DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Responses to Amendments and Arguments
The amendments filed 04/13/2026 have been entered. Claims 1 and 5 are amended, and Claim 6 and 11 are canceled. Claims 1-5, 7-10 and 12 remain pending in the application.
Applicant’s amendments filed 04/13/2026 have been fully considered overcome specification objections previously set forth in the Non-Final Office Action mailed 01/13/2026.
Applicant's argument and amendments filed 04/13/2026 with respect to
the rejection of claims 1-12 directed to a judicial exception under 35 U.S.C. 101 have been fully considered but are not persuasive. (See the detailed response presented below).
On pages 9-12 of Applicant’s response, Applicant alleges that the amended claims are drawn toward patent-eligible subject matter. … the claims are not directed to a mathematical concept but instead are directed toward detecting contents of five forms of nitrogen in the soil profile samples, including using the semi-micro-Kjeldahl method, the alkali-hydrolyzable diffusion method, a KCl-extracted continuous flow analyzer, and a chloroform fumigation and K2SO4 extraction method. The above detection methods involve a series of operations performed in the laboratory and therefore are not mathematical concepts nor performed in the human brain. … the claims are clearly a practical application, in that the claims generate and produce a tangible result that provides a meaningful improvement over existing technology as recognized by the specification. See, e.g., MPEP § 2106.05. As recited in amended independent claim 1, hyperspectral prediction models are trained using the data obtained using a semi-micro-Kjeldahl method, an alkali-hydrolyzable diffusion method, a Kl-extracted continuous flow analyzer, or by a chloroform fumigation and K2SO4 extraction method. Furthermore, the optimal prediction model is selected from hyperspectral prediction models based on evaluation indexes. Accordingly, the resulting model is not a general purpose artificial intelligence model but a specific model. … the present application is directed to an improvement in the technical field of soil nitrogen detection. … Applicant respectfully submits that the claims, when considered as a whole, recite additional elements that amount to significantly more than a judicial exception. … This process consumes a large amount of chemical reagents, is time-consuming and laborious, causing environmental pollution. Moreover, special analytical instruments are needed and are inconvenient to use. In addition, a final determining analysis result provides only an average value of the nitrogen content in the soil sample. The present application improves this technical field in two ways. First, it integrates specific, disparate laboratory techniques (e.g., semi-micro-Kj eldahl method, alkali-hydrolyzable diffusion method, KCl-extracted continuous flow analyzer, chloroform fumigation and K2SO4 extraction method) to generate a comprehensive and accurate set of soil nitrogen data. Second, it uses this specifically generated data to train and select an optimal hyperspectral prediction model. The resulting method reduces chemical reagent consumption, improves detection accuracy, and decreases time and labor costs.
The Examiner respectfully disagrees.
Note that the step of “detecting contents of at least five forms of nitrogen …” may encompass manually calculating or inferring the standard content of each form of soil nitrogen based on the detected contents in the soil profile samples (see at least paragraphs 0044, 0053- 0057 and 0065). (MPEP 2106.04(a)(2)). The feature related to “detecting contents of at least five forms of nitrogen …” is not a meaningful limitation to improvements to obtaining the standard content, but the claim does not present any specific or tangible feature/operation/act as to what the detecting step as well as a series of its involved operations are indicative of and/or how/what a specific or tangible element/act may be performed/configured to detect the contents of at least five forms of nitrogen to thereby obtain the standard content. (See MPEP 2106.05(f) and 2106.05(g)). Note that the added limitation of “wherein the five forms of nitrogen are soil total nitrogen, alkali- hydrolyzable nitrogen, ammonium nitrogen, nitrate nitrogen, and microbial biomass nitrogen (MBN), and content of the soil total nitrogen in the soil profile samples is determined by using a semi-micro-Kjeldahl method, content of the alkali-hydrolyzable nitrogen in the soil profile samples is determined by using an alkali-hydrolyzable diffusion method, content of the nitrate nitrogen and content of ammonium nitrogen in the soil profile samples are determined by a KCl- extracted continuous flow analyzer, and content of MBN is determined by a chloroform fumigation and K2SO4 extraction method” is not a meaningful limitation to improvements to the step of “detecting contents of at least five forms of nitrogen”, because “the five forms of nitrogen are soil total nitrogen, alkali- hydrolyzable nitrogen, ammonium nitrogen, nitrate nitrogen, and microbial biomass nitrogen (MBN), and content of the soil total nitrogen in the soil profile samples is determined ..” is merely indicative of a field of use related to the forms of nitrogen. Further, note than the alkali-hydrolyzable diffusion method, the KCl- extracted continuous flow analyzer, the chloroform fumigation and K2SO4 extraction method are high-level of generalities merely recited to perform generic computer functions of a generic computer component and/or mathematical algorithm, and these methods are indicative of mathematical concepts without any specific or tangible feature/operation/act as to how and/or with, for example, what factors/values/parameter/frequency/characteristic these methods and the analyzer perform detecting the contents of at least five forms of nitrogen to thereby obtain the standard content of each form of soil nitrogen.
Note that “hyperspectral prediction models are trained using the data obtained …” is insignificant extra-solution activity to merely execute a computer program and/or data process related to training the hyperspectral prediction models, where the method and the analyzer (a semi-micro-Kjeldahl method, an alkali-hydrolyzable diffusion method, a Kl-extracted continuous flow analyzer, or by a chloroform fumigation and K2SO4 extraction method) are high-level of generalities to perform abstract idea as addressed above. Therefore, this judicial exception is abstract ideal itself and not integrated into a practical application, and also has no significant more beyond the abstract idea. (See the details in the modified action set forth below).
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.
The current 35 USC 101 analysis is based on the current guidance (Federal Register vol. 79, No. 241. pp. 74618-74633). The analysis follows several steps. Step 1 determines whether the claim belongs to a valid statutory class. Step 2A prong 1 identifies whether an abstract idea is claimed. Step 2A prong 2 determines whether any abstract idea is integrated into a practical application. If the abstract idea is integrated into a practical application the claim is patent eligible under 35 USC 101. Last, step 2B determines whether the claims contain something significantly more than the abstract idea. In most cases the existence of a practical application predicates the existence of an additional element that is significantly more.
The 35 USC 101 analysis between each element of claims and its combination is presented in the table below
Claim number and elements
Judicial exception (Step 2A Prong one)
Practical application (Step 2A Prong two)/ Significantly more (Step 2B)
Claim 1
Step 1: Yes, statutory class
Step 2A Prong two: No / Step 2B: No
A machine learning-based hyperspectral detection and visualization method of a nitrogen content in a soil profile, comprising at least the following steps:
Step2A Prong one: Yes
sampling soil in a detection area based on a predetermined depth to obtain a plurality of soil profile samples about the detection area;
obtaining an initial hyperspectral image of each soil profile sample, and performing image preprocessing on the initial hyperspectral image to obtain an effective hyperspectral image;
“sampling soil in a detection area ~ to obtain a plurality of soil profile samples ~” is insignificant extra-solution activities to collect data.
“obtaining an initial hyperspectral image of each soil profile sample, and performing image preprocessing ~” is insignificant extra-solution activity to perform a generic computer function of data/image processing. (para 0047-0051).
selecting n regions of interest that are continuously distributed and have the same shape and size on the effective hyperspectral image, and calculating n pieces of average spectral data based on all pixels of each region of interest, wherein n is an integer;
abstract idea
mathematical concept
“selecting n regions of interest … calculating n pieces of average spectral data” is a math process and/or data processing. (para 44, 48-0053)
detecting contents of at least five forms of nitrogen in the soil profile samples in each region of interest to obtain a standard content of each form of soil nitrogen,
wherein the five forms of nitrogen are soil total nitrogen, alkali- hydrolyzable nitrogen, ammonium nitrogen, nitrate nitrogen, and microbial biomass nitrogen (MBN), and content of the soil total nitrogen in the soil profile samples is determined by using a semi-micro-Kjeldahl method, content of the alkali-hydrolyzable nitrogen in the soil profile samples is determined by using an alkali-hydrolyzable diffusion method, content of the nitrate nitrogen and content of ammonium nitrogen in the soil profile samples are determined by a KCl- extracted continuous flow analyzer, and content of MBN is determined by a chloroform fumigation and K2SO4 extraction method; and
abstract idea
mathematical concept
“detecting contents of at least five forms of nitrogen in the soil profile samples in each region of interest to obtain a standard content …” is a math process. (para 0044, 0053-0057, 0065).
establishing a plurality of hyperspectral prediction models by using at least one learning algorithm;
selecting an optimal prediction model corresponding to a soil nitrogen form from the plurality of hyperspectral prediction models based on evaluation indexes, predicting a soil nitrogen content corresponding to each pixel of the hyperspectral image of the soil profile in the corresponding form based on the optimal prediction model, denoting the soil nitrogen content as a predicted soil nitrogen content, and
outputting the predicted soil nitrogen content to obtain a visualized image.
abstract idea
mathematical concept
“establishing ~” is a math process. (para 0057-0076).
“selecting an optimal prediction model …, predicting a soil nitrogen content … denoting the soil nitrogen content …” is a math process. (para 0061-76).
“outputting …” is insignificant extra-solution activity to perform a generic computer function of data processing.
Claims 1-5, 7-10 and 12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-5, 7-10 and 12 are directed to an abstract idea. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception as addressed below and presented in the above table.
Step 2A: Prong One
Regarding Claim 1, the limitations recited in Claim 1, as drafted, are processes that, under its broadest reasonable interpretation, cover performance of the limitation in the mathematical calculations and/or the mind, as presented in the above table. Nothing in the claim elements precludes the step from practically being performed in the mind and/or the mathematical calculations. For example, “selecting n regions of interest that are continuously distributed and have the same shape and size on the effective hyperspectral image, and calculating n pieces of average spectral data based on all pixels of each region of interest, wherein n is an integer” in the context of this claim may encompass manually calculating or inferring the n pieces of average spectral data based on the selected regions of interest in the obtained hyperspectral image which is indicative of collected data used for perform abstract idea of mathematical calculations (see at least paragraphs 0044 and 0048-0053). (MPEP 2106.04(a)(2)). For example, “detecting contents of at least five forms of nitrogen in the soil profile samples in each region of interest to obtain a standard content of each form of soil nitrogen, wherein the five forms of nitrogen are soil total nitrogen, alkali-hydrolyzable nitrogen, ammonium nitrogen, nitrate nitrogen, and microbial biomass nitrogen (MBN), and content of the soil total nitrogen in the soil profile samples is determined by using a semi-micro-Kjeldahl method, content of the alkali-hydrolyzable nitrogen in the soil profile samples is determined by using an alkali-hydrolyzable diffusion method, content of the nitrate nitrogen and content of ammonium nitrogen in the soil profile samples are determined by a KCl- extracted continuous flow analyzer, and content of MBN is determined by a chloroform fumigation and K2SO4 extraction method” in the context of this claim may encompass manually calculating or inferring the standard content of each form of soil nitrogen based on the detected contents in the soil profile samples (see at least paragraphs 0044, 0053- 0057 and 0065). (MPEP 2106.04(a)(2)). The limitation of “the five forms of nitrogen are soil total nitrogen, alkali-hydrolyzable nitrogen, ammonium nitrogen, nitrate nitrogen, and microbial biomass nitrogen (MBN), and content of the soil total nitrogen in the soil profile samples is determined ..” is merely indicative of a field of use related to the forms of nitrogen, where the alkali-hydrolyzable diffusion method, the KCl-extracted continuous flow analyzer, the chloroform fumigation and K2SO4 extraction method are high-level of generalities merely recited to perform mathematical calculations related to the detecting step.
For example, “establishing a plurality of hyperspectral prediction models by using at least one learning algorithm” in the context of this claim may be indicative of image/data processing itself which may encompass manually calculating or inferring the prediction models using a mathematical algorithm (i.e., learning algorithm) (see at least paragraphs 0057-0076). (MPEP 2106.04(a)(2)). For example, “selecting an optimal prediction model corresponding to a soil nitrogen form from the plurality of hyperspectral prediction models based on evaluation indexes, predicting a soil nitrogen content corresponding to each pixel of the hyperspectral image of the soil profile in the corresponding form based on the optimal prediction model, denoting the soil nitrogen content as a predicted soil nitrogen content” in the context of this claim may be indicative of image/data processing itself which may encompass manually calculating or inferring the optimal prediction model, the soil nitrogen content in the hyperspectral image of the soil profile using a mathematical algorithm (i.e., learning algorithm) (see at least paragraphs 0061-0076). (MPEP 2106.04(a)(2)).
Step 2A: Prong Two
This judicial exception is abstract ideal itself and not integrated into a practical application. In particular, the specification details use of a computer processor to perform mathematical calculations or mental processes of “selecting n regions of interest that are continuously distributed and have the same shape and size on the effective hyperspectral image, and calculating n pieces of average spectral data based on all pixels of each region of interest, wherein n is an integer”, “detecting contents of at least five forms of nitrogen in the soil profile samples in each region of interest to obtain a standard content of each form of soil nitrogen”, “establishing a plurality of hyperspectral prediction models by using at least one learning algorithm” and “selecting an optimal prediction model corresponding to a soil nitrogen form from the plurality of hyperspectral prediction models based on evaluation indexes, predicting a soil nitrogen content corresponding to each pixel of the hyperspectral image of the soil profile in the corresponding form based on the optimal prediction model, denoting the soil nitrogen content as a predicted soil nitrogen content”. The limitations of “sampling soil in a detection area based on a predetermined depth to obtain a plurality of soil profile samples about the detection area” and “obtaining an initial hyperspectral image of each soil profile sample, and performing image preprocessing on the initial hyperspectral image to obtain an effective hyperspectral image” are insignificant extra-solution activities necessary to merely gather data (i.e., soil profile samples and hyperspectral image) by performing generic computer functions of a generic computer component such as, for example, a visible-near infrared spectroscopy (see Background in the instant application). See MPEP 2106.05(g). The limitation of “wherein the five forms of nitrogen are soil total nitrogen, alkali- hydrolyzable nitrogen, ammonium nitrogen, nitrate nitrogen, and microbial biomass nitrogen (MBN), and content of the soil total nitrogen in the soil profile samples is determined by using a semi-micro-Kjeldahl method, content of the alkali-hydrolyzable nitrogen in the soil profile samples is determined by using an alkali-hydrolyzable diffusion method, content of the nitrate nitrogen and content of ammonium nitrogen in the soil profile samples are determined by a KCl- extracted continuous flow analyzer, and content of MBN is determined by a chloroform fumigation and K2SO4 extraction method” is not a meaningful limitation to improvements to the step of “detecting contents of at least five forms of nitrogen”, because “the five forms of nitrogen are soil total nitrogen, alkali- hydrolyzable nitrogen, ammonium nitrogen, nitrate nitrogen, and microbial biomass nitrogen (MBN), and content of the soil total nitrogen in the soil profile samples is determined ..” is merely indicative of a field of use related to the forms of nitrogen. Further, note than the alkali-hydrolyzable diffusion method, the KCl- extracted continuous flow analyzer, the chloroform fumigation and K2SO4 extraction method are high-level of generalities merely recited to perform generic computer functions of a generic computer component and/or mathematical algorithm, and these methods (the alkali-hydrolyzable diffusion method, the KCl- extracted continuous flow analyzer, the chloroform fumigation and K2SO4 extraction method) are indicative of mathematical concepts without any specific or tangible feature/operation/act as to how and/or with, for example, what factors/values/parameter/frequency/characteristic these methods and the analyzer perform detecting the contents of at least five forms of nitrogen to thereby obtain the standard content of each form of soil nitrogen. The limitation of “outputting the predicted soil nitrogen content to obtain a visualized image” is insignificant post-solution activity necessary to merely display the visualized image of the predicted soil nitrogen content which is obtained from the mathematical calculations related to data/image processing. See MPEP 2106.05(g). Claim 1 does not present tangible or physical elements/components and/or integration of improvements to be indicative of specific features/structure/acts how and or with what to detect a nitrogen content in a soil profile and perform visualization of the predicted soil nitrogen content. (See MPEP 2106.04(d)). Claim 1 does not present a technical solution to a technical problem by providing an improvement to the functioning of computer, or to any other technology or technical field related to detecting a nitrogen content in a soil profile and performing visualization of the predicted soil nitrogen content. (See MPEP 2106.04(d)). Therefore, there is no showing of integration into a practical application such as an improvement to the functioning of a computer, or to any other technology or technical field, or use of a particular machine.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations of “sampling soil in a detection area based on a predetermined depth to obtain a plurality of soil profile samples about the detection area” and “obtaining an initial hyperspectral image of each soil profile sample, and performing image preprocessing on the initial hyperspectral image to obtain an effective hyperspectral image” are insignificant pre-solution activities necessary to merely gather data (i.e., soil profile samples and hyperspectral image) by performing generic computer functions of a generic computer component such as, for example, a visible-near infrared spectroscopy (see Background in the instant application). The limitation of “wherein the five forms of nitrogen are soil total nitrogen, alkali- hydrolyzable nitrogen, ammonium nitrogen, nitrate nitrogen, and microbial biomass nitrogen (MBN), and content of the soil total nitrogen in the soil profile samples is determined by using a semi-micro-Kjeldahl method, content of the alkali-hydrolyzable nitrogen in the soil profile samples is determined by using an alkali-hydrolyzable diffusion method, content of the nitrate nitrogen and content of ammonium nitrogen in the soil profile samples are determined by a KCl- extracted continuous flow analyzer, and content of MBN is determined by a chloroform fumigation and K2SO4 extraction method” is not a meaningful limitation to improvements to the step of “detecting contents of at least five forms of nitrogen”, because “the five forms of nitrogen are soil total nitrogen, alkali- hydrolyzable nitrogen, ammonium nitrogen, nitrate nitrogen, and microbial biomass nitrogen (MBN), and content of the soil total nitrogen in the soil profile samples is determined ..” is merely indicative of a field of use related to the forms of nitrogen. The limitation of “outputting the predicted soil nitrogen content to obtain a visualized image” is insignificant post-solution activity necessary to merely display the visualized image of the predicted soil nitrogen content which is obtained from the mathematical calculations related to data/image processing. See MPEP 2106.05(d). As discussed above, with respect to integration of the abstract idea into a practical application, using a computer system to perform “sampling soil in a detection area based on a predetermined depth to obtain a plurality of soil profile samples about the detection area”, “obtaining an initial hyperspectral image of each soil profile sample, and performing image preprocessing on the initial hyperspectral image to obtain an effective hyperspectral image”, “selecting n regions of interest that are continuously distributed and have the same shape and size on the effective hyperspectral image, and calculating n pieces of average spectral data based on all pixels of each region of interest, wherein n is an integer”, “detecting contents of at least five forms of nitrogen in the soil profile samples in each region of interest to obtain a standard content of each form of soil nitrogen”, “establishing a plurality of hyperspectral prediction models by using at least one learning algorithm” and “selecting an optimal prediction model corresponding to a soil nitrogen form from the plurality of hyperspectral prediction models based on evaluation indexes, predicting a soil nitrogen content corresponding to each pixel of the hyperspectral image of the soil profile in the corresponding form based on the optimal prediction model, denoting the soil nitrogen content as a predicted soil nitrogen content” and “outputting the predicted soil nitrogen content to obtain a visualized image” amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept cannot provide statutory eligibility. Claim 1 is not patent eligible.
Regarding Claims 2-5, 7-10 and 12, the limitations are further directed to an abstract idea, as described in claim 1. The limitations of “detecting outliers of all the average spectral data … randomly dividing filtered average spectral data … assigning a value range and a search step size to parameters … performing parameter optimization … establishing a regression relationship ...” in Claim 2, and “evaluating evaluation values …” in Claim 4 may encompass manually calculating or inferring the regression relationship between hyperspectral signals and different soil nitrogen contents, evaluation values using a mathematical model (see at least paragraphs 0053-0076). (MPEP 2106.04(a)(2)). The limitation of “at least a partial least square regression (PLSR) algorithm, an artificial neuron network (ANN) algorithm, and a support vector machine regression (SVMR) algorithm; and correspondingly, the plurality of hyperspectral prediction models are a PLSR prediction model, an ANN prediction model, and an SVMR prediction model” in Claim 3 may be indicative of mathematical algorithm itself to be performed by a generic computer function of a generic computer component.
The limitation of “obtaining a soil hyperspectral image based on a soil profile sample, obtaining each pixel on the soil hyperspectral image and a corresponding spectral reflectance curve, inputting the spectral reflectance curve into the optimal prediction model, and obtaining a predicted gray-scale image by means of the optimal prediction model, …; and performing pseudo-color processing on the predicted gray-scale image to obtain a visualized image about contents of total nitrogen, …” in Claim 5, “performing gray-scale and geometric correction …” in Claim 8 may encompass manually calculating or inferring the visualized image about the contents performed by mathematical processes related to data/image processing (see at least paragraphs 0053-0076). (MPEP 2106.04(a)(2)). The limitation of “one or more of an apparent absorption rate, a first derivative, a second derivative, Savitzky-Golay smoothing, a Gap-Segment derivative, detrending, or standard normal variable transformation” in Claim 9 is indicative of mathematical values/amounts/factors used for mathematical calculation as set forth above. For the reasons described above with respect to Claims 2-5, 7-10 and 12, the judicial exceptions are not meaningfully integrated into a practical application, or amount to significantly more than the abstract idea.
Citation of Pertinent Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jia et al. (US 20210365738 A1) teaches a method and apparatus for training a model, predicting a mineral, and relates to the fields of computer vision and deep learning technologies, where the method may include: acquiring a target hyperspectral image of a target area, the target hyperspectral image including at least one pixel point annotated with a mineral category; determining a mask image corresponding to the target hyperspectral image; determining a sample hyperspectral image according to the target hyperspectral image and the mask image; determining an annotation vector of each pixel point according to the at least one pixel point annotated with the mineral category; and training a model according to the sample hyperspectral image and the annotation vector of the each pixel point.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BYUNG RO LEE whose telephone number is (571)272-3707. The examiner can normally be reached on Monday-Friday 8:30am-4:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lee Rodak can be reached on (571) 270-5628. The fax phone number for the organization where this application or proceeding is assigned is 571-273-2555.
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/BYUNG RO LEE/Examiner, Art Unit 2858
/LEE E RODAK/Supervisory Patent Examiner, Art Unit 2858
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