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 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 following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(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) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claim 8 recite limitations that been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because it they use a generic placeholders “module” coupled with functional language without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
Since the claim limitation invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, the claim have been interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112 , sixth paragraph, applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
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 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.
Claims 1-3, 5-12, 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over D11 and further in view of D2.2
With regard to claim 1, D1 teach method for calculating a leaf area of a plant, comprising: obtaining a leaf image of a target plant on a planned path, wherein the planned path is a path determined based on a dynamic window approach (DWA) path planning algorithm (see abstract, § 5.3 ¶ 1: robot fitted with a camera to capture images of tomato plants including leaves, path planning using DWA or Dynamic Window Approach; fig. 4, 7: image of plant leaf) and segmenting the leaf (see fig. 9: leaf detection).
D1 fails to explicitly teach segmenting the leaf image by using a UNet model, to obtain a leaf segmentation image, wherein the UNet model comprises an encoder, a decoder, and a trained segmentation network that are connected to each other; and calculating a leaf area based on the leaf segmentation image. However, D2 teach the missing features (see abstract: segmenting the leaf using UNet model comprising encoder, decoder and segmentation network, see fig. 6; see abstract, § 4.2.1: leaf area).
Based on the combined teachings, one skilled in the art would have found it obvious to combine the teachings to arrive at the claimed invention. D1 is related to imaging tomato plants and detecting leaves. D2 is related to detecting or segmenting leaves using UNet. It would have been obvious to incorporate known teachings of D2 into the configuration of D1 for segmenting tomato leaves, yielding predictable results and enhanced segmentation.
With regard to claim 2, D1 teach method for calculating a leaf area of a plant according to claim 1, wherein the leaf image is an see fig. 4, 5, § 3 ¶ 1: robot equipped with camera to capture image of the tomato leaf). D1 fails to explicitly teach RGB camera, however D2 teach the missing feature (see p. 4 ¶¶ 1-2: RGB camera). One skilled in the art would have found it obvious to substitute the camera in D1 with the RGB camera disclosed in D2, yielding predictable results and high color accuracy or fidelity.
With regard to claim 3, D1 teach method for calculating a leaf area of a plant according to claim 2, wherein a process for determining the planned path comprises: determining a travel route of the target robot, wherein the travel route is determined by a line connecting a starting point and a target point (see abstract, § 5. 3: path planning from start to end point, see fig. 11); obtaining travel data of the target robot on the travel route in real time in a form of a dynamic window, wherein the travel data comprises obstacle position data, a travel direction, and a travel speed (see abstract: dynamic window approach; see § 2.1 ¶¶ 1-3: data comprises velocity, direction and obstacles); determining a trajectory function based on the DWA path planning algorithm and determining the planned path based on the travel data and the trajectory function (see abstract, § 2. 1 ¶¶ 1-3, § 5.3 ¶¶ 1-2: determining a trajectory or path plan based on DWA).
With regard to claim 5, D2 teach method for calculating a leaf area of a plant according to claim 1, wherein the segmenting the leaf image by using the UNet model to obtain the leaf segmentation image specifically comprises: extracting, by the encoder, features of the leaf image, to obtain image feature information (see abstract, § 3.2.1: feature extraction encoder); performing, by the decoder, data fusion based on the image feature information, to obtain fused feature data (see abstract, § 3.2: multiscale feature fusion in the decoder); and segmenting, by the trained segmentation network, the fused feature data, to obtain the leaf segmentation image (see abstract, § 3.2: segmentation). The motivation for combining the references is the same as stated in claim 1.
With regard to claim 6, D2 teach method for calculating a leaf area of a plant according to claim 5, wherein a process for determining the UNet model comprises: obtaining training data, wherein the training data comprises: leaf images for training and leaf segmentation images corresponding to the leaf images for training (see § 3.1 ¶¶ 1-2: training data); extracting, by the encoder, features from the leaf images for training, to obtain image feature information for training (see abstract, § 3.2.1: feature extraction encoder); performing data fusion based on the image feature information for training, to obtain fused feature data for training (see abstract, § 3.2: multiscale feature fusion in the decoder); constructing a segmentation network (see abstract, fig. 6: segmentation network); inputting the fused feature data for training into the segmentation network, updating and optimizing model parameters by using a see § 3.2.4 ¶ 1, § 3.3 ¶ 1: updating and optimizing parameters using a loss function, parameters include weights, categorical cross-entropy is used as a loss function; UNet model implicitly uses a gradient descent ); and connecting the encoder, the decoder, and the trained segmentation network to form the UNet model (see fig. 6).
D2 implicitly uses a gradient descent method, but fails to explicitly teach wherein the gradient descent is a stochastic gradient descent. However, Examiner takes Official Notice to the fact that stochastic gradient descent is extremely well known in the art before the effective filing date and one skilled in the art would have been motivated to incorporate known stochastic gradient descent method into the configuration of D2, yielding predictable and enhanced results. The advantages of using stochastic gradient descent would have been to enhance computational efficiency.
With regard to claim 7, D2 teach segmentation of the leaf image and determining the leaf area based on the obtained leaf region and extracting a contour of the leaf segmentation image to obtain a leaf contour (see abstract: segmenting the leaf using UNet model comprising encoder, decoder and segmentation network, see fig. 6; see abstract, § 4.2.1: leaf area; segmenting the leaf implies extracting the contour of the leaf region), however fails to explicitly teach converting the leaf contour into a binary image; performing morphological processing on the binary image, removing noise, and eliminating a blank area, to obtain a processed image; performing connected component analysis on the processed image to obtain a leaf region. However, Examiner takes Official Notice to the fact that the steps of generating a binarized image, performing morphological processing to remove noise and eliminate blank area, followed by performing connected component analysis is extremely well known in the art before the effective filing date and one skilled in the art would have been motivated to incorporate known teachings into the configuration of D2 yielding predictable, enhanced and accurate calculation of the area of segmented leaf.
With regard to claim 8, see discussion of claim 1.
With regard to claim 9, see discussion of claim 1. D1 further teach a memory and processor (see fig. 4, § 4.1: hardware setup; see also § 4.2: software setup).
With regard to claim 10, D1 teach where the memory is computer readable storage medium (see fig. 4, § 4.1: hardware setup).
With regard to claims 11-12, 14-16, see discussion of corresponding claims 2-3 and 5-7, respectively.
With regard to claims 18-20, see discussion of claim 10.
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over D1 in view of D2 and further in view of D3.3
With regard to claim 4, D1 fails to explicitly teach method for calculating a leaf area of a plant according to claim 3, wherein an expression of the trajectory function is as follows:
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wherein G (v, w) is the trajectory function; v is the travel speed; w is an angular velocity of travel; σ is a first weight coefficient; α is a second weight coefficient; β is a third weight coefficient; γ is a fourth weight coefficient; heading (v, w) is an azimuth function; velocity (v, w) is a linear velocity of the target robot; and dist (v, w) is a distance from the target robot to an obstacle.
However, D3 teach the missing feature (see equation 13).
Based on the combined teachings, one skilled in the art would have found it obvious to combine the teachings to arrive at the claimed invention. D1 is related to imaging tomato plants and detecting leaves using a robot whose path is planned using Dynamic Window Approach. D3 is related to path planning for mobile robots based on Dynamic Window Algorithm. One skilled in the art would have found it obvious to incorporate the specific trajectory function disclosed in D3 into the DWA path planning in D1, yielding predictable and enhanced results.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AVINASH YENTRAPATI whose telephone number is (571)270-7982. The examiner can normally be reached on 8AM-5PM.
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/AVINASH YENTRAPATI/Primary Examiner, Art Unit 2672
1 Al-Mashhadani, Zubaidah, and Joon-Hyuk Park. "Autonomous agricultural monitoring robot for efficient smart farming." 2023 23rd International Conference on Control, Automation and Systems (ICCAS). IEEE, 2023.
2 Bhagat, Sandesh, et al. "Eff-UNet++: A novel architecture for plant leaf segmentation and counting." Ecological informatics 68 (2022): 101583.
3 Sun, Ying, et al. "Local path planning for mobile robots based on fuzzy dynamic window algorithm." Sensors 23.19 (2023): 8260.
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