Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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 1-4 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.
Specifically, representative Claim 1 recites:
“A method for soil nitrogen content soft measurement based on a control system for on-demand fertilization of corn, the method comprising following steps: S1: setting up the control system for on-demand fertilization of corn, wherein the control system for on-demand fertilization of corn comprises a control unit, an electromagnetic valve assembly, a fertilization flow rate sensor assembly, a soil pH sensor, a soil moisture sensor, and a wireless data transmission unit, the electromagnetic valve assembly comprises 12-24 electromagnetic valves, and the fertilization flow rate sensor assembly comprises 12-24 fertilization flow rate sensors; wherein the control unit is fixed to an upper end of a fertilization machine frame; each electromagnetic valve of an electromagnetic valve assembly is fixed between a nozzle and an output hole of a fertilization pipe; each fertilization flow rate sensor of the fertilization flow rate sensor assembly is fixed on the fertilization pipe and is located to the right of each output hole of the fertilization pipe; the nozzle is positioned directly above a corn seedlings; the soil pH sensor and the soil moisture sensor are connected to the wireless data transmission unit and are placed in the soil; the 12-24 electromagnetic valves of the electromagnetic valve assembly and the wireless data transmission unit are controlled by the control unit; sampling the soil of corn with different fertilization amounts to obtain the fertilization amount data, measuring soil pH value, soil moisture, total nitrogen content, available nitrogen, and hydrolyzable nitrogen, wherein the fertilization amount, soil pH value, and soil moisture are inputs to the model, and the total nitrogen content, available nitrogen, and hydrolyzable nitrogen are outputs for structuring the training dataset for the soil nitrogen content soft measurement model; S2: normalizing the data obtained in S1 using the following processing method:
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wherein the y represents the normalized parameter data; the y.sub.max represents the maximum value of the expected normalization range; the y.sub.min represents the minimum value of the expected normalization range; the x.sub.max represents the maximum value in each row of parameter data; the x.sub.min represents the minimum value in each row of parameter data; and the V represents the actual parameter value; S3: establishing a soil nitrogen content soft measurement model based on the Optimize Adaptive Variation-Improved Inertial Weight-Whale Grey Wolf Optimizer-Sine Cosine Quantum Particle Swarm Optimization (OAV-IIW-WGWO-SCQPSO) algorithm to optimize the BP neural network and perform soil nitrogen content soft measurement; S3.1: establishing a 3-layer Back Propagation (BP) neural network topology structure with 3 layers consisting of input layer, hidden layer, and output layer, wherein the number of nodes in the input layer is 3, the number of nodes in the hidden layer is H, and the number of nodes in the output layer is 3, inputting the fertilization amount, soil pH value, and soil moisture data from the training dataset samples into the input layer, and corresponding expected outputs and actual outputs will be generated; initializing the number of nodes, weights, and thresholds for each layer of the BP neural network; S3.2: optimizing the BP neural network using the Optimize Adaptive Variation-Improved Inertial Weight-Whale Grey Wolf Optimizer-Sine Cosine Quantum Particle Swarm Optimization algorithm, wherein the optimization process comprises the following steps: S3.2.1: determining the particle dimension P.sub.d in the Optimize Adaptive Variation-Improved Inertial Weight-Whale Grey Wolf Optimizer-Sine Cosine Quantum Particle Swarm Optimization algorithm, wherein the calculation method is as follows: P.sub.d=in+in.Math.H+H+H.Math.out+out; wherein the in is the number of neurons in the input layer of the BP neural network; and the out is the number of neurons in the output layer of the BP neural network; S3.2.2: determining the particle fitness function and calculating the fitness of each particle, wherein the calculation method for the particle fitness function is:
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wherein the Y.sub.j represents the expected output of the j-th particle; and the y.sub.j represents the actual output of the j-th particle; S3.2.3: dividing the individuals α, β, and δ of the Optimize Adaptive Variation-Improved Inertial Weight-Whale Grey Wolf Optimizer-Sine Cosine Quantum Particle Swarm Optimization algorithm based on the size of their fitness; S3.2.4: according to the Whale Grey Wolf Optimizer (WGWO) algorithm, updating the particle positions under the guidance of the α, β, and δ individuals, wherein the method of updating particle positions is as follows: approaching the optimal solution: each particle approaches the optimal solution in the following manner:
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wherein the D is the Euclidean distance between the particle and the optimal solution; the x.sub.(p(t)) is the position of the optimal solution; the x.sub.t is the particle position before starting the approach to the optimal solution process; the x.sub.1+1 is the particle position after finishing the approach to the optimal solution process; the A and C are variable coefficients; the p is the contraction factor, linearly decreasing from 2 to 0; the r.sub.1 and r.sub.2 are two distinct random numbers in the range [0,1]; searching for the optimal solution, wherein each particle searches for the optimal solution in the following manner:
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wherein the q represents α, β, or δ; the D.sub.q is the Euclidean distance between the particle and each of the α, β, or δ particles; the x.sub.l is the distance each particle moves towards the q particle; the x.sub.1 is the distance each particle moves towards the α particle; the x.sub.2 is the distance each particle moves towards the β particle; the x.sub.3 is the distance each particle moves towards the & particle; the b is the logarithmic spiral shape constant; the R is a random number in the range [−1,1]; the r.sub.3 and r.sub.4 are random numbers in the range [0,1]; the rand[0,t] is a random number generated within the interval [0,t]; the rand[0,T] is a random number generated within the interval [0,T]; the x.sub.t+1.sup.1 represents the particle position after searching for the optimal solution; the determination method for variable coefficients A.sub.l and C.sub.l is the same as that for A and C; S3.2.5: calculating the particle fitness again; S3.2.6: updating the particle velocity and perform secondary position updates based on the fitness of the particles: optimize the inertia weight in the Quantum Particle Swarm Optimization and introduce the optimized inertia weight value into the Whale Grey Wolf Optimizer (WGWO) algorithm, wherein the updated formula for optimizing the inertia weight is as follows:
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wherein the ω.sub.− represents the decreasing inertia weight; the ω.sub.+ represents the increasing inertia weight; the ω.sub.max represents the maximum inertia weight; the ω.sub.min represents the minimum inertia weight; the T represents the number of iterations; the t represents the t-th iteration; incorporating the optimized inertia weight into the grey wolf hunting formula of the grey wolf algorithm, wherein the particle velocity update formula of the particle swarm algorithm, and the particle position update formula of the particle swarm algorithm to iteratively update the particles; S3.2.7: performing the third update on the particle position: when a random number generated within the interval [0,t] satisfies the mutation condition, i.e., when rand[0,t]>rand[0,T], based on the Optimize Adaptive Variation, perform the third update of particle position, the update formula is as follows:
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wherein the r.sub.7 represents a random number generated within the interval [0,1]; the x.sub.t+1.sup.3 represents the updated position of the particle; S3.2.8: assigning the results of the third particle update to the weights and thresholds of the BP neural network; if the fitness value generated by the current iteration of the particle swarm update is less than that generated by the previous iteration, update the individual best and global best values; otherwise, proceed to the termination condition evaluation; if the number of iterations of the particle swarm update meets the termination condition, stop the update, and the BP neural network obtains the optimal weights and thresholds; otherwise, return to S3.2 to continue updating the weights and thresholds of the BP neural network; S4: inputting the real-time fertilization amount, soil pH value, and soil moisture data collected into the soil nitrogen content soft measurement model established in S3, and output the total nitrogen content, available nitrogen, and hydrolyzed nitrogen content of the soil.”
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the groupings of subject matter that covers mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations.
Next, under the Step 2A, Prong Two, we consider whether the above claims that recites a judicial exception are integrated into a practical application.
The above claims comprise the following additional elements:
In Claim 1: A method for soil nitrogen content soft measurement based on a control system for on-demand fertilization of corn, the method comprising following steps: S1: setting up the control system for on-demand fertilization of corn, wherein the control system for on-demand fertilization of corn comprises a control unit, an electromagnetic valve assembly, a fertilization flow rate sensor assembly, a soil pH sensor, a soil moisture sensor, and a wireless data transmission unit, the electromagnetic valve assembly comprises 12-24 electromagnetic valves, and the fertilization flow rate sensor assembly comprises 12-24 fertilization flow rate sensors; wherein the control unit is fixed to an upper end of a fertilization machine frame; each electromagnetic valve of an electromagnetic valve assembly is fixed between a nozzle and an output hole of a fertilization pipe; each fertilization flow rate sensor of the fertilization flow rate sensor assembly is fixed on the fertilization pipe and is located to the right of each output hole of the fertilization pipe; the nozzle is positioned directly above a corn seedlings; the soil pH sensor and the soil moisture sensor are connected to the wireless data transmission unit and are placed in the soil; the 12-24 electromagnetic valves of the electromagnetic valve assembly and the wireless data transmission unit are controlled by the control unit; sampling the soil of corn with different fertilization amounts to obtain the fertilization amount data, measuring soil pH value, soil moisture, total nitrogen content, available nitrogen, and hydrolyzable nitrogen; output the total nitrogen content, available nitrogen, and hydrolyzed nitrogen content of the soil.
The additional elements in the preambles are recited in generality and represent insignificant extra-solution activity (field-of-use limitations) that is not meaningful to indicate a practical application.
The additional elements in the claims such as a control system, electromagnetic valve assembly, a fertilization flow rate sensor assembly, a soil pH sensor, a soil moisture sensor, a wireless data transmission unit, structural arrangements of control unit, electromagnetic valve, fertilization flow rate sensor, the fertilization pipe, the nozzle, the soil pH sensor and the soil moisture sensor are examples of generic control/functional equipment (components) represent mechanical layout of equipment that is generally recited and not meaningful and are not qualified as particular machines to indicate a practical application. These limitations (additional elements) have only tangential relationship to the judicial exception (insignificant extra-solution activity) (MPEP 2106.05(g)). Additionally, these additional elements are used as “a tool to perform an existing process” representing insignificant-extra solution activity according to MPEP 2106.05(f) (2).
The limitations that generically recite sampling the soil of corn with different fertilization amounts to obtain the fertilization amount data, measuring soil pH value, soil moisture, total nitrogen content, available nitrogen, and hydrolyzable nitrogen; output the total nitrogen content, available nitrogen, and hydrolyzed nitrogen content of the soil represent insignificant extra-solution activity of mere data gathering. According to the October update on 2019 SME Guidance such steps are “performed in order to gather data for the mental analysis step, and is a necessary precursor for all uses of the recited exception. It is thus extra-solution activity, and does not integrate the judicial exception into a practical application”.
Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis) because these additional elements/steps are well-understood and conventional in the relevant art based on the prior art of record (CN 111357463, CN 209732208, Laird, LF 600, Fertigation, etc.).
The independent claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2-4 provide additional features/steps which are part of an expanded abstract idea of the independent claims (additionally comprising abstract idea steps) and, therefore, these claims are not eligible without meaningful additional elements that reflect a practical application and/or additional elements that qualify for significantly more for substantially similar reasons as discussed with regards to Claim 1.
Examiner Note with Regards to Prior Art of Record
Claims 1-4 are distinguished over prior art of record based on the reasons below.
The following references are considered to be the closest prior art to the claimed invention:
Wen-zhi Han et al. (CN 209732208) discloses a water, fertilizer and pesticide integrated automation control device; the watering, fertilizing and spraying combined on to a working platform using a duty controller automatically adjusting the PWM ratio electromagnetic valve to realize the precise control a solenoid valve and a sensor for controlling a fertilizer meteor. It comprises multiple solenoid valves are arranged in the fertilizer pipeline according to the conventional arrangement in the field of fertilizer application. On this basis, those skilled in the art can easily arrange 12 to 24 solenoid valves according to the needs. The electromagnetic valve group composed of the above components and the corresponding fertilizer sensor group are arranged, which are the conventional technical means in this field.
Di Zhang et al. (CN 109673480) discloses a detection subsystem, a control subsystem, a fertilizer mixing subsystem and a sprinkling irrigation subsystem, wherein the detection subsystem comprises a crop body sensor, a soil moisture content sensor and a soil fertility sensor; the control subsystem comprises a first receiving module, a calculating module and a comparison early warning module, and the fertilizer mixing subsystem comprises a fertilizer mixing device.
Li-juan Niu et al. (CN 111357463) discloses fertilizer delivery method in an agricultural irrigation and fertilization system detecting the soil moisture in a collection area through a soil moisture sensor, detecting soil nutrients in the collection area through a soil nutrient sensor, detecting the soil moisture and nutrient data in several collection areas in the farmland and transmitting it to a controller; step 2: based on the soil moisture and nutrient data, calculating, by the controller, the amount of fertilizer needed for irrigation in each collection area, and then determining the appropriate fertilizer liquid concentration to adapt to the moisture and nutrient conditions in the collection area, then arranging the required fertilizer liquid concentration in each collection area in order from low to high.
David Laird et al. (US 20190285608) discloses a fertilizer applicator operation or coupled with georeference data collected simultaneously to generate a map of soil nitrate levels for the field, which can be used as a prescription for nitrogen fertilizer application including a control system of a mechanical mechanism attached to a vehicle; it includes a sensor can be attached to (integrated with) a fertilizer applicator, allowing real time modulation of nitrogen fertilizer application rates based on measured soil nitrate levels.
Liquid Fertilizer LF 600 M1, APV - Technische Produkte GmbH
Dallein 62, AT - 3753 Hötzelsdorhttps, 2019, https://en.apv.at/products/crop-protection-fertilisation/liquid-fertilizer-new, discloses architecture of a front-mounted liquid fertilizer machine with a 600-liter tank capacity. It is built for precise dosage and simultaneous application of liquid agents during soil cultivation, harrowing, or sowing. Uses specific nozzle options (spot or flat jet) and flow sensors to regulate exact distribution rates.
Fertigation Series 20: Fertilizer and Chemical Injection Devices, Youtube, 2021, https://www.youtube.com/watch?v=XpxkkoYo3eA discloses mechanical components of a fertilization device.
Bo Wang et al., “Research on accurate perception and control system of fertilization amount for corn fertilization planter”, Front. Plant Sci., 24 November 2022, Sec. Sustainable and Intelligent Phytoprotection, Volume 13, 13 pages, discloses a control system for corn fertilization planter that includes a fertilizer flow sensor was installed on the fertilizer discharge pipe to detect the real-time fertilizer discharge amount, and the speed of the fertilizer discharge motor was adjusted based on the real-time flow feedback, so as to realize the precise control of the fertilizer discharge amount.
However, in regards to Claim 1, the claims differ from the above closest prior art, either singularly or in combination, because the art fails to anticipate or render obvious while disclosing individual features of the invention, would not create an obvious combination to a person having ordinary skill in the art the mathematical relationships claimed, in combination with all other limitations in the claim as claimed and defined by applicant.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDER SATANOVSKY whose telephone number is (571)270-5819. The examiner can normally be reached on M-F: 9 am-5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Rastovski can be reached on (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALEXANDER SATANOVSKY/
Primary Examiner, Art Unit 2857
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