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 .
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 1-3, and 5-24 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 providing a fertilizer recommendation for a crop with a handheld system comprising a crop nutrient detection device, wherein the method comprises steps of:
- i) determining a crop nutrient content by carrying out at least a measurement Mi by means of the crop nutrient detection device in at least one location (xj,yj) within an agricultural field;
- ii) determining a crop nutrient detection device position at the at least one measurement location;
- iii) receiving data, wherein receiving the data comprises receiving field data of the agricultural field and receiving remote spectral data from at least a plurality of wavelengths of the agricultural field;
- iv) processing the data to obtain at least one coefficient indicative of a crop status within the agricultural field;
- v) generating the fertilizer recommendation for the crop within the agricultural field based on the determined crop nutrient content, the determined position of the crop nutrient detection device and the at least one coefficient indicative of the crop status within the agricultural field,
wherein the fertilizer recommendation for a given location NREC (Xi,yi) based on a plurality of measurements M at locations (Xi, yi) is defined by the following equation:
N
R
E
C
x
i
,
y
i
=
f
M
x
j
,
y
j
+
C
*
g
[
R
x
i
,
y
i
-
R
0
wherein f represents an agronomic calibration function that translates an at least one measurement value of the crop nutrient detection device into a baseline value fertilizer recommendation;
g represents an agronomic calibration function that translates a value R of the at least one generated coefficient at (xi,yi) into a respective location dependent fertilizer recommendation, R represents the value of the at least one coefficient or a combination thereof considered at position (xi,yi), and C and Ro represent calibration constants.
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 grouping of subject matter when recited as such in a claim limitation, that covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, steps of “- iv) processing the data to obtain at least one coefficient indicative of a crop status within the agricultural field (calculation of coefficient(s)); and
- v) generating the fertilizer recommendation for the crop within the agricultural field based on the determined crop nutrient content, the determined position of the crop nutrient detection device and the at least one coefficient indicative of the crop status within the agricultural field,
wherein the fertilizer recommendation for a given location NREC (Xi,yi) based on a plurality of measurements M at locations (Xi, yi) is defined by the following equation:
N
R
E
C
x
i
,
y
i
=
f
M
x
j
,
y
j
+
C
*
g
[
R
x
i
,
y
i
-
R
0
wherein f represents an agronomic calibration function that translates an at least one measurement value of the crop nutrient detection device into a baseline value fertilizer recommendation;
g represents an agronomic calibration function that translates a value R of the at least one generated coefficient at (xi,yi) into a respective location dependent fertilizer recommendation, R represents the value of the at least one coefficient or a combination thereof considered at position (xi,yi), and C and Ro represent calibration constants (use of equation)” are treated by the Examiner as belonging to mathematical concept grouping, while the steps of “- i) determining a crop nutrient content by carrying out at least a measurement Mi by means of the crop nutrient detection device in at least one location (xj,yj) within the an agricultural field (determination based on data);
- ii) determining a crop nutrient detection device position at the at least one measurement location (determination based on data)” are treated as belonging to mental process grouping.
Similar limitations comprise the abstract ideas of Claim 6.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
The above claims comprise the following additional elements:
Claim 1: A method for providing a fertilizer recommendation for a crop with a handheld system comprising a crop nutrient detection device, wherein the method comprises the steps of: - ii) receiving data, wherein receiving the data comprises receiving field data of the agricultural field
Claim 6: A computer implemented method for determining at least one measurement region for carrying out at least one measurement with a crop nutrient detection device for providing a fertilizer recommendation to a crop, comprising the steps of: - ii) receiving data, wherein receiving the data comprises receiving field data of the agricultural field.
The additional element in the preamble of “A computer implemented method for determining at least one measurement region for carrying out at least one measurement with a crop nutrient detection device for providing a fertilizer recommendation to a crop” is not qualified for a meaningful limitation because it only generally links the use of the judicial exception to a particular technological environment or field of use. The step of receiving data, wherein receiving the data comprises receiving field data of the agricultural field represents a mere data gathering step and only adds an insignificant extra-solution activity to the judicial exception. A computer (generic processor) is generally recited and are not qualified as a particular machine.
In conclusion, the above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do 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).
The claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2, 3, 5, and 7-24 provide additional features/steps which are part of an expanded algorithm, so these limitations should be considered part of an expanded abstract idea of the independent claims.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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) 6-8, 10-15, 17 and 19-24 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ethington et al. (US 20210350478 A1), hereinafter “Ethington”.
Regarding Claim 6, Ethington teaches a computer implemented method for determining at least one measurement region for carrying out at least one measurement with a crop nutrient detection device for providing a fertilizer recommendation to a crop (Ethington [0270] FIG. 6 is an example method for recommending agricultural activities in the agricultural environment of FIG. 1. Method 500 is implemented by agricultural intelligence computer system 150 (shown in FIG. 1).), comprising steps of:
- i) determining an agricultural field comprising the crop for which the recommendation is intended (Ethington [0270] FIG. 6 is an example method for recommending agricultural activities in the agricultural environment of FIG. 1. The area must be determined to be analyzed);
- ii) receiving data, wherein receiving the data comprises receiving field data of the agricultural field (Ethington [0270] Agricultural intelligence computer system 150 receives 610 a plurality of field definition data. Agricultural intelligence computer system 150 retrieves 620 a plurality of input data from a plurality of data networks 130A, 130B, and 140.);
- iii) determining at least one measurement region within the agricultural field (Ethington [0270] Agricultural intelligence computer system 150 determines 630 a field region based on the field definition data. Agricultural intelligence computer system 150 identifies 640 a subset of the plurality of input data associated with the field region.) for carrying out the at least one measurement based on the field data (this limitation is intended use and therefore does not carry patentable weight).
Regarding Claim 7, Ethington further teaches wherein receiving data further comprises receiving remote spectral data from at least a plurality of wavelengths of the agricultural field (Ethington [0226] Field health advisor module 424 receives and processes all such data points (along with field image data) to determine and identify a crop health index for each location in each field identified by the user each time a new field image is available. In an example embodiment, field health advisor module 424 determines a crop health index as a normalized difference vegetation index (“NDVI”) based on at least one near-infrared (“NIR”) reflectance value and at least one visible spectrum reflectance value at each raster location in the field. In another example embodiment, the crop health index is a NDVI based on multispectral reflectance. Also see [0254] In some embodiments, a reflectivity (e.g., infrared, near-infrared, thermal, visual) measurement is used to adjust the starter recommendation.), and the method further comprises the steps of processing the remote data to obtain at least a coefficient indicative of the a crop status within the agricultural field and further determining the at least one measurement region within the agricultural field for carrying out the at least one measurement based on the at least one coefficient (Ethington [0237] The variable rate suitability score is preferably determined based on the statistical spatial variation of field-specific & environmental data 170 and field condition data 180 and other data associated with the field.).
Regarding Claim 8, Ethington further teaches wherein determining at least one measurement region further comprises determining at least one measurement location within the measurement region (Ethington [0121] In an example embodiment, the field health advisor module provides the user with the ability to select a location on a field to get more information about the health index, soil type or elevation at a particular location.).
Regarding Claim 10, Ethington further teaches wherein receiving field data further comprises receiving geographic identifiers regarding the geometry of the boundaries of the agricultural field, and wherein determining the at least one measurement region within the agricultural field further comprises determining the at least one measurement region based on the field data (Ethington [0233] wherein field definition data includes field identifiers, geographic identifiers, boundary identifiers, and crop identifiers;).
Regarding Claim 11, Ethington further teaches wherein the crop nutrient detection device comprises a location unit and the method further comprises receiving location data from the crop nutrient detection device and determining the at least one measurement region based on the location data (Ethington [0121] In an example embodiment, the field health advisor module provides the user with the ability to select a location on a field to get more information about the health index, soil type or elevation at a particular location.).
Regarding Claim 12, Ethington further teaches wherein the method further comprises receiving crop data and weather data, wherein determining the at least one measurement region within the agricultural field further comprises determining the at least one measurement region based on the crop and weather data (Ethington [0121] the field condition data 180 includes growth stage conditions, field weather conditions, soil moisture, and precipitation conditions).
Regarding Claim 13, Ethington further teaches wherein the method further comprises receiving weather forecast data and wherein determining the at least one measurement region within the agricultural field further comprises determining the at least one measurement region based on the weather forecast data (Ethington [0036] The term “environmental data” refers to environmental information related to farming activities such as weather information […] Environmental data may be obtained from external data sources accessible by the agricultural intelligence computer system. Environmental data may also be obtained from internal data sources integrated within the agricultural intelligence computer system. Data sources for environmental data may include weather radar sources, satellite-based precipitation sources, meteorological data sources (e.g., weather stations). Also see [0048] As part of the field condition data provided, the agricultural intelligence computer system tracks field weather conditions for each field identified by the user. The agricultural intelligence computer system determines current weather conditions including field temperature, wind, humidity, and dew point. The agricultural intelligence computer system also determines forecasted weather conditions including field temperature, wind, humidity, and dew point for hourly projected intervals, daily projected intervals, or any interval specified by the user. The forecasted weather conditions are also used to forecast field precipitation, field workability, and field growth stage. Near-term forecasts are determined using a meteorological model (e.g., the Microcast model) while long-term projections are determined using historical analog simulations.).
Regarding Claim 14, Ethington further teaches wherein the method further comprises determining a sub-area within the agricultural field, where the at least one measurement region should be contained (Ethington [0121] In an example embodiment, the field health advisor module provides the user with the ability to select a location on a field to get more information about the health index, soil type or elevation at a particular location. Smaller areas can be focused on within the larger region).
Regarding Claim 15, Ethington further teaches wherein the method further comprises receiving farm data and determining the measurement region based on the farm data (Ethington [0035] The term “field-specific data” refers to (a) field data (e.g., field name, soil type, acreage, tilling status, irrigation status), (b) harvest data (e.g., crop type, crop variety, crop rotation, whether the crop is grown organically, harvest date, Actual Production History (APH), expected yield, yield, crop price, crop revenue, grain moisture, tillage practice, weather information (e.g., temperature, rainfall) to the extent maintained or accessible by the user, previous growing season information), (c) soil composition (e.g., pH, organic matter (OM), cation exchange capacity (CEC)), (d) planting data (e.g., planting date, seed(s) type, relative maturity (RM) of planted seed(s), seed population), (e) nitrogen data (e.g., application date, amount, source), (f) pesticide data (e.g., pesticide, herbicide, fungicide, other substance or mixture of substances intended for use as a plant regulator, defoliant, or desiccant), (g) irrigation data (e.g., application date, amount, source), and (h) scouting observations (photos, videos, free form notes, voice recordings, voice transcriptions, weather conditions (temperature, precipitation (current and over time), soil moisture, crop growth stage, wind velocity, relative humidity, dew point, black layer)). ).
Regarding Claim 17, Ethington further teaches wherein the method further comprises determining a crop nutrient content in at least one of the measurement regions and generating a fertilizer recommendation (Ethington [0035] The term “field-specific data” refers to […] (c) soil composition (e.g., pH, organic matter (OM), cation exchange capacity (CEC))).
Regarding Claim 19, Ethington further teaches system for determining at least one measurement region for carrying out at least one measurement with a crop nutrient detection device for providing a fertilizer recommendation to a crop, wherein the system comprises a processor configured to carry out the method according to claim 6 (Ethington [0007] The networked agricultural intelligence system includes a user device, a plurality of data networks computer systems, an agricultural intelligence computer system comprising a processor and a memory in communication with the processor. The processor is configured to receive a plurality of field definition data from the user device, retrieve a plurality of input data from a plurality of data networks, determine a field region based on the field definition data, identify a subset of the plurality of input data associated with the field region, determine a plurality of field condition data based on the subset of the plurality of input data, identify a plurality of field activity options, determine a recommendation score for each of the plurality of field activity options based at least in part on the plurality of field condition data, and provide a recommended field activity option from the plurality of field activity options based on the plurality of recommendation scores.).
Regarding Claim 20, Ethington further teaches comprising a display and an input unit, wherein the system further comprises a Graphical User Interface configured to display the at least one measurement region (Ethington [0131] In the example embodiment, grower 110 utilizes user devices 112, 114, 116, and/or 118 to interact with agricultural intelligence computer system 150. In one example, user device 112 is a smart watch, computer-enabled glasses, smart phone, PDA, or “phablet” computing device capable of transmitting and receiving information such as described herein. Also see [0145] User system 202 also includes at least one media output component 215 for presenting information to user 201. Media output component 215 is any component capable of conveying information to user 201. And [0146] In some embodiments, user system 202 includes an input device 220 for receiving input from user 201.).
Regarding Claim 21, Ethington further teaches wherein the graphical user interface is further configured to display a plurality of measurement regions and the system is further configured to receive an input for selecting at least one from the at least one of the plurality of measurement regions displayed (Ethington [0182] The agricultural intelligence computer system might automatically display and transmit the date and time and field definition data associated with the field-specific data, such as geographic coordinates and boundaries.).
Regarding Claim 22, Ethington further teaches wherein the system is further configured to display weather forecast data, crop and/or field data associated with the at least one measurement region (Ethington [0164] Current weather conditions and forecasted weather conditions (hourly, daily, or as specified by the user) are displayed on the user device graphically along with applicable information regarding the specific field, such as field name, crop, acreage, field precipitation, field workability, field growth stage, soil moisture, and any other field definition data or field-specific & environmental data 170 that the user may specify. Such information may be displayed on the user device in one or more combinations and level of detail as specified by the user.).
Regarding Claim 23, Ethington further teaches wherein the system is further configured to receive farm data and display at least one scheduled task, wherein the system is further configured to display the at least one measurement region, and different itineraries which include the at least one measurement region and at least one of the scheduled tasks displayed (Ethington [0183] Agricultural intelligence computer system 150 is additionally configured to display scouting and logging events related to the receipt of field-specific data from the user via one or more agricultural machines or agricultural machine devices that interacts with agricultural intelligence computer system 150 or via the user device. Such information can be displayed as specified by the user. In one example, the information is displayed on a calendar on the user device, wherein the user can obtain further details regarding the information as necessary. In another example, the information is displayed in a table on the user device, wherein the user can select the specific categories of information that the user would like displayed.).
Regarding Claim 24, Ethington further teaches wherein the system is further configured to receive an input for determining at least one predetermined area of the agricultural field and the system is further configured to determine the at least one measuring region within the at least one predetermined area (Ethington [0164] Current weather conditions and forecasted weather conditions (hourly, daily, or as specified by the user) are displayed on the user device graphically along with applicable information regarding the specific field, such as field name, crop, acreage, field precipitation, field workability, field growth stage, soil moisture, and any other field definition data or field-specific & environmental data 170 that the user may specify. Such information may be displayed on the user device in one or more combinations and level of detail as specified by the user. Also see [0159] the user may use user device 114 to access a plurality of windows or displays showing field condition data 180 and/or recommended agricultural activities 190. The user can specify the data to be displayed).
The Examiner notes that there are no prior art rejections for claims 1-3, 5, 9, 16, and 18.
Regarding Claim 1, Ethington, as best understood by the Examiner, when considered alone and in combination with the remaining prior art of record, does not seem to fairly teach or suggest wherein the fertilizer recommendation for a given location NREC (Xi,yi) based on a plurality of measurements M at locations (Xi, yi) is defined by the following equation:
N
R
E
C
x
i
,
y
i
=
f
M
x
j
,
y
j
+
C
*
g
[
R
x
i
,
y
i
-
R
0
wherein f represents an agronomic calibration function that translates an at least one measurement value of the crop nutrient detection device into a baseline value fertilizer recommendation;
g represents an agronomic calibration function that translates a value R of the at least one generated coefficient at (xi,yi) into a respective location dependent fertilizer recommendation, R represents the value of the at least one coefficient or a combination thereof considered at position (xi,yi), and C and Ro represent calibration constants.
Claims 2, 3, 5, 16, and 18 are not rejected under prior art due to their dependence on claim 1.
Regarding Claim 9, Ethington does not seem to fairly teach or suggest wherein determining at least one measurement region based on the at one coefficient further comprises determining a region wherein a value R of the at least one coefficient or a combination thereof is comprised within a range of at least one of the following: 0.7 RAvg < R < 0.85 RAvg, or 0.85 RAvg< R < 1.15 RAvg, or 1.15 RAvg< R < 1.3 RAvg, wherein RAvg, is defined as an average value within the agricultural field of the at least one coefficient. Ethington discloses a different “coefficient”, which would not be rendered obvious in view of the remaining prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
He et al. (CN 108575240 A) discloses One Based On Dry Potato For Optimizing The Recommend Fertilizing Method Of Model.
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/CHRISTIAN T BRYANT/Examiner, Art Unit 2863