DETAILED ACTION
This action is in response to the application filed on. Claims 1-10 are pending and have been examined.
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 Status
Claim 9 has been amended with unacceptable multiple dependent claim wording, “A method claim as claimed in claim 5-8 any item.” Please see MPEP 608.01(n) for correction. Claims 1-10 are pending for examination in this application.
Priority
Receipt is acknowledged that application claims priority to foreign application with application number CN202210396435.7 dated April 15, 2022. Copies of certified papers required by 37 CFR 1.55 have been received. Priority is acknowledged under 35 USC 119(e) and 37 CFR 1.78.
Receipt is acknowledged that application is a National Stage application of PCT/CN2022/114077 with a priority date of August 23, 2022 is acknowledged under 35 USC 119(e) and 37 CFR 1.78.
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.
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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) is/are:
“image acquisition module” in independent claim 10
“ image feature extraction module” in independent claim 10
“evaluation model module” in independent claim 10
“harvesting timing determination module” in independent claim 10
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/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 this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 9 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter.
The limitations of dependent claim 9, includes, “A method claim as claimed in claim 5-8 any item.” The limitation is interpretated as dependent on dependent claim 8 as in the original amended claim. It is unclear given the current limitations what, “any item” is referring to.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brueckner et al, US 20140050364 in view of Perry et al, US 20190050948 in further view of Ni et al, CN 110084194A.
Regarding claim 10, Brueckner teaches
A monitoring and evaluation system for comprehensive evaluation index of machine-harvested cotton defoliation effect, wherein comprises (see Brueckner, Paragraph [0006], “an improved method and apparatus for optically evaluating harvested crop in a harvesting machine”):
a machine-harvested cotton canopy RGB image acquisition module for acquiring machine-harvested cotton canopy RGB images (see Brueckner, Paragraph [0007], “an image of the harvested crop is initially recorded with a camera such that an electronic image processing system (online) has a digital image of the harvested crop,” and Paragraph [0046], “The mean value image of the camera image and the average intensity of each colour channel is calculated for this purpose,” a camera is considered to be an image acquisition module; a camera image is considered to be an RGB image as it contains color channels;);
an image feature extraction module for extracting visible-light (see Brueckner, Fig. 3, Paragraph [0030], “FIG. 3 shows a flow diagram, according to which the image processing system 80 operates,” and Paragraph [0041], “Determining colour, texture and contour-based image features with subsequent classification by using individual values from the data bank (S126 to S128)”);
Brueckner does not expressively teach
extracting the visible-light vegetation index features,
a comprehensive evaluation model module for inputting the visible-light vegetation index features, color component features and texture features into a trained comprehensive evaluation model of machine-harvested cotton defoliation effect to output the defoliation effect evaluation values;
a machine-harvested cotton harvesting timing determination module for determining the harvesting timing of machine-harvested cotton based on the defoliation effect evaluation value.
However, Perry in a similar invention in the same field of endeavor teaches
extracting the visible-light vegetation index features (see Perry, Paragraph [0102], “Data calculated based on other agricultural information, including … normalized difference vegetation index, modified soil-adjusted vegetation index, data calculated using other information in the database, and the like”),
a comprehensive evaluation model module for inputting the visible-light vegetation index features, color component features and texture features into a trained comprehensive evaluation model of machine-harvested cotton defoliation effect to output the defoliation effect evaluation values (see Perry, Paragraph [0114], “the input/output module 405 accesses the geographic database 135 and the agricultural database 140 to retrieve data for use by the training module 410 to train prediction models. The input/output module 405 can coordinate the transfer of information between modules of the crop prediction engine 155, and can output information generated by the crop prediction engine, for instance, crop production prediction information and/or a set of farming operations that optimize crop production,” agricultural information is input into the training model and an output of crop production prediction information );
a machine-harvested cotton harvesting timing determination module for determining the harvesting timing of machine-harvested cotton based on the defoliation effect evaluation value (see Perry, Paragraph [0040], “The user of the agronomist client device 108 …. can change the harvest date, for instance by moving the harvest date up based on expected inclement weather,” and “a user of the agronomist client device 108 can modify farming operations identified by the crop prediction system 125 as optimal based on information available to the user but not available to the crop prediction system 125 at the time the predictions were made”).
The combination of Brueckner and Perry are analogous art because they are both in the same field of endeavor of evaluating harvested crop. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to calculate the vegetation index and input agricultural information to train prediction models; and to change the harvest date based on the prediction model as taught in the system of Perry in the method of Brueckner to set farming operations that optimize crop production (Perry, Paragraph [0114]).
Brueckner in view of Perry does not expressively teach
the trained comprehensive evaluation model of machine-harvested cotton defoliation effect is an extreme learning machine model based on particle swarm optimization algorithm through training with visible-light vegetation index features, color component features and texture features of the machine-harvested cotton canopy RGB images as input and the defoliation effect evaluation value as output;
the extreme learning machine model comprising an input layer, a hidden layer and an output layer, and the particle swarm optimization algorithm provided for optimizing the weight values of the input layer and the bias values of the hidden layer;
However, Ni in a similar invention in the same field of endeavor teaches
the trained comprehensive evaluation model of machine-harvested cotton defoliation effect is an extreme learning machine model based on particle swarm optimization algorithm (see Ni, Paragraph [0039], “The weights and biases of the Extreme Learning Machine are randomly determined, which can easily lead to overfitting. The particle swarm optimization algorithm is used to optimize the weights and biases of the Extreme Learning Machine”)
through training with visible-light vegetation index features, color component features and texture features of the machine-harvested cotton canopy RGB images as input and the defoliation effect evaluation value as output (see Ni, Paragraph [0069], “The optimized Extreme Learning Machine is used as the final classifier to process the 36 dimensional high-order features after dimensionality reduction, so as to realize hyperspectral image classification and thus identify cotton seed mulch film,” Brueckner and Perry were relied on to teach visible-light vegetation index features, color component features and texture features and Ni teaches an extreme learning model based particle swarm optimization and outputs hyperspectral image classification and identify cotton seed mulch film which is considered to be an output );
the extreme learning machine model comprising an input layer, a hidden layer and an output layer, and the particle swarm optimization algorithm provided for optimizing the weight values of the input layer and the bias values of the hidden layer (see Ni, Paragraph [0020], “the weights and biases of the extreme learning machine are used as particles in the particle swarm optimization algorithm, with particle length D = k(n+1), where: k is the number of hidden layer nodes, k = 20; n is the input dimension, n = 36”);
The combination of Brueckner, Perry, and Ni are analogous art because they are all in the same field of endeavor of optimizing harvested crop. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to optimize the weights and biases of an extreme learning model using particle swarm optimization algorithm; to identify an output as taught in the method of Ni in the apparatus of Brueckner in view of Perry to improve the quality of harvested cotton. (Ni, Paragraph [0005]).
As per claim 1, Claim 1 claims a monitoring and evaluation method for comprehensive evaluation index of machine-harvested cotton defoliation effect, comprising the same limitations as Claim 10. Therefore, the rejection and rationale are analogous to that made in Claim 10.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brueckner et al, US 20140050364 in view of Perry et al, US 20190050948 in view of Ni et al, CN 110084194A in further view of Eichhorn et al, US 20230230202.
Regarding claim 2, Brueckner in view of Perry in further view of Ni does not expressively teach a method as claimed in claim 1,
wherein the method further comprises the following step after acquiring the machine-harvested cotton canopy RGB image: stitching the machine-harvested cotton canopy RGB image by Pix4Dmapper software to obtain a machine-harvested cotton canopy RGB ortho-image.
However, Eichhorn in a similar invention in the same field of endeavor teaches
wherein the method further comprises the following step after acquiring the machine-harvested cotton canopy RGB image: stitching the machine-harvested cotton canopy RGB image by Pix4Dmapper software to obtain a machine-harvested cotton canopy RGB ortho-image (see Eichhorn, Paragraph [0110], “the soil and crop residue have sufficient visual structure for a mapping software, such as Pix4Dmapper, to stitch together a 2D orthomosaic map from the processed image data,” orthomosaic map contains merged ortho-images ).
The combination of Brueckner, Perry, Ni, and Eichhorn are analogous art because they are all in the same field of endeavor of optimizing harvested crop. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to use Pix4Dmapper to stitch together a 2D orthomosaic map as taught in the method of Eichhorn in the method of Brueckner in view of Perry in further view of Ni to make decisions about crop management throughout the growing season (Eichhorn, Paragraph [0003]).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brueckner et al, US 20140050364 in view of Perry et al, US 20190050948 in view of Ni et al, CN 110084194A in further view of Shi et al, US 20240288602.
Regarding claim 4, Brueckner in view of Perry in further view of Ni does not expressively teach a method as claimed in claim 1,
wherein the method further comprises the following step after extracting the visible-light vegetation index features, color component features and texture features of the machine-harvested cotton canopy RGB images: selecting the extracted visible-light vegetation index features, color component features and texture features using random forest method respectively to obtain the selected image features; the selected image features comprising at least one visible-light vegetation index feature, at least one color component feature, and at least one texture feature.
However, Shi in a similar invention in the same field of endeavor teaches
wherein the method further comprises the following step after extracting the visible-light vegetation index features, color component features and texture features of the machine-harvested cotton canopy RGB images: selecting the extracted visible-light vegetation index features, color component features and texture features using random forest method respectively to obtain the selected image features; the selected image features comprising at least one visible-light vegetation index feature, at least one color component feature, and at least one texture feature (see Shi, Paragraph [0060], “the independent variable select model selected is the random forest model, and the independent variable set to be selected for screening should include four categories of characteristics: spectral features, vegetation index features, salt index features, and soil-related indices”).
The combination of Brueckner, Perry, Ni, and Shi are analogous art because they are all in the same field of endeavor of optimizing harvested crop. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to use a random forest model and select independent variables: spectral features, vegetation index features, salt index features, and soil-related indices as taught in the method of Shi in the method of Brueckner in view of Perry in further view of Ni to evaluate the salinity of the soil which impacts soil health and crop production (Shi, Paragraph [0003]).
Allowable Subject Matter
Claim(s) 3 and 5-9 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 DOMINIQUE JAMES whose telephone number is (703)756-1655. The examiner can normally be reached 9:00 am - 6:00 pm EST.
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/DOMINIQUE JAMES/Examiner, Art Unit 2666
/EMILY C TERRELL/Supervisory Patent Examiner, Art Unit 2666
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