DETAILED 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 .
Applicant’s amendments and request for reconsideration filed 5/26/2026 are acknowledged and entered.
Withdrawn Rejections/Objections
The rejection of claim 10 in the Office Action mailed 02/26/2026 is withdrawn in view of the cancellation of said claim, filed 05/26/2026.
Rejections and/or objections not reiterated from previous office actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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
Claim 10 is canceled.
Claims 1-9 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.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 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:
“…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.
Response to Arguments
Claim Rejections - 35 USC § 101
Applicant's arguments filed 05/26/2026 have been fully considered but they are not persuasive.
The applicant traverses the rejection of claims 1-9 under 35 USC § 101 as not establishing a prima facie case of obviousness, stating that, in light of the amendments:
The amended claims are self-evident as eligible subject matter.
The amended claims are not directed to an abstract idea.
Amended Claim 1 Recites a Specific Machine, Not a Generic Computer.
Amended Claim 1 Achieves a Concrete Technical Improvement.
The Technical Advantages of the Specific Combination Are Evidence of Non Conventionality.
The Examiner respectfully asserts that above arguments are not persuasive. Regarding Claims 1-9, the applicant traverses the rejection under 35 U.S.C. §101.
Regarding Step 2A Prong 1, the applicant disagrees that the instant claims recite an abstract idea. There are no arguments presented to Step 2A Prong 1 that would suggest the claims recited in the previous action as containing a judicial exception do not recite said judicial exception. In view of the amendment to independent claim 1, which states “…obtaining test meteorological data of the to-be-tested area… and displaying the predicted yield of the to-be-tested area on the display”, there is no disclosure in the claims nor specification that would define “obtaining” and “displaying” as necessitating anything other than a mental process. Additionally, the amended claim 1 adds “a processor, a display and a memory storing a hierarchical linear regression model and instructions, and the instructions are executed by the processor to implement the crop yield prediction method comprises…” to the preamble; however, this reads as intended use and does not effect the claim analysis under Step 2A prong 1 (see MPEP 2111.02)
Regarding Step 2A, Prong 2, The applicant asserts that the claims integrate the abstract ideas into a practical application.
Regarding the analysis of claims 1-9 under Step 2A Prong 2, Step 2A Prong 2 examines if claims reciting judicial exceptions under prong one are integrated into a practical application.
The applicant states: “…amended independent claim 1 is directed to improving the existing genome technologies, which includes additional features that are significantly more than a judicial exception, and is integrated into a practical application.”
Firstly claims 1, 4-6 and 9 recite elements which are not considered abstract ideas, but perform functions of inputting, collecting, and outputting the data needed to carry out the abstract idea. These steps are considered insignificant extra-solution activity, and are not sufficient to integrate an abstract idea into a practical application as they do not impose any meaningful limitation on the abstract idea or how it is performed.
The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent. To integrate a judicial exception into a practical application, the additional limitations must not be mere instructions to apply the judicial exception [see MPEP § 2106.04(d) and MPEP § 2106.05(g)].
Secondly, claim 1recites the additional elements of a programmed computer, processing unit and a memory. Neither the programmed computer, processing unit and nor a memory apply the mathematical processes into any practical application, rather, they are simply devices for carrying out the judicial exception (mental process).
The use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. The Court found that the recitation of the computer in the claim amounted to mere instructions to apply the abstract idea on a generic computer. 573 U.S. at 225-26, 110 USPQ2d at 1984. The Supreme Court also discussed this concept in an earlier case, Gottschalk v. Benson, 409 U.S. 63, 70, 175 USPQ 673, 676 (1972), where the claim recited a process for converting binary-coded-decimal (BCD) numerals into pure binary numbers. The Court found that the claimed process had no meaningful practical application except in connection with a computer. Benson, 409 U.S. at 71-72, 175 USPQ at 676. The claim simply stated a judicial exception (e.g., law of nature or abstract idea) while effectively adding words that "apply it" in a computer. Therefore, claims 1-20 do not integrate abstract ideas into a practical application.
Finally, Step 2B examines if claims that do not integrate the judicial exception provide an inventive concept (See MPEP § 2106.05).
Explaining the Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018) as an example of using the computer as a tool to perform a mental process. The MPEP stated "The patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes. 881 F.3d at 1366, 125 USPQ2d at 1652-53"
As indicated in the summary of the Berkheimer v. HP ruling above and in view of the specifications, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exceptions because the claims recite additional elements that are generic, conventional, or nonspecific. Those additional elements are as follows:
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 additional elements of obtaining information do not cause the claims to rise to the level of significantly more than the judicial exception. The courts have recognized receiving or transmitting data over a network and storing and retrieving information in memory [see MPEP§2106.05(d)(II)], as well-understood, routine, conventional activity when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
Furthermore, the additional elements recited in the claims amount to well-understood, routine and conventional activity as evidenced by Rashid et al. and Mohan et al. which disclose these methods in their review. The status of Rashid et al. and Mohan et al. as review articles does not negate their disclosure of conventional activity. Therefore, the rejection of claims 1-9 under 35 U.S.C. § 101, when the limitations are considered individually and as a whole, is maintained.
Claim Rejections - 35 USC § 103
The following rejection is newly recited.
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, 4-6 are rejected under 35 U.S.C. 103 as being unpatentable over Zhenhai Li et al (Field Crops Research Volume 248, 1 March 2020, 107711, pg. 1-9).
Regarding claim 1, Li et al. teaches a crop yield prediction model using remote sensing data and hierarchical linear modeling (HLM) (Abstract, Introduction). Li et al. states meteorological data was collected and downloaded in section 2.2.1, suggesting computer use (re: clm. 1, … A crop yield prediction method, which is implemented by a crop yield prediction system, comprising a processor, a display and a memory storing a hierarchical linear regression model and instructions, and the instructions are executed by the processor to implement the crop yield prediction method comprises…)
Li et al. further teaches a yield prediction model using vegetation index in section 2.3.1 and Table 2, which discloses spectral indices, and obtains meteoroligcal data in section 2.2.1 (re: clm. 1, … obtaining a test normalized difference vegetation index of a to-be-tested area of to-be-tested area, obtaining test meteorological data of the to-be-tested area; inputting the test normalized difference vegetation index and the test meteorological data into the hierarchical linear regression model, to obtain a predicted yield of the to-be-tested area…)
Li et al. further teaches in section 2.2 (Data acquisition, Meterological data collection) that meteorological data includes daily solar radiation, precipitation, and maximum and minimum air temperatures, which are used as variables in the equations disclosed in section 2.3.1 (Yield prediction mode) (re: clm. 1, … 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 from a daily value data set of surface climate data; obtaining measured yield data of the crop planting area stored in the memory; 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…).
Li et al. further teaches inputting vegetation index and yield into two regression equations in section 2.3.1 (re: clm. 1, …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…)
Li et al. does not explicitly teach displaying the yield (re: clm. 1, … displaying the predicted yield of the to-be-tested area on the display…)
In KSR Int 'l v. Teleflex, the Supreme Court, in rejecting the rigid application of the teaching, suggestion, and motivation test by the Federal Circuit, indicated that “The principles underlying [earlier] cases are instructive when the question is whether a patent claiming the combination of elements of prior art is obvious. When a work is available in one field of endeavor, design incentives and other market forces can prompt variations of it, either in the same field or a different one. If a person of ordinary skill can implement a predictable variation, § 103 likely bars its patentability.” KSR Int'l v. Teleflex lnc., 127 S. Ct. 1727, 1740 (2007).
Applying the KSR standard of obviousness to Li et al., the Examiner concluded that the combination of Li et al’s computational yield prediction method with a display (such as a monitor) represents an illustration of the reasoning that it would have been “obvious to try" choosing from a finite number of identified, predictable solutions to achieve the claimed process. Because displaying predicted results on a monitor (which is ubiquitous at the time of filing) would have been reasonably predictable at the time of the invention, there would have been a reasonable expectation of successful development of a computational yield prediction system applying the computational yield method to a computer with a monitor. The state of the art at the time of filing and the prior art (Li et al. suggests downloading data which is computational) indicates a finite number of reasonable solutions to the problem of displaying yield data predicted with the methods as disclosed by Li et al. Therefore, displaying the results of a prediction equation would have been obvious to one of skill in the art, because the art provides multiple strategies for achieving said display, and merely choosing one or more of these strategies represents the selection of a solution which would provide visual depiction of results and is a choice from a finite number of identified, and predictable solutions. (paraphrased from Example 2 of MPEP 2143 E.). Therefore, claim 1 would have been prima facie obvious absent evidence to the contrary.
Regarding claim 4, Li et al. teaches a formula for performing band calculation in the table of formulas (Table 2) in section 2.4 (re: clm. 4, … The crop yield prediction method according to claim 2, wherein a formula for performing band calculation on the spectral reflectance is…). Li et al. teaches the limitations of claim 4.
Regarding claim 5, Li et al. teaches the first regression equation in section 2.3.1 (re: clm. 5, The crop yield prediction method according to claim 1, wherein a formula of the first regression equation is…). Li et al. teaches the limitations of claim 5.
Regarding claim 6, Li et al. teaches in section 2.3 and 2.3.2 in their Level-2 equation use of daily solar radiation (Rad), daily maximum (Tmax) and minimum (Tmin) air temperatures, and the total precipitation (Pre) in a regression equation (re: clm. 6, …The crop yield prediction method according to claim 5, wherein a formula of the second regression equation is…). Li et al teaches the limitations of claim 6.
Regarding claim 9, Li et al. taches in section 2.3 and 2.3.2 in their Level-2 equation use of daily solar radiation (Rad), daily maximum (Tmax) and minimum (Tmin) air temperatures, and the total precipitation (Pre) in a regression equation (re: clm. 9, …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…). Li et al. uses this data in a regression equation used for prediction (sec. 2.3.1, 2.3.2, re: clm. 9, … calculating the training meteorological data…). Li et al. teaches the limitations of claim 9.
Claim(s) 2, 3, 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Zhenhai Li et al. as applied to claims 1, 4-6 and 9 above in view of Aghighi et al. (IEEE JOURNAL OFSELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,VOL.11,NO.12,DECEMBER 2018) and Markham et al. (Remote Sensing of Environment 122 (2012) 30–40).
Li et al. is applied to claims 1, 4-6 and 9 above.
Regarding claims 2, 3 and 7, Li et al. teaches a crop yield prediction (re: clm. 2, … The crop yield prediction method according to claim 1…), obtaining remote sensing image data, and calculating a spectral reflectance as disclosed in Table 2 and section 2.3 (2.3.1). Li et al. additionally teaches performing band calculation on spectral reflectance data to obtain a normalized difference vegetation index as disclosed in Table 2, which shows band calculations in the formulation column (re: clm. 2, … performing band calculation on the spectral reflectance to obtain the training normalized difference vegetation index…).
Li et al. does not explicitly disclose the use of image data or Landsat image data (re: clm. 2, … obtaining remote sensing image data of the crop planting area, clm. 3, … wherein the remote sensing image data is Landsat image data; and bands of the Landsat image data comprises blue band, green band, red band, and near-infrared band) nor does Li et al. disclose corn as the tested crop (re: clm. 7, …The crop yield prediction method according to claim 1, wherein the crop in the to-be-tested area is corn.)
Aghighi et al. teaches Machine Learning Regression Techniques for the Silage Maize Yield Prediction Using Time-Series Images of Landsat 8 OLI (Title, re: clm. 3, … wherein the remote sensing image data is Landsat image data…), and further teaches remote sensing data comprising a red, blue, green, and near-infrared band inherently through the use of Landsat image data , as disclosed by Markham et al. (pg. 1-2, table 1) (re: clm. 3, and bands of the Landsat image data comprises blue band, green band, red band, and near-infrared band…). Aghighi et al. further teaches a study of corn (Maize, re: clm. 7, …The crop yield prediction method according to claim 1, wherein the crop in the to-be-tested area is corn…).
Applying the KSR standard to Li et al., Aghighi et al. and Markham et al., the examiner concludes that some teaching, suggestion, or motivation in the prior art would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
One of ordinary skill in the art of agriculture would have been motivated to apply the Landsat image data analysis technique as applied to corn as disclosed by Aghighi et al. to the grain yield measurement method as disclosed by Li et al. as the combination would lead to a stronger grain yield measurement method
One of skill in the art before the effective filing date of the claimed invention would have had a reasonable expectation of success of applying the Landsat 8 method to the wheat yield prediction model as both methods exist in the same field of invention. Therefore, the invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Regarding claim 8, Li et al. teaches crop yield prediction but does not disclose corn in the grain filling stage (re: clm. 8, …corn is in the grain filling stage.)
Aghighi et al. teaches remote imaging of corn as previously disclosed.
Applying the KSR standard of obviousness to Li et al., Aghighi et al. and Markham et al the examiner concludes that some teaching, suggestion, or motivation in the prior art would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
One of ordinary skill in the art of agriculture would have been motivated to apply the Landsat image data analysis technique as applied to corn as disclosed by Aghighi et al. to the grain yield measurement method as disclosed by Li et al. as the combination would lead to a stronger grain yield measurement method. In support of this motivation, Li et al. states: “remote sensing data has the potential to represent the crop growth status at multiple time points and to be interactive with the climate environment information”, suggesting the desire to capture images of a crop at multiple time points along its growth stage.
One of skill in the art before the effective filing date of the claimed invention would have had a reasonable expectation of success of applying the Landsat 8 method to the wheat yield prediction model as both methods exist in the same field of invention. Therefore, the invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Response to Arguments
Claim Rejections - 35 USC § 103
The applicant traverses the rejection of claims 1-9 under 35 USC § 103 as not establishing a prima facie case of obviousness, stating that the prior art (Zhu et al. ("Remote Sensing", vol. 13, no. 3, pp. 356 (1-14), publication date January 21, 2021) as submitted with the Office Action mailed 2/26/2026 is ineligible. This argument is persuasive. The rejection of claims 1-9 under 35 USC § 103 is newly recited in light of the amendments.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN T STUBBS whose telephone number is (571)272-0340. The examiner can normally be reached M-F 8-5 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Larry Riggs can be reached at 571-270-3062. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/J.T.S./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
Prosecution Insights