based on the command information from the pollination processing control unit 680, based on the feature information indicating the characteristics of the flowers registered in advance and the image of the plant 9 captured by the imaging unit 64. The pollination position (for example, the center position of the plant 9, the position of the flower pistil, or the position of the flower) is detected. For example, in the pollination position detection unit 650, as feature information indicating the feature of the flower of the plant 9, information about the shape of the flower, information about the size of the flower, information about the color of the flower, and the like are registered in advance. In addition, the pollination position detection unit 650 performs image processing on the image of the plant 9 imaged by the imaging unit 64, and image information necessary for detecting the pollination position of the plant 9, that is, the flower Image information relating to feature information indicating features is extracted. Then, the pollination position detection unit 650 uses a pattern matching method based on the feature information indicating the feature of the flower and the image information, or a method for calculating and detecting a feature value evaluation value based on the feature information. For example, the position of the flower of the plant 9 and the pollination position are detected. Further, the pollination position detection unit 650 outputs the detected pollination position to the pollination processing control unit 680. In response to the command information from the pollination processing control unit 680, the pollination history information generation unit 660 causes the end effector unit 63 to perform a pollination operation on the pollination position detected by the pollination position detection unit 650 by the control of the pollination operation control unit 630.”).
Regarding claim 2, Kunigome teaches the agricultural assistance system according to claim 1, wherein the predetermined parameter is at least one selected from the group consisting of an orientation of the predetermined site in the plant and a positional relationship of the predetermined site in the plant, in a three-dimensional space for generating the three-dimensional model (Kunigome, page 7, para. 2-6; FIG. 7; page 41, para. 8, page 42, para. 1-3: FIG. 16; Kunigome, page 7, para. 7-9; page 8, para. 1-4; page 9, para. 7; page 15, para. 1-3; see rejection of claim 1 above; a positional relationship between the predetermined sites (flower, stem, root, etc.) of the plant are found to determine pollination location in the 3D model; see FIG. 7 for 3D model; Kunigome, page 16, para. 6-9: “In addition, an actual arrangement model (second breeding model) with respect to the position of the flower, which estimates the actual position when the flower has grown, is required. In the second breeding model, the number, density, arrangement, and the like are given as determination items in the determination based on the state of the flower / fruit in the three-dimensional model based on the three-dimensional coordinate position of the flower / fruit. In the selected item, modeling can be performed by using the position and the actual size of the actual arrangement with respect to the position of the flower as reference values. This second breeding model can be used not only as a reference for individual actual growth, but also when determining adjacent real interference based on the size and isolation distance of each adjacent real.”).
Regarding claim 3, Kunigome teaches the agricultural assistance system according to claim 1, wherein the predetermined generation conditions comprise at least one selected from the group consisting of a size of the three-dimensional model, a number of petals in the three-dimensional model, a color of the three-dimensional model, a shape of the three-dimensional model, a background in a space in which the three-dimensional model is generated, a position of illumination in the space in which the three-dimensional model is generated, and an orientation of the three-dimensional model (Kunigome, page 7, para. 2-6; FIG. 7; page 41, para. 8, page 42, para. 1-3: FIG. 16; see rejection of claim 1 above; an orientation of the different parts of the plant is used to generate the three-dimensional model).
Regarding claim 4, Kunigome teaches the agricultural assistance system according to claim 1, wherein the predetermined site in the target plant to be evaluated is at least one position selected from the group consisting of a flower pistil, a flower stamen, a fruit, and a leaf (Kunigome, page 7, para. 7-9; page 8, para. 1-4; page 9, para. 7; page 15, para. 1-3; Kunigome, page 12, para. 1-3; see rejection for claim 1 above; the predetermined site is either leaves, flowers/fruits, roots or stems).
Regarding claim 5, Kunigome teaches the agricultural assistance system according to claim 1, wherein the predetermined operation is at least one operation selected from the group consisting of pollinating flowers, harvesting fruits, removing leaves, and thinning out fruits (Kunigome, page 7, para. 7-9; page 8, para. 1-4; page 9, para. 7; page 15, para. 1-3; see rejection above for discussion of the second trained model that is trained to take an input an image of a target plant and output a position (predetermined parameter) for pollination; Kunigome, page 12, para. 1-3; page 24, para. 5-6; page 25, para. 1; see rejection of claim 1 above; pollinating flowers is done by the agricultural system).
Regarding claim 6, Kunigome teaches the agricultural assistance system according to claim 1, comprising an imaging mechanism that captures images of plants (Kunigome, page 20, para. 4; FIG. 7, FIG. 13A-B, and FIG. 2; see rejection of claim 1 above showing camera for imaging).
With regards to claim 13, it recites substantially the same functions of the system of claim 1. Thus, the analysis in rejecting claim 1 is equally applicable to claim 13.
With regards to claim 14, it recites the functions of the system of claim 1, as a process. Thus, the analysis in rejecting claim 1 is equally applicable to claim 13.
Claim Rejections - 35 USC § 103
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 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 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Kunigome, in view of U.S. Patent Application Publication No.: 2021/0027397 (Kikuchi).
Regarding claim 7, Kunigome teaches the agricultural assistance system according to claim 1.
Kunigome fails to teach
wherein the processing circuitry configure to: obtains a second trained model generated by training with: at least one of ventilation and sunlight in a cultivated field; and a fruit yield in a harvest season. Kikuchi teaches
wherein the processing circuitry configure to: obtains a second trained model generated by training with: at least one of ventilation and sunlight in a cultivated field; and a fruit yield in a harvest season (Kikuchi, para. [0038]-[0042]: “Here, examples of “actual performance information regarding crops”, which serve as objective variables, include yield, quality, sales (profit), and so on … “Information regarding a cultivated land” collected by the information collection unit 11 and “information regarding cultivated lands” that serves as explanatory variables may be information that includes information regarding the variety of the cultivated crop, information regarding the properties of the soil in the cultivated land, and environmental information regarding the cultivated land … Examples of “environmental information regarding the cultivated land” includes weather information, farming information, and so on. More specifically, examples of weather information include the temperature, the humidity, the solar radiation amount, the wind speed, the wind direction, the rainfall amount, and so on, per unit time … The prediction model creation unit 15 can perform machine learning on the above-described explanatory variables and objective variables, using linear regression (multiple regression, Ridge regression, lasso, fused lasso, principal component regression, partial least square regression, or the like), or non-linear regression (a decision tree, a Gaussian process, neural networks, or the like). A method for training a prediction model may be determined in advance, or automatically selected from among a plurality of methods. In the latter case, the prediction model creation unit 15 applies a plurality of methods to the above-described explanatory variables and objective variables, and adopts the method with which indicators that have been determined in advance (the prediction error, the calculation time, and so on) defined in advance are optimal values. The following describes a specific example of the prediction model 14. or example, the prediction model creation unit 15 generates a prediction model expressed by Math 1 shown below for each of varieties 1, 2, . . . and n, where an objective variable y denotes an indicator to which attention is paid (yield, sugar content, sales, or the like) and x denotes an explanatory variable
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It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the processing circuitry, as taught by Kunigome, to be configured to obtain a second trained model generated by training with: at least one of ventilation and sunlight in a cultivated field; and a fruit yield in a harvest season, as taught by Kikuchi.
The suggestion/motivation for doing so would have been to that accurately predicting crop yields is essential for meeting food production demands while minimizing waste; machine learning enhances crop yield predictions by analyzing vast datasets, including historical yield records, weather patterns, and soil conditions; these algorithms identify patterns and correlations that would be impossible to detect manually, helping farmers anticipate harvest outcomes more effectively.
Therefore, it would have been obvious to combine Kunigome, with Kikuchi, to obtain the invention as specified in claim 7.
Regarding claim 8, Kunigome, in view of Kikuchi, teaches the agricultural assistance system according to claim 7, wherein the processing circuitry configure to: estimates, based on an output from the first trained model, an optimal positional relationship of any one selected from the group consisting of leaves, flowers, and fruits, and causes the operation mechanism to perform the predetermined operation based on the optimal positional relationship of any one selected from the group consisting of leaves, flowers, and fruits, estimated by the positional relationship estimation (Kunigome, page 31, para. 7; page 33, para. 1-2; FIG. 5; Page 36, para. 8-9; FIG. 13: “In the determination based on the state of the flower / fruit in the determination unit 200, the number, density, arrangement, flowering state of the flower, maturity of the fruit, and the like are listed as determination items. FIG. 5 is a diagram illustrating a positional relationship between a flower to be detected and a fruit. FIGS. 5 (a) and 5 (c) show the flowering state, and FIGS. 5 (b) and 5 (d) show the fruiting state. Since the actual position depends on the position of the flower, the actual number and position can be specified by specifying the number and position of the flowers. The positions of the flowers f1 and f2 shown in the flowering period change as the positions of the fruits F1 and F2 in the mature period. The number of flowers (fruits) is detected by counting the quantity existing within a predetermined range or the number of flowers (fruits) attached to the stem (specified branch). The density of the flower (fruit) is calculated by dividing the quantity of the detected flower (fruit) by the unit capacity. Alternatively, the density of flowers (fruits) is simply detected by counting the number of flowers (fruits) present in a predetermined range of the captured image. The arrangement of flowers (fruits) is detected as a three-dimensional coordinate position by using a three-dimensional measurement technique. Moreover, the actual position of the fruiting period can be estimated from the detected position of the flower. The actual estimated position can be calculated based on the three-dimensional coordinate position of the flower by assuming the actual size and weight and using the strength and length of the branch from which the fruit hangs as calculation conditions. From this calculated estimated position, it is possible to estimate interference with an adjacent actual object. That is, it can be estimated that interference occurs when the relative distance is closer than the actual size. The flowering state of the flower is detected as a state of whether or not it is flowering (or the degree of flower opening) based on the shape of the flower, the size of the flower, or the color of the flower.”;
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“FIG. 13 is a diagram illustrating an example of a plurality of images obtained by imaging the pollination positions from different positions. Fig.13 (a) is an image which imaged the pollination position from the position corresponding to the left eye, and FIG.13 (b) is an image which imaged the pollination position from the position corresponding to the right eye. In this figure, the symbol K15 indicates the region of the pollination position where the flower f10 is detected, and the symbol 63A indicates the injection unit 63A of the end effector unit 63. In this method, the position of one imaging unit 64 is moved to the left and right, and a region K15 of a pollination position captured as a two-dimensional image is extracted based on two pieces of image information obtained from each position. Then, the distance to the target pollination position is detected from the relationship between the two imaging positions in the three-dimensional space and the extracted pollination position region K15.”;
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Kunigome, in view of Kikuchi teaches the agricultural assistance system according to claim 7, wherein the processing circuitry configure to: estimates, based on a predicted yield that is output from the second trained model, an optimal positional relationship of any one selected from the group consisting of leaves, flowers, and fruits, and causes the operation mechanism to perform the predetermined operation based on the optimal positional relationship of any one selected from the group consisting of leaves, flowers, and fruits, estimated by the positional relationship estimation (Kunigome teaches finding optimal positional relationships of aspects of the plant imaged from first trained model; Kikuchi, para. [0038]-[0042] teaches outputting a predicted yield from a second trained model; therefore, when in combination, once a predicted yield is known, the position/orientation output from the first trained model indicating positioning for pollination is improved since crop prediction is related to density of the crops that are ready for pollination opposed to those that are not).
Allowable Subject Matter
Claims 9-12 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL ADAM SHARIFF whose telephone number is 571-272-9741. The examiner can normally be reached M-F 8:30-5PM.
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/MICHAEL ADAM SHARIFF/
Examiner, Art Unit 2672
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672
Prosecution Insights