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 .
Acknowledgments
Applicant elected claims 1-10 without traverse with respect to Election/Restriction requirement filed 1/15/2026.
Claims 1-10 are pending.
Applicant filed information disclosure statement.
Allowable Subject Matter
Claims 5 is allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Motavalli (US20120083907A1) in further view of Willness (US20130231968A1) who teaches equations used with respect to crops with variable rate inputs. However, with respect to exemplary claim 5, the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claim 5.
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 1-10 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 than the judicial exception itself.
Regarding Step 1 of subject matter eligibility for whether the claims fall within a statutory category (See MPEP 2106.03), claims 1-10 are directed to a method.
Regarding step 2A-1, claims 1-10 recites a Judicial Exception. Exemplary independent claim 1 recites the limitations of
…accessing…field data associated with an agricultural field; determining… based on the field data, an index value of biomass health for multiple regions in the agricultural field; determining…a number of unique soil types in the agricultural field, based on the index value of the biomass health for each of the multiple regions; determining…a distinctness of pairs of the unique soil types in the agricultural field; determining… a statistical variation based on the distinctness of the pairs; calculating, …based on the statistical variation, a variable rate suitability score for the agricultural field relating to spatially varying a rate of application of one or more crop inputs to the agricultural field; and displaying…the agricultural field along with the variable rate suitability score for the agricultural field.
These limitations, as drafted, are a process that, under its broadest reasonable interpretation cover concepts of accessing, determining, calculating, and displaying data. The claim limitations fall under the abstract idea grouping of mental process (including an observation, evaluation, judgment, opinion) , because the limitations can be performed in the human mind, or by a human using a pen and paper. For example, but for the language of agricultural intelligence computing device, the claim language encompasses simply accessing field data, determining an index value from the field data, determining soil types, determining a distinctness between a pair of soil types, determining a statistical variation of the soil types based on the distinctness, calculate a variable rate suitability score, and displaying a field with the variable rate suitability score. These are mere data manipulation steps that do not require a computer. A user is able to access information, make determinations, and display data without the use of a computer.
It is clear the limitations recite this abstract idea grouping, but for the recitation of a generic computer component. The mere nominal recitations of a generic computer component does not take the limitations out of the mental process grouping.
Regarding step 2A-2- This judicial exception is not integrated into a practical application, and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims recite the additional element of agricultural intelligence computing device.
This component is recited at a high level of generality, and merely automates the steps. The additional limitation is no more than mere instructions to apply the exception using a generic computer component.
The combination of the additional element is no more than mere instructions to apply the exception using a generic computer component or software. Accordingly, even in combination, the additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Further, the claims do not provide for recite any improvements to the functioning of a computer, or to any other technology or technical field; applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; applying the judicial exception with, or by use of, a particular machine; effecting a transformation or reduction of a particular article to a different state or thing; or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
The dependent claims have the same deficiencies as their parent claims as being directed towards an abstract idea, as the dependent claims merely narrow the scope of their parent claims. For example, the dependent claims further describe the field data such as the field data having condition and definition data. In addition, the dependent claims further recite details on the variable rate suitability score such as providing an equation on how its calculated.
Regarding step 2B the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because claim 1 recites
Method, however method is not considered an additional element.
Claim 1 further recites agricultural intelligence computing device
When looking at the additional element individually, the additional element is purely functional and generic, the Applicant’s specification states general purpose computer configurations as seen in para 0117.
When looking at the additional element in combination, the Applicant’s specification merely states a general-purpose computer configurations as seen in para 0117. The computer components add nothing that is not already present when the steps are considered separately. See MPEP 2106.05
Looking at these limitations as an ordered combination and individually adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, recitations of generic computer structure to perform generic computer functions that are used to "apply" the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself.
Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-10 are rejected under 35 U.S.C. 101.
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.
Claim(s) 1, 9, and 10 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Motavalli (US20120083907A1).
Regarding claim 1, Motavalli teaches
A computer-implemented method (See abstract-Methods and products are provided for facilitating application of variable-product agrochemicals, such as fertilizer, to an application area, such as a field) This teaches a method. (See figure 1B) This shows a computer.
accessing, by an agricultural intelligence computing device, field data associated with an agricultural field; (See fig. 1B) This shows the agriculture computing device (i.e. 164) accesses field data such as data stored in item 162. (See para 0039-FIG. 1B illustrates another aspect of exemplary operating environment 100. FIG. 1B shows data store 162 that stores fertilizer-application related information. This information includes application-area information 120 and fertilizer-application parameters 130. This information may also include historical information 140 and fertilizer application information 150, in some embodiments. )
determining, by the agricultural intelligence computing device, based on the field data, an index value of biomass health for multiple regions in the agricultural field (See para 0039-Additionally, information stored in data store 162 can be searched, queried, analyzed using computing device 164 and user interface 168. For example in one embodiment, a grower could input a query, through user interface 168) This shows an agriculture device such as item 164 can determine an index value for bio mass. An index value for biomass corresponds to soil PH levels and/or soil organic matter. This is for a plurality of locations in the application area as seen here (See para 0038-Fertilizer application-area 110 includes characteristics 112, which comprise features, geography, terrain, composition, or nature of various locations in application-area 110. For example, characteristics 112 might include the elevation, slope, soil structure, wetness, soil pH-level, soil organic matter, texture, residue, permeability, apparent electrical conductivity (ECa), vegetation, presence and quantity of a substances in the soil, average daily exposure to sunlight, average rainfall, temperature, or any other characteristic that might be relevant to fertilizer application).
determining, by the agricultural intelligence computing device, a number of unique soil types in the agricultural field, based on the index value of the biomass health for each of the multiple regions Based on the ph level of the soil (i.e. index value of biomass health) the system determines soil types in the field such as acidic soil or alkaline soil. (See para 0116- By way of example, for the soil pH-level attribute, subzones associated with pH-levels less than 7 might be merged into a zone corresponding to “acidic,” since the attribute values associated with each subzone correspond to the same attribute classification, (i.e., “acidic”). ) (See para 0116-But because an attribute value of 6.9 is considered acidic and an attribute value of 7.1 is considered basic or alkaline, under this embodiment, the discrete land units associated with the two attribute values might fall into different zones)
determining, by the agricultural intelligence computing device, a distinctness of pairs of the unique soil types in the agricultural field; The fact the system can determine soil ph levels in various locations in the application site (item 210 in figure 1C) means the system can determine distinctness of the soil for a pair of land units. For example, when looking at a pair of land units, the system can determine one soil is acidic and one is alkaline.
determining, by the agricultural intelligence computing device, a statistical variation based on the distinctness of the pairs The fact the system determines a distinctness also means it determines a statistical variation such as ph level of 6.9 and 7.1 with respect to different soils in the different land units as seen in fig. 1C.
calculating, by the agricultural intelligence computing device, based on the statistical variation, a variable rate suitability score for the agricultural field relating to spatially varying a rate of application of one or more crop inputs to the agricultural field; The variable rate suitability score corresponds to the rate of fertilizer to put in the land units. The fertilizer corresponds to the crop input in the land units. This is based on attribute values such as the soil ph levels. (See para 0093-At a step 901, a schedule specifying fertilizer-application information for each application cell is determined. The schedule can include, in one embodiment, information specifying for each application cell, a product of fertilizer to apply, or a quantity or rate and fertilizer type or types, and a mixture ratio, if a mixture of fertilizer types is to be applied.) (See para 0129-Turning now to FIG. 9, a flow diagram is shown illustrating an exemplary method for determining a fertilizer product for each application cell, shown as 900. As previously described, a fertilizer product can include the type or types of fertilizer, fertilizer application rate or quantity, and fertilizer-mixture information such as a proportion of mixed fertilizer types. In one embodiment, fertilizer products may be determined based on attribute values associated with zones or portions of zones falling within each application cell and based on fertilizer-application parameters. In one embodiment, the attribute values (or index values) of location points or discrete land units enclosed by an application cell are used to determine fertilizer product.) (See para 0040-. For example, for the attribute of soil pH, application-area information 120 might include a set of attribute values representing the location and measured soil pH-levels of various location points in the application area.)
and displaying, by the agricultural intelligence computing device, the agricultural field along with the variable rate suitability score for the agricultural field. (See para 0046-Fertilizer-application information 150 is used for generating applicator-controller information 184, which is discussed below. In some embodiments, fertilizer-application information 150 also can be used to produce charts, tables, or geographically referenced maps showing fertilizer application products for an application area ) (See para 0048-User interface 168 is used for displaying information and parameters stored in data store 162 including fertilizer-application information 150, which in some embodiments, may be in the form of one or more tables, charts, or geographically referenced maps.) (See para 0045-Fertilizer-application information 150 is received from computing device 164 and stored in data store 162. Fertilizer-application information 150 includes the results of the process for determining a fertilizer to apply to an application cell based on application-area information 120 and fertilizer-application parameters 130. This includes information specifying a product of fertilizer to apply in an application cell. Fertilizer product includes the type or types of fertilizer, and can also include quantity of fertilizer, application rate) This shows that a map can displayed of the application cell and fertilizer products that includes application rate (i.e. variable rate suitability score).
Regarding claim 9, Motavalli teaches
wherein displaying the agricultural field includes displaying a map of multiple agricultural fields including said agricultural field, in which a graphical indication of the variable rate suitability score is visually associated with the agricultural field. (See para 0046-Fertilizer-application information 150 is used for generating applicator-controller information 184, which is discussed below. In some embodiments, fertilizer-application information 150 also can be used to produce charts, tables, or geographically referenced maps showing fertilizer application products for an application area ) (See para 0048-User interface 168 is used for displaying information and parameters stored in data store 162 including fertilizer-application information 150, which in some embodiments, may be in the form of one or more tables, charts, or geographically referenced maps.) (See para 0045-Fertilizer-application information 150 is received from computing device 164 and stored in data store 162. Fertilizer-application information 150 includes the results of the process for determining a fertilizer to apply to an application cell based on application-area information 120 and fertilizer-application parameters 130. This includes information specifying a product of fertilizer to apply in an application cell. Fertilizer product includes the type or types of fertilizer, and can also include quantity of fertilizer, application rate) This shows that a map can displayed of the application cell and fertilizer products that includes application rate (i.e. variable rate suitability score). The land units corresponds to the multiple agricultural fields as seen in fig. 2A.
Regarding claim 10, Motavalli teaches
further comprising modifying operation of an agricultural implement, based on the variable rate suitability score, to change the rate of application of the one or more crop inputs to the agricultural field by the agricultural implement. (See para 0046- Fertilizer-application information 150 is used for generating applicator-controller information 184, which is discussed below. In some embodiments, fertilizer-application information 150 also can be used to produce charts, tables, or geographically referenced maps showing fertilizer application products for an application area, thereby enabling a grower to see or modify a fertilizer application strategy for the application area.) This shows that the system can receive modifications from a user. The modification can be for the farm practice (i.e. fertilizer application strategy). The farm strategy includes the variable rate suitability score since it deals with application rate. Once the modification is received, this information is turned into instructions for the applicator that services the field/land. (See figure 1B) this shows the Applicator.
Claim Rejections - 35 USC § 103
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 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) 2 and 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Motavalli (US20120083907A1) in further view of Johnson (US20140089045A1).
Regarding Claim 2, Motavalli teaches the limitations of claim 1, however Motavalli doesn’t teach the land regions with respect to square feet, however Johnson teaches
wherein the multiple regions include at least about every square foot within a field boundary of the agricultural field. (See para 0110- The size of the grids are determined by the user in this embodiment, however it is anticipated that the grid sizes will typically vary from acres to square feet, depending on the visual data source and user preferences.) This shows the grid sizes are the size of square feet.
Motavalli and Johnson are analogous art because they are from the same problem-solving area of agricultural crops and both belong to G06Q50. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Motavalli’s invention by incorporating the method of Johnson because Motavalli would also be able to set the land unit values to every square foot. This would make the system of Motavalli more sophisticated since this give the user greater control on how to apply fertilizer to the application site and also provides the user with more in depth analysis of the application site.
Regarding Claim 3, Motavalli and Johnson teaches the limitations of claim 2, however Motavalli further teaches
wherein the field data includes field definition data and field condition data; and wherein the agricultural field includes multiple agricultural fields. (See para 0038- For example, characteristics 112 might include the elevation, slope, soil structure, wetness… vegetation, presence and quantity of a substances in the soil, average daily exposure to sunlight, average rainfall, temperature, or any other characteristic that might be relevant to fertilizer application.) This shows field definition data such as vegetation and elevation. This also teaches field condition data such as soil structure, wetness, and temperature. The agriculture fields show multiple fields (i.e. land units) as seen in fig. 2A.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Motavalli (US20120083907A1) in further view of McClure (US20140249893A1).
Regarding Claim 4, Motavalli teaches the limitations of claim 1, however Motavalli further teaches
wherein determining the distinctness of the pairs of the unique soil types in the agricultural field includes referencing empirical data associating each of the pairs of soil types The system determines the distinctness of pairs of soil based on empirical data which is measured such as measured PH-levels as taught above.
However Motavalli doesn’t teach compatibility score, however McClure teaches with a compatibility score indicating a degree to which the soil type of said pair is manageable without loss of yield (See para 0093- For instance, with respect to soil zone 8510 data processing module 2550 can be configured to parse the plurality of subspace datasets of field harvest data 7900 (FIG. 7) to determine which of the subspace datasets comprise a subspace location matching the boundaries of soil zone 8510. As an example, data processing module 2550 will access subspace location 7911 while parsing subspace dataset 7910, and identify thereby that subspace 6910 falls within the boundaries of soil zone 8510.) This shows a compatibility score of match or no match. Space 6910 matches 8510 area. This match indicates the pair of these two areas will not produce a loss in yield since they are both the same soil and figures 9 shows space 8510 shows 120 BPA yield and figure 10 shows space 6910 with 180 BPA which shows an increase in yield and not a decrease.
Motavalli and McClure are analogous art because they are from the same problem-solving area of agricultural crops and both belong to G06Q50. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Motavalli’s invention by incorporating the method of McClure because Motavalli would also be able to implement an interface like fig. 9 of McClure which shows soil types with yield information. This would allow the user of Motavalli to better gauge the land and provide additional details such as combined yield and benchmark data. This makes the system of Motavalli more sophisticated.
Claim(s) 6, 7, and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Motavalli (US20120083907A1).
Regarding claim 6, Motavalli teaches the limitations of claim 1, however Motavalli further teaches
further comprising generating management zones in the agricultural field, based on the soil types… in response to the variable rate suitability score (See para 0004- In one embodiment, a field (or area of land) is delineated and mapped into zones of ground, that are suitable for receiving a certain agrochemical, based on characteristics of the ground within the zones. ) (See para 0060- Application area 210 also includes four zones 225; each zone includes discrete land units with associated index-values of the same index classification) This shows mapping land into zones. This is in response to characteristics of the land. Characteristics include rate of fertilizer used in the land (i.e. variable rate suitability score) and soil ph type. (See para 0026- The boundaries of an application cell (or at least its width) are determined by the physical reach or other limitations of a fertilizer applicator (such as a multi-bin spreader), user preferences, and by other application parameters, in one embodiment. The fertilizer-application parameters are also used, along with the application-area attribute values to determine the fertilizer-application information, which will be used to apply fertilizer to each cell. Fertilizer-application information includes an identification of type or types of fertilizer to apply to a cell, a rate and/or quantity of each fertilizer type to apply,)
and wherein displaying the agricultural field includes displaying boundaries for the management zones to a user (See fig. 2b) This shows boundaries to the zones. This is displayed to the user on an interface.
However Motavalli doesn’t teach the variable rate suitability score exceeding a threshold, however another section of Motavalli teaches exceeding a threshold (See para 0028- An attribute threshold or characteristic-threshold might also be in the form of multiple thresholds, forming a bracket or range of characteristic values, such as considering only attribute values falling below a first value and above a second value.) This shows threshold and exceeding a threshold such as a value.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Motavalli’s variable rate suitability score by comparing it to a threshold. This would allow the system of Motavalli to be more sophisticated since it would be able to see when the variable rate score exceeds a threshold for an area which may be bad for an area and require a further determination of zones. The areas of exceeding thresholds would also be information displayed to a user on an interface.
Regarding claim 7, Motavalli teaches the limitations of claim 6, however Motavalli further teaches
wherein generating the management zones is further based on a set of secondary spatial characteristics; and wherein the set of secondary spatial characteristics includes elevation, electrical conductivity, organic matter content, presence of tilling, presence of irrigation, reflectivity or characteristics derived from reflectivity, and/or thermal emissivity. (See para 0038- For example, characteristics 112 might include the elevation, slope, soil structure, wetness, soil pH-level, soil organic matter, texture, residue, permeability, apparent electrical conductivity (ECa), vegetation, presence and quantity of a substances in the soil, average daily exposure to sunlight, average rainfall, temperature, or any other characteristic that might be relevant to fertilizer application.) This teaches elevation and electrical conductivity and irrigation.
Regarding claim 8, Motavalli teaches the limitations of claim 6, however Motavalli further teaches
receiving, by the agricultural intelligence computing device, from the user, a modification of a farm practice criterion associated with one of the management zones; and applying the modified farm practice criterion to each location in said one of the management zones. (See para 0046- Fertilizer-application information 150 is used for generating applicator-controller information 184, which is discussed below. In some embodiments, fertilizer-application information 150 also can be used to produce charts, tables, or geographically referenced maps showing fertilizer application products for an application area, thereby enabling a grower to see or modify a fertilizer application strategy for the application area.) This shows that the system can receive modifications from a user. The modification can be for the farm practice (i.e. fertilizer application strategy). Once the modification is received, this information is turned into instructions for the applicator that services the field/land.
Conclusion
The prior art made of record and not relied upon considered pertinent to Applicant’s disclosure.
Willness (US20130231968A1) Discloses equations used with respect to crops with variable rate inputs.
Lindores (20120109614) Discloses a system for estimating a crop characteristic comprises a database, a plant growth model correlator, and a crop characteristic estimator.
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/MUSTAFA IQBAL/Primary Examiner, Art Unit 3625
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