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 .
Response to Arguments
Applicant’s arguments, see page 7, filed 04/28/2025, with respect to claim rejections under 35 U.S.C. 112 have been fully considered and are persuasive. The rejections of claims 4-7 under 35 U.S.C. 112 have been withdrawn.
Applicant's arguments filed 04/28/2025, with respect to claim rejections under 35 U.S.C. 101 have been fully considered but they are not persuasive.
On page 10, Applicant remarks that the claimed invention reflects an improvement in the functioning of a computer. This is not persuasive because using a processor to analyze data is not considered an improvement to the functioning of the computer. The claim recites receiving and analyzing data.
Applicant then states that the disclosure of use of actual soil test data integrates the mathematical process into a practical application. This is not persuasive because obtaining the soil test values is considered data gathering. The “actual soil test” is an explanation of where some of the gathered data comes from, but is not considered part of the claimed invention, and therefore does not carry patentable weight.
Applicant's arguments filed 04/28/2025, with respect to claim rejections under 35 U.S.C. 102 have been fully considered but they are not persuasive.
Beginning on page 8, Applicant remarks that the disclosure of Lee is much more complex than the use of a statistical relationship of the claimed invention, and that Lee only applies to extrapolating values over a single season. This is not persuasive because, even if Lee is more complicated, Lee still uses data that includes statistical data (Lee [0047] Examples of external data include weather data, imagery data, soil data, or statistical data relating to crop yields, among others. External data 110 may consist of the same type of information as field data 106.). Additionally, Lee uses models and measurements of soil parameters of multiple fields to estimate properties at a single field of interest (Lee Fig. 7), including past and present data during the data analysis (i.e. Lee [0128] SSURGO, which is a database containing a century’s worth of soil information), which is more than just using data that only encompasses “past operations”.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 19 and 2-18 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 19 recites:
A system comprising one or more processors and one or more memories storing computer program instructions for predicting soil nutrient levels for a current growing season in a common agricultural field having a plurality of regions including at least one first region having a current soil test value that is known from an actual soil test and at least one second region having a current soil test value that is unknown, the system, when executing the computer program instructions by the one or more processors, being configured to:
receive a request for a nutrient level in said at least one second region;
provide a soil test model which defines a statistical relationship between:
(i) nutrient levels for a given region of a training agricultural field in a given growing season, and
(ii) field specific characteristics for the given region in a previous growing season prior to the given growing season and nutrient levels in one or more of the given region or a proximate region of the training agricultural field from the given growing season;
acquire known field specific characteristics from a prior growing season prior to the current growing season for said at least one second region;
acquire the current soil test value for said at least one first region;
apply said known field specific characteristics and said current soil test value from said at least one first region to the soil test model to calculate a predicted nutrient level for said at least one second region; and
transmit the predicted nutrient level to a user.
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 (machine).
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, step of “providing a soil test model which defines a statistical relationship between: (i) nutrient levels for a given region of a training agricultural field in a given growing season, and (ii) field specific characteristics for the given region in a previous growing season prior to the given growing season and nutrient levels in one or more of the given region or a proximate region of the training agricultural field from the given growing season (mathematical formula); and
applying said known field specific characteristics and said current soil test value from said at least one first region to the soil test model to calculate a predicted nutrient level for said at least one second region (inputting values into a formula)” is treated by the Examiner as belonging to mathematical concept grouping, while the steps of “applying said known field specific characteristics and said current soil test value from said at least one first region to the soil test model to calculate a predicted nutrient level for said at least one second region (inputting values into a formula); and
transmitting the predicted nutrient level to a user (sharing the result)” are treated as belonging to mental process grouping.
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 19: A system comprising one or more processors and one or more memories storing computer program instructions for predicting soil nutrient levels for a current growing season in a common agricultural field having a plurality of regions including at least one first region having a current soil test value that is known from an actual soil test and at least one second region having a current soil test value that is unknown; receive a request for a nutrient level in said at least one second region; acquire known field specific characteristics from a prior growing season prior to the current growing season for said at least one second region; acquire the current soil test value for said at least one first region.
The additional element in the preamble of “A system of predicting soil nutrient levels for a current growing season in a common agricultural field having a plurality of regions including at least one first region having a current soil test value that is known from an actual soil test and at least one second region having a current soil test value that is unknown” 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. Receiving a request for a nutrient level in said at least one second region; acquiring known field specific characteristics from a prior growing season prior to the current growing season for said at least one second region; and acquiring the current soil test value for said at least one first region represent mere data gathering steps and only add insignificant extra-solution activity to the judicial exception. One or more memories (generic memory) and a one or more processors (generic processor) are generally recited and are not qualified as particular machines.
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 2-18 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 –
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 19, 2, 3, and 8-18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lee (US 20170316124 A1).
Regarding Claim 19, Lee teaches a system comprising one or more processors and one or more memories storing computer program instructions for predicting soil nutrient levels (Lee [0064] Nutrient modeling instructions 135 comprise computer readable instructions which, when executed by one or more processors, causes agricultural intelligence computer system 130 to perform computation of nutrient values in soil using soil data, crop data, and weather data.) for a current growing season in a common agricultural field having a plurality of regions including at least one first region having a current soil test value that is known from an actual soil test and at least one second region having a current soil test value that is unknown (Lee teaches creating a soil nutrient model that uses past and current data and nutrient values from various fields, then using current data of a specific field, determines the nutrient values of that field. See Lee Fig. 7 and note how data from one or more fields is used with measurements from a particular field to determine a new model for that field to calculate a parameter value.), the system, when executing the computer program instructions by the one or more processors, being configured to:
receive a request for a nutrient level in said at least one second region (Lee [0062] “Model,” in this context, refers to an electronic digitally stored set of executable instructions and data values, associated with one another, which are capable of receiving and responding to a programmatic or other digital call, invocation, or request for resolution based upon specified input values, to yield one or more stored output values that can serve as the basis of computer-implemented recommendations, output data displays, or machine control, among other things. A request or desire for an output is necessary to perform a method for that output);
provide a soil test model (Lee [0062] In an embodiment, model and field data is stored in model and field data repository 160. Model data comprises data models created for one or more fields. For example, a crop model may include a digitally constructed model of the development of a crop on the one or more fields.) which defines a statistical relationship between:
(i) nutrient levels for a given region of a training agricultural field in a given growing season(Lee [0064] Nutrient modeling instructions 135 comprise computer readable instructions which, when executed by one or more processors, causes agricultural intelligence computer system 130 to perform computation of nutrient values in soil using soil data, crop data, and weather data. Also see [0062] The model may include […] a model of the current status of the one or more fields), and
(ii) field specific characteristics for the given region in a previous growing season prior to the given growing season and nutrient levels in one or more of the given region or a proximate region of the training agricultural field from the given growing season (Lee [0064] Nutrient modeling instructions 135 comprise computer readable instructions which, when executed by one or more processors, causes agricultural intelligence computer system 130 to perform computation of nutrient values in soil using soil data, crop data, and weather data. Also see [0062] The model may include a model of past events on the one or more fields);
acquire known field specific characteristics from a prior growing season prior to the current growing season for said at least one second region (Lee [0125] The digital model of nutrient content may also comprise a plurality of values defining other characteristics of the soil at the particular field over the particular period of time such as moisture content and temperature of the soil.);
acquire the current soil test value for said at least one first region (Lee [0125] At step 702, a digital model of nutrient content in soil of one or more fields over a particular period of time is stored. A digital model of nutrient content in soil generally comprises a plurality of values defining amounts of nutrient in soil at a particular field over a particular period of time.);
apply said known field specific characteristics and said current soil test value from said at least one first region to the soil test model to calculate a predicted nutrient level for said at least one second region (Lee [0126] The digital model of nutrient content in soil may identify one or more properties of the soil, such as nutrient content, temperature, and moisture content, based on one or more data inputs, one or more parameters, and one or more structural relationships.); and
transmit the predicted nutrient level to a user (Lee [0054] Presentation layer 134 may be programmed or configured to generate a graphical user interface (GUI) to be displayed on field manager computing device 104, cab computer 115 or other computers that are coupled to the system 130 through the network 109. The GUI may comprise controls for inputting data to be sent to agricultural intelligence computer system 130, generating requests for models and/or recommendations, and/or displaying recommendations, notifications, models, and other field data.).
Regarding Claim 2, Lee further teaches to train the soil test model using a machine learning algorithm with soil test values and field specific characteristics associated with a plurality of different training agricultural fields at different stages throughout one or more growing seasons (Lee [0100] In an embodiment, the agricultural intelligence computer system 130 is programmed or configured to create an agronomic model. In this context, an agronomic model is a data structure in memory of the agricultural intelligence computer system 130 that comprises field data 106, such as identification data and harvest data for one or more fields. The agronomic model may also comprise calculated agronomic properties which describe either conditions which may affect the growth of one or more crops on a field. Also see Fig. 3 and [0102-104] describing learning model creation (i.e. regression and genetic algorithms)).
Regarding Claim 3, Lee further teaches to train the soil test model using data from a training agricultural field having a plurality of regions including at least one known region in which a soil test value is known from an actual soil test and at least one unknown region in which the soil test value is unknown, by assigning a virtual value as the soil test value for said at least one unknown region based upon the soil test value of said at least one known region (Lee [0064] Nutrient modeling instructions 135 comprise computer readable instructions which, when executed by one or more processors, causes agricultural intelligence computer system 130 to perform computation of nutrient values in soil using soil data, crop data, and weather data. Also see [0062] In an embodiment, model and field data is stored in model and field data repository 160. Model data comprises data models created for one or more fields. […] The model may include […] a model of the current status of the one or more fields. A model is trained on the data given to it, assigning a value as a variable is necessary to determine parameter relationships and train the model).
Regarding Claim 8, Lee further teaches for each known region having data from a plurality of soil tests associated therewith, the system further calculates an average value from said data and assigning the average value as the soil test value for that known region (Lee [0143] In an embodiment, perturbing the inputs and/or parameters comprises sampling from distributions associated with each of the inputs and/or parameters. For example, a Gaussian distribution for a particular parameter and/or input may be identified by a mean value for the parameter and/or input and a standard deviation for the parameter and/or input. The models can be given data in different forms as shown in Fig. 8).
Regarding Claim 9, Lee further teaches wherein said known field specific characteristics include the nutrient levels acquired from soil tests performed during the prior growing season (Lee [0064] Nutrient modeling instructions 135 comprise computer readable instructions which, when executed by one or more processors, causes agricultural intelligence computer system 130 to perform computation of nutrient values in soil using soil data, crop data, and weather data. Also see [0062] The model may include a model of past events on the one or more fields).
Regarding Claim 10, Lee further teaches wherein said known field specific characteristics include weather data relating to common agricultural field during either or both of the current growing season and the prior growing season (Lee [0064] Nutrient modeling instructions 135 comprise computer readable instructions which, when executed by one or more processors, causes agricultural intelligence computer system 130 to perform computation of nutrient values in soil using soil data, crop data, and weather data. Also see [0062] The model may include a model of past events on the one or more fields, a model of the current status of the one or more fields, and/or a model of predicted events on the one or more fields.).
Regarding Claim 11, Lee further teaches wherein said known field specific characteristics include soil characteristics other than nutrient levels, measured in-field during either or both of the current growing season and the prior growing season (Lee [0125] The digital model of nutrient content may also comprise a plurality of values defining other characteristics of the soil at the particular field over the particular period of time such as moisture content and temperature of the soil. Also see [0128] External data 110 may include any additional data about the field, the one or more crops, weather, precipitation, meteorology, and/or soil and crop phenology. Weather, precipitation, and meteorology may be received as current temperature/precipitation data and future forecast data for the one or more fields. Current temperature/precipitation data may include measurements of temperature. External data about the field may be directly measured).
Regarding Claim 12, Lee further teaches wherein said known field specific characteristics include remotely sensed data acquired during either or both of the current growing season and the prior growing season (Lee [0125] The digital model of nutrient content may also comprise a plurality of values defining other characteristics of the soil at the particular field over the particular period of time such as moisture content and temperature of the soil. Also see [0128] External data 110 may include any additional data about the field, the one or more crops, weather, precipitation, meteorology, and/or soil and crop phenology. Weather, precipitation, and meteorology may be received as current temperature/precipitation data and future forecast data for the one or more fields. Current temperature/precipitation data may include measurements of temperature. Additionally, and/or alternatively, the current temperature/precipitation data and forecast data may include data at specific observation posts that is interpolated to locations in between the observation posts. External data about the field may be directly measured including remotely at observation posts).
Regarding Claim 13, Lee further teaches wherein said known field specific characteristics include harvest layer information associated with either or both of the current growing season and the prior growing season (Lee [0128] External data 110 may also include soil data for the one or more fields. For example, the Soil Survey Geographic database (SSURGO) contains per layer soil data at the sub field level for areas in the United States, including percentage of sand, silt, and clay for each layer of soil.).
Regarding Claim 14, Lee further teaches wherein said known field specific characteristics include yield values associated with either or both of the current growing season and the prior growing season (Lee [0127] Additionally, field data 106 may include information relating to historical harvest data including […] actual production history, yield).
Regarding Claim 15, Lee further teaches wherein said known field specific characteristics include agronomist recommendations associated with either or both of the current growing season and the prior growing season (Lee [0127] Additionally, field data 106 may include information relating to historical harvest data including […] tillage practices, and manure application history.).
Regarding Claim 16, Lee further teaches wherein said known field specific characteristics include fertilizer applications associated with either or both of the current growing season and the prior growing season. (Lee [0127] Additionally, field data 106 may include information relating to historical harvest data including […] tillage practices, and manure application history.).
Regarding Claim 17, Lee further teaches wherein the step of providing the soil test model further comprises:
selecting one or more field specific characteristics among a plurality of field characteristics available for the training agricultural field using an embedded feature selection approach (Lee [0104] At block 310, the agricultural intelligence computer system 130 is configured or programmed to perform data subset selection using the preprocessed field data in order to identify datasets useful for initial agronomic model generation. The agricultural intelligence computer system 130 may implement data subset selection techniques including, but not limited to, a genetic algorithm method,); and
training the soil test model to define said statistical relationship using the selected field specific characteristics (Lee [0106] At block 320, the agricultural intelligence computer system 130 is configured or programmed to implement agronomic model creation based upon the cross validated agronomic datasets. In an embodiment, agronomic model creation may implement multivariate regression techniques to create preconfigured agronomic data models.).
Regarding Claim 18, Lee further teaches for each first region of the common agricultural field having data from a plurality of actual soil tests associated therewith (Lee [0125] At step 702, a digital model of nutrient content in soil of one or more fields over a particular period of time is stored. A digital model of nutrient content in soil generally comprises a plurality of values defining amounts of nutrient in soil at a particular field over a particular period of time.), the system further calculates an average value from said data and assigning the average value as the current soil test value for that first region (Lee [0143] In an embodiment, perturbing the inputs and/or parameters comprises sampling from distributions associated with each of the inputs and/or parameters. For example, a Gaussian distribution for a particular parameter and/or input may be identified by a mean value for the parameter and/or input and a standard deviation for the parameter and/or input. Mean values are used in the modeling process).
Conclusion
THIS ACTION IS MADE FINAL. 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 CHRISTIAN T BRYANT whose telephone number is (571)272-4194. The examiner can normally be reached Monday-Thursday and Alternate Fridays 7:00-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, LISA CAPUTO can be reached at (571) 272-2388. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHRISTIAN T BRYANT/Examiner, Art Unit 2863 08/05/2025
/LISA M CAPUTO/Supervisory Patent Examiner, Art Unit 2863
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