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 .
DETAILED ACTION
Status of Claims
This action is a Final action on the merits in response to the application filed on 09/26/2023.
Claims 1 and 11 have been amended. Claims 8, 14 and 15 have been cancelled. Claims 16-18 have been added. Claims 1-7, 9-13 and 16-18 are currently pending and have been examined in this application.
Response to Amendment
Applicant’s amendment has been considered.
Applicant’s amendment is sufficient to overcome the 35 U.S.C. 112(f) and 35 U.S.C. 112(b) rejections in the previous office action.
Response to Arguments
Applicant’s remarks have been considered.
Applicant argues, “None of these recitations describes the observation, evaluation, or judgment of data by a human mind.” (pg.8)
Examiner respectfully disagrees. The claims encompass Mental Processes related to observation and evaluation of data. For instance, the steps of providing crop phenology training data, training a machine learning system, providing a selection of crops and locations, generating a crop phenology prediction for selection of crops at a location and generating agronomic control data based on the prediction involve collecting and analyzing data to produce a result (a prediction and control data). These step could reasonably be performed in the human mind or with pen/paper.
Further, based on MPEP 2106.04(a)(2)(III)(C), “Claims can recite a mental process even if they are claimed as being performed on a computer.” Here, generic computer components (e.g. a processor and crm) are performing generic computer functions such as providing, training, generating and controlling, which encompass abstract concepts.
Applicant argues, “ Under Step 2A, Prong Two, even assuming arguendo that the training and prediction steps could be characterized as involving a mathematical concept, claim 1 integrates any such concept into a practical application.” (pgs. 6-7)
The judicial exceptions are not integrated into a practical application. The claims recite the additional elements of an input/output interface, a processing unit, a non-transitory computer readable medium, a processor and an agricultural device. These are generic computer components recited at a high level of generality as performing generic computer functions (see Spec ¶0025)
For instance, the step of providing crop phenology training data and providing a selection of a plurality of crops and specific location are data gathering activities. The steps of training a machine learning system using crop phenology training data and generating a crop phenology prediction involve analyzing data (using complex mathematics) and producing a result (the prediction). The steps of generating argronomic control data based on the crop prediction and controlling an agricultural device using the agronomic control data to apply a substance and/or plant or harvest involves analyzing data and producing a result (agronomic control data). The control limitation is broadly recited and interpreted as providing instructions to a person (pen and paper) or sending to a device, not actually controlling the device as no details on how the device is controlled are included in the claim.
Each of the additional of the additional limitations is no more than mere instructions to apply the exception using a generic computer components (e.g. a processor). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (e.g. a processor). Therefore, the additional elements do not integrate the abstract ideas into a practical application because it does not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
Applicant argues, “ Claim 1 thus recites a particular way of achieving a technological result (using a cross-location machine learning prediction architecture to generate control data by which an agricultural device performs field operations) rather than merely claiming the result itself.” (pg. 7)
Unlike a McRO or DDR the claims are not directed to a technological solution. The recite generic computer component performing generic computer steps such as providing, training , generating and controlling which encompass abstract concepts. As previously stated controlling an agricultural device using control data is broadly recited and interpreted providing instructions (control data ) to a device (see Spec ¶0024), without further details as to how the controlling occurs. The claims appear to provide an improved business process for determining crop phenology predictions to determine optimal times for harvesting, fertilizing or pest control (see ¶0002). There is no support in the Specification or the claims for an improvement in a technology or a technical field.
Applicant argues, “ Under Step 2B, even if claim 1 were deemed to recite an abstract idea not integrated into a practical application, the ordered combination recited in claim 1 is not merely a generic implementation of that idea.” (pgs. 7-8)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As stated above, the additional elements of a processor, a crm , a processing unit and input/output interface are considered generic computer components performing generic computer functions that amount to no more than instructions to implement the judicial exception. Mere, instructions to apply an exception using generic computer components cannot provide an inventive concept.
Applicant argues, “ The Office Action does not identify any reference, judicial notice, citation to the published application, or other evidence demonstrating that the claimed combination of (i) cross-location machine learning training on multi-crop, multi-location phenology data, … was well-understood, routine, and conventional.” (pgs. 7-8)
Examiner notes that no rejection was made regarding well-understood, routine and conventional limitations in the independent claims.
The remainder of Applicant’s arguments are moot in view of new grounds of rejection as necessitated by amendment.
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 1-7, 9-13 and 16-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites:
providing crop phenology training data for a plurality of crops and a plurality of locations;
training a machine learning system using the crop phenology training data;
providing a selection of the plurality of crops and a specific location, wherein the specific location is different from any of the plurality of locations or the selection of the plurality of crops is different from the crops at the specific location provided in the crop phenology training data;
generating a crop phenology prediction for the selection of the plurality of crops at the specific location using the trained machine learning system;
generating agronomic control data based on the crop phenology prediction; and
The limitation under its broadest reasonable interpretation covers Mental Processes related to observation and evaluation of data but for the recitation of generic computer components (e.g. a processor). For example, providing a crop phenology training data, training a machine learning system using training data, providing a selection of the plurality of crops and locations and generating a prediction involve collecting and analyzing data then producing a result. Accordingly, the claim recites an abstract idea of Mental Processes.
Additionally, the claims encompass Mathematical Concepts related to mathematical calculations relating to generating a crop prediction using a machine learning model.
The dependent claims encompass the same abstract ideas. For instance, Claims 2-5 is directed to crop phenology training data; Claim 6 is directed to crop phenology prediction includes growth state prediction (analysis); Claim 7 is directed to two locations and the specific location are on different continent/country; Claim 9 is directed to machine learning type; Claim 10 is directed to updating training data and machine learning system (analysis using complex math); Claim 11 is directed to generating an agronomic recommendation (analysis); Claim 12 is directed to for providing selection of crops and specific location and generating crop phenology prediction (analysis); Claim 13 is directed to CRM and processor; Claim 16 is directed to agricultural device type; Claim 17 is directed to crop phenology training data comprising historical data and Claim 18 is directed to using crop phenology prediction as an input for pest and/or disease model.
The judicial exceptions are not integrated into a practical application. Claim 1 recites the additional element of an agricultural device. Claim 12 recites the additional an input/output interface, a processing unit and an agricultural device. Claim 13 recites the additional elements of a non-transitory computer readable medium, a processor and an agricultural device. These are generic computer components recited at a high level of generality as performing generic computer functions (see Spec ¶0025)
For instance, the step of providing crop phenology training data and providing a selection of a plurality of crops and specific location are data gathering activities. The steps of training a machine learning system using crop phenology training data and generating a crop phenology prediction involve analyzing data (using complex mathematics) and producing a result (the prediction). The steps of generating argronomic control data based on the crop prediction and controlling an agricultural device using the agronomic control data to apply a substance and/or plant or harvest involves analyzing data and producing a result (argonomic control data). The control limitation is broadly interpreted as providing instructions to a person (pen and paper) or device, not actually controlling the device as no details on how the device is controlled are included in the claim.
Each of the additional of the additional limitations is no more than mere instructions to apply the exception using a generic computer components (e.g. a processor). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (e.g. a processor). Therefore, the additional elements do not integrate the abstract ideas into a practical application because it does not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As stated above, the additional elements of a processor, a crm , a processing unit and input/output interface are considered generic computer components performing generic computer functions that amount to no more than instructions to implement the judicial exception. Mere, instructions to apply an exception using generic computer components cannot provide an inventive concept.
The dependent claims when analyzed both individually and in combination are also held to be ineligible for the same reason above and the additional recited limitations fail to establish that the claims are not directed to an abstract. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
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, 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. Therefore, Claims 1-7, 9-13 and 16-18 are not patent eligible.
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4,5, 9-13 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Perry et al. (US 2019/0050948) further in view of Alvarez et al. (US 2020/0097851)
Claim 1:
Perry discloses:
A method for generating a crop phenology prediction comprising: providing crop phenology training data for a plurality of crops and a plurality of locations; (see at Abstract, machine learning to predict crop production and to identify a set of farming operations; see also ¶0004, the system trains a crop prediction engine using crop growth information (phenology data))
training a machine learning system using the crop phenology training data ; (see at least ¶0004, the system trains a crop prediction engine using crop growth information (phenology data); see at least ¶0011, crop growth information; see also ¶0070 train crop prediction models; see also ¶0118)
providing a selection of the plurality of crops and a specific location, wherein the specific location is different from any of the plurality of locations or the selection of the plurality of crops is different from the crops at the specific location provided in the crop phenology training data and (see at least ¶0014, selected set of farming operations includes variety of crops to plant; see also ¶0042, grower database include crop data; see also ¶0091, identifying a particular field and crop combination )
generating a crop phenology prediction for the selection of the plurality of crops at the specific location using the trained machine learning system . (see at least ¶0004, the system trains a crop prediction engine using crop growth information (phenology data); see also ¶0034, crop prediction based on trained prediction model; see also ¶0135)
generating agronomic control data based on the crop phenology prediction; and (see at least ¶0034, crop prediction model produces a set of farming operations that result in a measure of crop production at a specific time and location; see also ¶0121, farming operations including planting info and fertilization applications)
While Perry discloses the above limitations, Perry does not explicitly disclose the following limitation; however, Alvarez does disclose:
generating agronomic control data based on the crop phenology prediction (see at least Figure 8A and associated text; see also ¶0167-¶0168, deriving agricultural recommendations for the field and transmit directly to agricultural machine; see also ¶0067, modeling using field data; see also ¶0050, field data includes phenology and crop growth data)
controlling an agricultural device using the agronomic control data to apply an agricultural substance and/or agricultural product to a field at the specific location and/or to plant or harvest the field at the specific location. (see Figure 8A and ¶0176, the output generated by the model may be used to control an agricultural machine such as a tractor; see also Abstract )
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the training of crop prediction models utilizing crop growth data of Perry with the planting recommendations and outputting control information to an agricultural machine to perform agricultural tasks of Alvarez to facilitate implementation of recommendations (Spec see Abstract).
Claim 4:
Perry and Alvarez disclose claim 1. Perry further discloses:
wherein the crop phenology training data (1) comprises crop identifiers (9) and crop phenology indicators(10). (see at least ¶0004, training crop prediction engine using crop growth information; see also ¶0091, identifies a field or plot and crop varieties; see also ¶0035)
Claim 5:
Perry and Alvarez disclose claim 4. Perry further discloses:
wherein the crop phenology training data (1) further comprises at least one out of a group, the group consisting of geolocation identifiers (11), agricultural method identifiers (14), planting dates (12), days after planting (20), relationship of crop growth stage and accumulated growing degree days identifiers, biophysical descriptors (15), weather descriptors (13), and plant growth regulators application descriptors (16). (see at least ¶0077, cumulative growing degree days from crop types and planting dates)
Claim 9:
Perry and Alvarez disclose claim 1. Perry further discloses:
wherein the machine learning system (6) is a decision tree, in particular a gradient boosted decision tree, a computer-implemented neural network and/or an artificial neural network. (see at least ¶0139, gradient boosting; see also ¶0007, neural network)
Claim 10:
Perry and Alvarez disclose claim 1. Perry further discloses:
wherein the method further comprises: adding new data to the crop phenology training data (1); (see at least ¶0118, updating crop prediction model including new data is received for fields, crop variants and growth data)
updating the machine learning system (6) by training with the new data; and (see at least ¶0118, updating crop prediction model including new data is received for fields, crop variants and growth data)
generating an updated crop phenology prediction (7) for the selection of the plurality of crops at the specific location (18) using the updated machine learning system (6). (see at least ¶0118, updating crop prediction model including new data is received for fields, crop variants and growth data)
Claim 11:
Perry and Alvarez disclose claim 1. Perry further discloses:
comprising in particular a time, an amount of and/or a type of an agricultural substance and/or agricultural product to be applied to a field at the specific location (18) with the selection of the plurality of crops and/or a time for planting and/or harvesting a field at the specific location (18) with the selection of the plurality of crops. (see at least ¶0144, recommend replant and can identify farming operations including type of crop to plant, planting date to optimize crop production; see also ¶0011-¶0012; see also ¶0135)
Claim 12:
Perry discloses:
A system for generating a crop phenology prediction, the system comprising: at least one input interface for providing a selection of crops and a specific location, wherein the specific location is different from any of a plurality of locations provided in crop phenology training data or the selection of crops is different from crops at the specific location provided in the crop phenology training data; (see at least ¶0014, selected set of farming operations includes variety of crops to plant; see also ¶0042, grower database include crop data; see also ¶0091, identifying a particular field and crop combination, see also Abstract, training models; see also ¶0070 train crop prediction models; see also ¶0118 )
at least one processing unit configured to carry out a method for generating a crop phenology prediction according to the method of claim 1; and (see at least Figure 10 and associated text)
at least one output interface for outputting the crop phenology prediction, the agronomic control data for the selection of crops at the specific location to an agricultural device, (see at least ¶0034, crop prediction model produces a set of farming operations that result in a measure of crop production at a specific time and location; see also ¶0121, farming operations including planting info and fertilization applications; see also ¶0042, grower database include crop data; see also ¶0091, identifying a particular field and crop combination )
While Perry discloses the above limitations, Perry does not explicitly disclose the following limitation; however, Alvarez does disclose:
at least one output interface for outputting the crop phenology prediction, the agronomic control data for the selection of crops at the specific location to an agricultural device, (see at least Figure 8A and associated text; see also ¶0167-¶0168, deriving agricultural recommendations for the field and transmit directly to agricultural machine; see also ¶0067, modeling using field data; see also ¶0050, field data includes phenology and crop growth data)
the agricultural device being controlled by the agronomic control data to apply an agricultural substance and/or agricultural product to a field at the specific location and/or to plant or harvest the field at the specific location. (see Figure 8A and ¶0176, the output generated by the model may be used to control an agricultural machine such as a tractor; see also Abstract )
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the training of crop prediction models utilizing crop growth data of Perry with the planting recommendations and outputting control information to an agricultural machine to perform agricultural tasks of Alvarez to facilitate implementation of recommendations (Spec see Abstract).
Claim 13:
Perry and Alvarez disclose claim 1. Perry further discloses:
A non-transitory computer-readable medium having instructions encoded thereon that, when executed by a processor in a system (21), cause the processor to carry out a method according to claim 1. (see at least ¶0182, CRM)
Claim 16:
Perry and Alvarez disclose claim 1. Perry further discloses:
wherein the agricultural device is a smart spraying system, a smart seeding system, and/or a smart harvesting system. (see at least ¶0020, smart harvesting system)
Claims 2 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Perry et al. (US 2019/0050948) further in view of Alvarez et al. (US 2020/0097851) further in view of Sinha et al. (US 20210110157).
Claim 2:
While Perry and Alvarez disclose claim 1, and Perry further discloses crop growth information (see ¶0004) including historical information (see ¶0015, ¶0046), neither Perry nor Alvarez explicitly disclose the following limitations; however, Sinha does disclose:
wherein the crop phenology training data comprises historical crop phenology data, in particular crop phenology data of past seasons and/or crop phenology data of the current season (see at least ¶0039, training remote sensing imagery data and crop maturity ground truth data; see also ¶0018, past data)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the training of crop prediction models utilizing crop growth data of Perry and the planting recommendations and outputting control information to an agricultural machine to perform agricultural tasks of Alvarez with the predicting of phenological crop development of Sinha (see Spec ¶0061).
Claim 6:
While Perry and Alvarez disclose claim 1, neither Perry nor Alvarez disclose the following limitation; however, Sinha does disclose:
wherein the crop phenology prediction (7) comprises a growth stage (19) prediction, in particular on the BBCH scale. (see at least Figures 6A-6C and associated text; see also ¶0114, provides a comparison of observed ground truth crop maturity (converted from field-observed BBCH) and estimated crop maturity for all fields in each site; see also ¶0112)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the training of crop prediction models utilizing crop growth data of Perry and the planting recommendations and outputting control information to an agricultural machine to perform agricultural tasks of Alvarez with the predicting of phenological crop development including growth indicators of Sinha (see Spec ¶0061).
Claims 3, 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over (US 2019/0050948) further in view of Alvarez et al. (US 2020/0097851) further in view of Mewes et al. (US 2016/0247079).
Claim 3:
While Perry and Alvarez disclose claim 1, neither explicitly disclose the following limitation; however, Mewes does disclose:
wherein the crop phenology training data (1) comprises process model generated crop phenology data (5). (see at least ¶0028, crop and planting data is provided as output data from phenology models)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the training of crop prediction models utilizing crop growth data of Perry and the planting recommendations and outputting control information to an agricultural machine to perform agricultural tasks of Alvarez with the phenology models of Mewes to obtain crop specific characteristics (see ¶0028).
Claim 7:
While Perry and Alvarez disclose claim 1, neither explicitly disclose the following limitation; however, Mewes does disclose:
wherein at least two locations (17.1, 18; 17.2) out of the plurality of locations (17) and the specific location (18) are on different continents, in particular in different countries. (see at least ¶0096, datasets pertaining fields, farms or any combination of locations (township, county, country, etc.))
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the training of crop prediction models utilizing crop growth data of Perry and the planting recommendations and outputting control information to an agricultural machine to perform agricultural tasks of Alvarez with the phenology models of Mewes to obtain crop specific characteristics (see ¶0028).
Claim 17:
While Perry and Alvarez disclose claim 1, neither explicitly disclose the following limitation; however, Mewes does disclose:
wherein the crop phenology training data comprises historical crop phenology data and process model generated crop phenology data, wherein the historical crop phenology data exists for a first set of the plurality of crops and/or a first set of the plurality of locations, and the process model generated crop phenology data exists for a second set of the plurality of crops and/or a second set of the plurality of locations different from the first set. (see at least ¶0028, crop and planting data is provided as output data from phenology models; see also ¶0030, historical data; see also ¶0096, datasets pertaining fields, farms or any combination of locations (township, county, country, etc.))
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the training of crop prediction models utilizing crop growth data of Perry and the planting recommendations and outputting control information to an agricultural machine to perform agricultural tasks of Alvarez with the phenology models of Mewes to obtain crop specific characteristics (see ¶0028).
Claims 18 are rejected under 35 U.S.C. 103 as being unpatentable over Perry et al. (US 2019/0050948) further in view of Alvarez et al. (US 2020/0097851) further in view of Osborne (US 2014/0358486).
Claim 18:
While Perry and Alvarez disclose claim 1, neither explicitly disclose the following limitation; however, Osborne does disclose:
further comprising using the crop phenology prediction as an input for a pest and/or disease model that predicts an emergence of pests and/or diseases. (see at least ¶0055, pest prediction)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the training of crop prediction models utilizing crop growth data of Perry and the planting recommendations and outputting control information to an agricultural machine to perform agricultural tasks of Alvarez with the pest prediction service of Osborne to estimate pest emergence and disease development (see ¶0002).
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied:
Klein et al. (US 2019/0179009) discloses crop models includes creating, training, and updating crop models for determining at least crop growth rates of a plurality of different plants in a plurality of different fields.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Renae Feacher whose telephone number is 571-270-5485. The Examiner can normally be reached Monday-Friday, 9:00 am - 5:00 pm. If attempts to reach the examiner by telephone are unsuccessful, the Examiner's supervisor, Beth Boswell can be reached at 571-272-6737.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
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/Renae Feacher/
Primary Examiner, Art Unit 3625
