ouDETAILED ACTION
Notice of Pre-AIA or AIA Status
The application, filed on or after March 16, 2013, is being examined under the first inventor to file present provisions of the AIA .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. CN202110845851.6, filed on 07/26/2021.
Status of Claims
Claims 1-10 are currently pending and examined on the merits.
Information Disclosure Statement
The information disclosure statement filed 6/07/2022 is acknowledged. A signed copy of the corresponding 1449 form has been included with this Office action.
- 35 USC § 112(f)
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:
“…a test data obtaining module, configured to obtain…” in claim 10, line 1
“…a…prediction module, configured to input…” in claim 10, line 2.
“…a…first obtaining module, configured to obtain…” in claim 10, line 3
“… a…second obtaining module, configured to obtain…” in claim 10, line 3-4
“… a…second obtaining module, configured to obtain…” in claim 10, line 5
“… a…training module, configured to train…” in claim 10, line 5-6
In cases involving a special purpose computer-implemented means-plus-function limitation, the Federal Circuit has consistently required that the structure be more than simply a general-purpose computer or microprocessor and that the specification must disclose an algorithm for performing the claimed function. See, e.g., Noah Systems Inc. v. Intuit Inc., 675 F.3d 1302, 1312, 102 USPQ2d 1410, 1417 (Fed. Cir. 2012); Aristocrat, 521 F.3d at 1333, 86 USPQ2d at 1239.
The specific embodiments of the present disclosure detail the crop yield prediction system and its modules in the specification, paragraphs 0036-0042, which state modules configured to obtain data, test data, and construct equations; however, Applicant’s specification does not disclose a specific means of configuration or a description of the function of said specific configuration to obtain data, test data, and construct equations.
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 the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 10 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 10 recites a system comprised of modules that functions via said modules which obtain and input data, as well as modules to construct mathematical equations. In the specification, in paragraphs 0085-0098, the applicant discloses the embodiments of the modules. The applicant does not state in the specification what the modules include, except for the regression model which is stated on paragraphs 0087-0091 to include a first obtaining module, a second obtaining module, a construction module, and a training module. The applicant discloses the functions of the modules but not composition or makeup of said modules.
Original, amended, or new claims are each given their broadest reasonable interpretation in light of, and consistent with the written description of the invention. Claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or how the result is to be achieved.
As set forth in MPEP 2161, 2181 and 2185, the claims must be supported by adequate written description of the step-by-step directions, algorithms, or structures to carry out the claimed steps. Functional claim limitations may be adequately described if: (1) The written description adequately links or associates adequately described particular structure, material, or acts to perform the function recited; or (2) it is clear based on the facts of the application that one skilled in the art would have known what specific structure, material, or acts disclosed in the specification perform the specialized function. See Aristocrat Techs. Australia PTY Ltd. v. Int’l Game Tech., 521 F.3d 1328, 1336-37, 86 USPQ2d 1235, 1242 (Fed. Cir. 2008). As stated previously, as the applicant does not disclose the composition or makeup of said modules in the claims or specification, the written description does not adequately link or associate adequately described particular structure, material, or acts to perform the function recited, nor it is clear based on the facts of the application that one skilled in the art would have known what specific structure, material, or acts disclosed in the specification perform the specialized function. Additionally, in the specification within paragraphs 0033-0039 and 0080-0090, the applicant simply reiterates the function of the modules without disclosing corresponding structure, material or act that performs the entire claimed function.
Merely restating a function associated with a means-plus-function limitation is insufficient to provide the corresponding structure for definiteness. See, e.g., Noah, 675 F.3d at 1317, 102 USPQ2d at 1419; Blackboard, 574 F.3d at 1384, 91 USPQ2d at 1491; Aristocrat, 521 F.3d at 1334, 86 USPQ2d at 1239. It follows therefore that such a mere restatement of function in the specification without more description of the means that accomplish the function would also likely fail to provide adequate written description under section 112(a) or pre-AIA section 112, first paragraph (MPEP 2181). The applicant can overcome this insufficiency by disclosing a structure, material or act that performs the entire claimed function in the claim or specification.
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 10 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 which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “module” in claim 10 is a relative term which renders the claim indefinite. The term “module” is not defined by the claim, the applicant does not disclose structure, material or acts for performing the recited function in the specification or claims, nor does the specification provide a standard for ascertaining the requisite degree. One of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Therefore, the functions of configurations of said “module” detailed by the applicant in claim 10 via the phrase have been rendered indefinite by the use of the term “module”.
If there is no disclosure of structure, material or acts for performing the recited function, the claim fails to satisfy the requirements of 35 U.S.C. 112(b). The disclosure of the structure (or material or acts) may be implicit or inherent in the specification if it would have been clear to those skilled in the art what structure (or material or acts) corresponds to the means- (or step-) plus-function claim limitation. See id. at 1380, 53 USPQ2d at 1229; In re Dossel, 115 F.3d 942, 946-47, 42 USPQ2d 1881, 1885 (Fed. Cir. 1997). However, "[a] bare statement that known techniques or methods can be used does not disclose structure" in the context of a means plus function limitation. Biomedino, LLC v. Waters Technology Corp., 490 F.3d 946, 952, 83 USPQ2d 1118, 1123 (Fed. Cir. 2007) (Disclosure that an invention "may be controlled by known differential pressure, valving and control equipment" was not a disclosure of any structure corresponding to the claimed "control means for operating [a] valving " and the claim was held indefinite). See also Budde v. Harley-Davidson, Inc., 250 F.3d 1369, 1376, 58 USPQ2d 1801, 1806 (Fed. Cir. 2001); Cardiac Pacemakers, Inc. v. St. Jude Med., Inc., 296 F.3d 1106, 1115-18, 63 USPQ2d 1725, 1731-34 (Fed. Cir. 2002) (Court interpreted the language of the "third monitoring means for monitoring the ECG signal…for activating …" to require the same means to perform both functions and the only entity referenced in the specification that could possibly perform both functions is the physician. The court held that excluding the physician, no structure accomplishes the claimed dual functions. Because no structure disclosed in the embodiments of the invention actually performs the claimed dual functions, the specification lacks corresponding structure as required by 35 U.S.C. 112, sixth paragraph, and fails to comply with 35 U.S.C. 112, second paragraph.) (See MPEP 2181).
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 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claim is directed to “A crop yield prediction system, comprising: a test data obtaining module…”, but the specification does not provide detail suggesting the system or module is anything other than software per se (see MPEP 2106.03). Therefore, claim 10 does not fall within any statutory category and is not patent eligible (Step 1: NO).
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of mental steps, mathematic concepts, organizing human activity, or a natural law without significantly more.
Step 2A, Prong 1
In accordance with MPEP § 2106, claims 1-9 are found to recite statutory subject matter:
Claim 1 recites: “A crop yield prediction method…” (line 1), which is a process.
Claims 2-9 are dependent on claim 1.
Claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to abstract ideas and mathematical concepts:
Claim 1 recites a process of predicting vegetation yields, which is a series of mental steps (i.e. can be performed with pen and paper) and mathematical concepts:
“…a method for determining the hierarchical linear regression model…” (line 4-5), mental step
“…constructing a first regression equation and a second regression equation…” (line 7-8), mathematical concept
“… inputting the training normalized difference vegetation index and the measured yield data into the first regression equation, and the training meteorological data into the second regression equation to train the first regression equation and the second regression equation” (line 9-10), mathematical concept
“…determining the trained first regression equation as the hierarchical linear regression model” (line 12), mental step
Claim 2 recites performing calculations to obtain vegetation index data, which is a mathematical concept:
“…calculating a spectral reflectance based on the remote sensing image data…” (line 3-4), mathematical concept
“…performing band calculation on the spectral reflectance to obtain the training normalized difference vegetation index” (line 4-5), mathematical concept
Claim 4 recites a band calculation formula, which is a mathematical concept:
“…wherein a formula for performing band calculation on the spectral reflectance is…” (line 1-2), mathematical concept
Claim 5 recites a formula for regression, which is a mathematical concept:
“…wherein a formula of the first regression equation is…” (line 1-2), mathematical concept
Claim 6 recites a formula for regression, which is a mathematical concept:
“…wherein a formula of the second regression equation is…” (line 1-2), mathematical concept
Claim 9 recites calculating training meteorological data, wherein the training data comprises environmental measurements, which is a mathematical concept:
“…calculating the training meteorological data based on the daily value data set of surface climate data, wherein the training meteorological data comprises average daily maximum temperature, average daily minimum temperature, average daily precipitation, and average sunshine duration” (line 5-7), mathematical concept
The claims recite an abstract idea of calculating for the purposes of crop yield prediction (See MPEP 2106.07(a)).
These recitations are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. As such, claim(s) 1-9 recite(s) an abstract idea/law of nature/natural phenomenon (Step 2A, Prong 1: YES).
Step 2A, Prong 2
Claims fund to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology or applies or uses the recited judicial exception to affect a particular treatment for a condition. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment or mere instructions to apply the recited judicial exception via a generic treatment. Specifically, the claims recite the following additional elements:
Claim 1 recites obtaining crop-yield-related data, which is a process of data gathering or data presentation:
“…obtaining a test normalized difference vegetation index and test meteorological data of a to- be-tested area…” (line 1-2), data gathering
“…obtaining a training normalized difference vegetation index of a crop planting area…” (line 4-5), data gathering
“…obtaining training meteorological data and measured yield data of the crop planting area…” (line 5-6), data gathering
Claim 2 recites obtaining image data, which is a process of data gathering or data presentation:
“…obtaining remote sensing image data of the crop planting area…” (line 2-3), data gathering
Claim 7 recites a facet of data, which is not an improvement to technology:
“…wherein the crop in the to-be-tested area is corn,” (line 1) not an improvement to technology
Claim 8 recites a facet of data, which is not an improvement to technology:
“…wherein the corn is in the grain filling stage,” (line 1) not an improvement to technology
Claim 9 recites collecting surface climate data, which is data gathering:
“…obtaining a daily value data set of surface climate data…” (line 2-3), data gathering
The step of obtaining and inputting data does not integrate the abstract idea into a practical application and constitutes an insignificant extra-solution activity (i.e., data gathering and presentation), which does not impose a meaningful limit on the abstract idea. As discussed above, there are no additional limitations to indicate that the claimed analysis engine requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
There are no limitations that indicate that the claimed analysis engine or the formats of the provided data require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. As such, claims 1-9 are directed to an abstract idea.(Step 2A, Prong 2: NO).
Step 2B
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment.
As discussed above, there are no additional limitations to indicate that the claimed process requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
Furthermore, the additional elements recited in the claims amount to well-understood, routine and conventional activity, as evidenced by Rashid, et al. (M. Rashid, B. S. Bari, Y. Yusup, M. A. Kamaruddin and N. Khan, "A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction," in IEEE Access, vol. 9, pp. 63406-63439, 2021) and Mohan, et al. (Mohan, A., Venkatesan, M. (2020). Spatial Data-Based Prediction Models for Crop Yield Analysis: A Systematic Review. In: Venkata Krishna, P., Obaidat, M. (eds) Emerging Research in Data Engineering Systems and Computer Communications. Advances in Intelligent Systems and Computing, vol 1054. Springer, Singapore, pp. 341-351)
Rashid, et al. discloses a review of crop yield prediction using machine learning. Although Rashid et al. primarily coves palm oil yield, they specifically cover a maize yield section with descriptions of publications that employ linear regression modeling (Section IV. B; the crop in the to-be-tested area is corn; a method for determining the hierarchical linear regression model; constructing a first regression equation and a second regression equation; obtaining a test normalized difference vegetation index and test meteorological data of a to- be-tested area) using normalized difference vegetation index (Section V, F; obtaining a test normalized difference vegetation index and test meteorological data of a to- be-tested area). Additionally, Mohan, et al. discloses a review of spatial data-based prediction models that include regression models using NDVI for maize yield predictions (Introduction, Sec. 2.2 Vegetation Indices; Sec. 3.4 Prediction Models; constructing a first regression equation and a second regression equation; obtaining a test normalized difference vegetation index and test meteorological data of a to- be-tested area.) Thus, the additional elements recited in the claims are conventional.
MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-9 is/are not patent eligible.
Claim Rejections - 35 USC § 102
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-10 are rejected under 35 U.S.C. 102 (b) as being anticipated by Zhu, et al. (“Remote Sensing", vol. 13, no. 3, pp. 356 (1-14), publication date 21 January 2021).
Zhu, et al. is directed to a crop yield prediction method.
Claim 1 is directed to:
A crop yield prediction method, comprising:
obtaining a test normalized difference vegetation index and test meteorological data of a to- be-tested area;
inputting the test normalized difference vegetation index and the test meteorological data into a hierarchical linear regression model, to obtain a predicted yield of the to-be- tested area;
wherein a method for determining the hierarchical linear regression model is: obtaining a training normalized difference vegetation index of a crop planting area;
obtaining training meteorological data and measured yield data of the crop planting area;
constructing a first regression equation and a second regression equation,
wherein dependent variables of the second regression equation are a slope and an intercept of the first regression equation; and
inputting the training normalized difference vegetation index and the measured yield data into the first regression equation, and the training meteorological data into the second regression equation to train the first regression equation and the second regression equation, and determining the trained first regression equation as the hierarchical linear regression model.
Regarding claims 1 and 10, Zhu, et al. discloses a crop yield prediction method that:
obtains the normalized difference vegetation index (NDVI) (Abstract; Sec. 3.3; Table 2), obtains test meteorological data (Sec. 3.3, “…the date of the critical reproductive period of the meteorological station during 2016–2019 were downloaded from the National Information Center of the China Meteorological Administration”),
inputs the test (NDVI) and test meteorological data into a hierarchical linear regression model (HLM) (Introduction, “…Landsat 8 images, meteorological and measured yield data were acquired to determine the optimal vegetation index (VI) for maize-yield prediction using the LR method, and to build regional maize-yield prediction models using HLM and assess its accuracy”; Sec 2, Methods, Sec. 3.5, para. 1),
obtains a predicted yield of the to-be-tested area (Figure 3),
obtains a NDVI of a crop planting area (Figure 5, Discussion, Sec. 5.1, 5.2, “HLM provides a good direction for yield prediction, but its accuracy still has much room for improvement…”),
obtains training NDVI and measured yield data from the crop planting area (Sec 5.1, Discussion, “We further analyzed the relationship between meteorological factors and equation parameters (slope and intercept). Results showed that the slope and intercept of the yield regression equation had obvious correlation with meteorological factors, especially rainfall (PRE), which proved the initial hypothesis that the environment influences NDVI variations.”),
constructs a first regression equation and a second regression equation (Sec. 2, Methods, “HLM is a hierarchical hybrid model, which is an upgraded model of LR regression. The HLM used in this study consisted of two levels. The first level is similar to the ordinary LR model, which contains independent remote sensing data variables and yield. The independent variables in the second layer were environmental factors.” wherein,
dependent variables of the second regression equation are a slope and an intercept of the first regression equation (Sec. 2, Methods, “The dependent variable corresponds to slope and intercept in the first layer model.”),
inputs the training NDVI and the measured yield data into the first regression equation and the training meteorological data into the second regression equation to train the first regression equation and the second regression equation (Sec. 4.2.1, “VIs and meteorological data were used in the yield-predicting model established by HLM”; Sec 2, Methods; “The HLM prediction model used in this study is a two-level, completely random coefficient model. Level-1yield is an LR model about VI and yield… In the second level of the model…meteorological factors constitute the equation.”, and
determines the trained first regression equation as the hierarchical linear regression model (HLM) (Sec 4.2; “Among these three indices, the HLM constructed by NDVI had the highest precision…”).
Zhu et al.’s HLM model teaches on all the limitations of claim 1 and is done so computationally; therefore, Zhu et al. anticipates claim 1.
Dependent claim 2 is directed to the crop yield prediction method of claim 1, wherein the obtaining a training normalized difference vegetation index of a crop planting area comprises:
obtaining remote sensing image data of the crop planting area;
calculating a spectral reflectance based on the remote sensing image data; and
performing band calculation on the spectral reflectance to obtain the training normalized difference vegetation index.
Regarding dependent claim 2, Zhu et al. obtains remote sensing image data of a crop planting area (Sec. 3.2, Remote Sensing Data), calculates a spectral reflectance based on the remote sensing image data, (Sec. 3.5, “Field data, including yield data, spectral VI data, and meteorological factors, collected from 10 areas (n = 100, calibration group), were used to calculate Pearson’s correlation coefficient between yield and VIs.”; Sec. 4.1, Table 2), and performs band calculation on the spectral reflectance, obtaining training NDVI. As Zhu et al obtains training NDVI, it is inherent that acquiring a training normalized vegetation index of the crop growing area includes acquiring telemetric image data of the crop growing area; calculating spectral reflectivity from the telemetric image data; performing band calculation on the spectral reflectivity resulting in the training normalized vegetation index. Therefore, Zhu et al. addresses all limitations of claim 2. Zhu et al. anticipates claim 2.
Dependent claim 3 is directed to characteristics of the remote sensing image data. It stipulates the data is Landsat image data with the bands of the Landsat image data comprising blue, green, red, and near-infrared.
Regarding dependent claim 3, Zhu et al. discloses use of Landsat 8 imaging data (Introduction, pg. 2). It is inherent that Landsat 8 imaging data captures images within the blue, green, red, and near-infrared spectrum, as evidenced by the United States Geological Survey (USGS) Common Landsat Band RGB Composites (USGS, 2017 August 04). Therefore, Zhu et al. anticipates claim 3.
Dependent claim 4 is directed to a formula for performing band calculation on spectral reflectance, comprising variables for spectral reflectance of a near-infrared band, a spectral reflectance of red band, and NDVI.
Regarding dependent claim 4, Zhu et al. discloses a HLM equation and several other formulas and references for vegetation indexes (VI) (Table 2, pg. 5). Within Zhu et al.’s table, Zhu et al. discloses spectral reflectance of red band (for example, R890) in a formula to calculate NDVI. Therefore, Zhu et al. anticipates claim 4.
Regarding dependent claim 5, Zhu et al. discloses the following features (in Table 2, pg. 5; Abstract, sections 1 to 6): The first layer is a linear regression LR model for optimal vegetation index VI and yield as shown in equation (1): (1), where Oj is the intercept of the model, lj is the slope of the model and eij is the random error. VI and meteorological data were used in the yield prediction model built by HLM. Three vegetation indices-NDVI, WDRVI, and MSR-that were closely related to yield in the calibration dataset were involved in the development of the predictive model (Table 5 and Figure 3). Of these three metrics, HLM accuracy was highest for NDVI construction. It is implicit that the formula of the first regression equation is: Yij = 0j + lj x NVDIi + eij; wherein Oj is the intercept of the first regression equation, lj is the slope of the first regression equation, eij is the stochastic error of the first regression equation, Yij is the ith predicted yield, NDVIi is the ith normalized vegetation index of the training normalized vegetation indices, and j is a numeric subscript. Therefore, Zhu et al. anticipates claim 5
Regarding dependent claim 6, Zhu et al. discloses the following features in the Abstract and in Sections 1 to 6: Meteorological data. Minimum temperature (Tmin, ° C), maximum temperature (Tmax, ° C), number of hours of sunlight (RAD), rainfall (PRE, mm) for weather station days, dates of critical development of weather stations from 2016-2019, available from the China Weather Service National Information Center (pg. 2-3; Table 2; Table 6). The mean value of the meteorological data 1 month before the grouting period was calculated as the meteorological factor variable of the HLM (Sec. 3.3). At the second layer of the model, Oj, lj and meteorological factors constitute equations. Mean meteorological data before the grouting period (number of hours of sunlight RAD, rainfall PRE, maximum temperature Tmax, minimum temperature Tmin) are independent variables as shown in equation (2) (pg. 3). Equation (2) shows mj represents the intercept Oj and the slope 1j of the Level-1 model, m0 is the intercept of the Level-2 model, m1-m4 represent the slope of the meteorological parameter, and umj represents the stochastic error of this horizontal function (pg. 3). Zhu et al showed that precipitation in different regions affects the prediction accuracy of both models (Figure 5b).
It is implicitly disclosed (accounting for the levels of the equation) that the formula of the second regression equation is:
Oj = 00 + 01 x RAD + 02 x Tmax + 03 x Tmin + 04 x PRE + Oj;
1j = 10 + 11 x RAD + 12 x Tmax + 13 x Tmin + 14 x PRE + 1j;
wherein, 00 is a first intercept of the second regression equation and 10 is a second intercept of the second regression equation, RAD is the average number of insolation hours in the training meteorological data, 01 is a first slope of the average number of insolation hours and 11 is a second slope of the average number of insolation hours, Tmax is an average daily maximum temperature in the training meteorological data, 02 is a first slope of the average daily maximum temperature, 12 is a second slope of the average daily maximum temperature, Tmin is an average daily minimum temperature in the training meteorological data, 03 is a first slope of the average daily minimum temperature, 13 is a second slope of the average daily minimum temperature, PRE is an average daily precipitation in the training meteorological data, 04 is a first slope of the average daily precipitation, 14 is a second slope of the average daily precipitation, Oj is a first random error of the second regression equation, and lj is a second random error of the second regression equation) (pg. 3). These variables match those in claim 6; therefore, Zhu et al. anticipates claim 6.
Regarding claim 7, Zhu et al. discloses in their abstract and statistical analysis section 3.3 that 100 samples from 10 areas, and 101 other samples from 34 areas were used in this study, implicitly disclosing corn (“maize”) as a crop in the to-be-tested area. Therefore, as Zhu et al. includes corn in their analysis, anticipates claim 7.
Regarding claim 8, Zhu et al. discloses in section 3.2 that “Image acquisition time was within 1 week after the crops had entered the filling stage.” Therefore, Zhu et al. anticipates claim 8.
Regarding claim 9, Zhu et al. discloses in the abstract and methods (Sec 2, pg. 2-3) a model using 100 samples from 10 regions and evaluated the model's performance in Gillin Province using 101 other samples from 34 regions (Abstract, Sec. 3). Zhu et al. discloses field data collected from 10 regions (n = 100, calibration set), including yield data, spectral VI data, and meteorological factors, were used to calculate Pearson correlation coefficients between yield and VI (Sec. 3.5). Zhu et al. built the linear regression model with VI and the MLR and HLM predicted yields are built with VI and meteorological variables (Intro, “Therefore, in this study, Landsat 8 images, meteorological and measured yield data
were acquired to determine the optimal vegetation index (VI) for maize-yield prediction using the LR method, and to build regional maize-yield prediction models using HLM and assess its accuracy.”, pg. 2; Sec 2, Methods, Sec 3.5). Zhu et al further discloses that other field data obtained from 34 regions (n = 101, validation set) were used to compare the yield estimation accuracy of the models. The ratio of calibration and validation sets was 1:1 (Sec 3.5). In the calibration set, only a small number of regional samples were selected for model building. On the other hand, in the validation set, sample points from more regions are included to validate the suitability of the model in unknown regions. Meteorological data, Minimum temperature (Tmin, ° C), maximum temperature (Tmax, ° C), number of hours of sunlight (RAD), rainfall (PRE, mm) for weather station days, dates of critical development of weather stations from 2016-2019, are available from the China Weather Service National Information Center. The mean value of the meteorological data 1 month before the grouting period was calculated as the meteorological factor variable of the HLM. Further analysis of the results showed that precipitation in different regions affected the prediction accuracy of both models (Figure 5b). Scatter plots with normalized root mean square error (nRMSE) differences for multiple linear regression (MLR) and hierarchical linear modeling (HLM) models as the horizontal axis and rainfall as the vertical axis are divided into three parts. In the first part, the daily average precipitation was less than 7.5 mm, the difference in nRMSE for the MLR and HLM models was controlled to within 5%, and the distribution of nRMSE over time and region was random. The second and third sections indicate that HLM has better predicted regions. In the second part, the daily average precipitation is below 7.5 mm and the difference between the two predictive models of nRMSE is more than 5%.” (Sec 4.3.2). Zhu et al further discloses, in Sec 4.3.2 that the third part of the average daily rainfall is greater than 7.5 mm, the difference in nRMSE is increased and the instability of the MLR is more prominent. As daily precipitation increases, the prediction accuracy in regions presents a trend towards large differences in nRMSE (MLR)-nRMSE (HLM). MLR performance is more unstable in areas where average daily rainfall is low or high. Therefore, by applying and analyzing equations 1 and 2 (pg. 2-3) using computational methods (satellite imaging data, for example), Zhu et al implicitly discloses obtaining a daily value data set of surface climate data, wherein the daily value data set of surface climate data comprises daily maximum temperature, daily minimum temperature, daily precipitation, and sunshine duration of the to-be-tested area; and calculating the training meteorological data based on the daily value data set of surface climate data, wherein the training meteorological data comprises average daily maximum temperature, average daily minimum temperature, average daily precipitation, and average sunshine duration. Therefore, Zhu et al. anticipates claim 9.
Conclusion
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/J.T.S./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686