Application/Control Number: 17/948,256 Page 2
Art Unit: 1686
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 .
Applicant Response
Applicant's response, filed 12/22/2025 has been fully considered. Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Claim Status
Claims 1-18 and 20 are canceled.
Claims 19 and 21-37 are pending and under examination herein.
Claim 19 is objected to.
Claims 19 and 21-37 are rejected.
Priority
The instant application claims the benefit of foreign priority to 2021-167728, filed 10/12/2021. as such, the effective filing date assigned to each of claims 19 and 21-37 is 10/12/2021. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Drawings
The drawings filed 09/20/2022 were accepted by the examiner in the office action mailed 10/21/2024.
Claim Objections
Claim 19 is objected to because of the following informalities: in the last step, “of the growing plant with by control” should be “of the growing plant This objection is newly recited and necessitated by claim amendments.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 19 and 21-37 remain rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Newly recited portions are necessitated by claim amendments.
Claim 19, and claims dependent therefrom, is unclear with respect to “changed control data”. The metes and bounds of claim 19, and all claims dependent thereon, are rendered indefinite by the lack of clarity. Specifically, the claim recites generating output control data based on the predicted one of the plurality of cultivation results in the previous step, and it is if unclear if the “changed control data” and the “output control data” refer to the same data. If they are different, the claims would lack essential steps for generating the changed output data. For the purposes of examination, the changed control data (recited in both claim 19 and 37) is interpreted to be the output control data.
Response to applicant’s arguments
Applicant states the claims have been amended to be more definite and requests the rejection be withdrawn (Applicant’s Arguments, p 11, para 2-3).
It is respectfully submitted that this is not persuasive, as the amendments have introduced the issues discussed above.
Claim Rejections - 35 USC § 101
The rejection of claims 19 and 21-37 under 35 U.S.C. 101 is withdrawn in view of claim amendments filed 12/22/2025, which integrate the recited judicial exceptions into practical application. Specifically the claim 19 steps of controllably changing a cultivation condition of the growing plant using the output control data based on the predicted cultivation results applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
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.
Claims 19 and 21-37 remain rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Perry et al. (US20190050948A1; previously cited; hereafter referred to as Perry). Newly recited portions are necessitated by claim amendments
With respect to claims 19 and 37, Perry discloses a crop prediction system with a computer system with modules that performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, optimize crop production, using a system environment which includes client devices, external databases containing past, present and future predicted data, sensor data sources, image data sources, and a crop prediction system (abstract; fig 1; fig 4; fig 10; para 0032; para 0090).
Perry discloses accessing field information comprises collecting the field information from one or more sensors located at the first portion of land, including soil temperature, air temperature, soil moisture, leaf temperature, leaf wetness, and spectral data over multiple wave length bands reflected from or absorbed by ground, etc., as well as from image data sources, and elaborates that field information can consist of current and/or historical information (para 0010-0018; fig 1; claim 11).
Perry discloses the image data can be used by machine learning operation of the crop prediction system to train crop prediction models, to apply crop prediction models to predict future crop production, and to identify farming operations that optimize future crop production, and can include data from previously grown plants that have been harvested (para 0070; para 00160). Perry discloses the image data, such as those from refractometers for measuring refractive light passing through a sample can be used to measure total dissolved solids in a sample (e.g. sugar content of an aqueous solution) such as a plant sap) (claim 1; para 0015; fig 1; fig 8-9; para 0063-76). Perry further discloses crop quality in the agricultural database used for the model can refer to any aspect of a crop, including damage levels from mold, insect, heat, cold, frost, other material damage, color of leaves, etc. of past, present and predicted future crop type or plant variant planted and data describing its growth and development (i.e. cultivation injury) (para 0036-0037; para 0091-0092; claim 52).
Perry discloses the training module can perform one or more machine learning operations to identify patterns or relationships within the training set of data based on feature values within the training set of data deemed potentially relevant to crop production associated with the field parameters (para 0115).
Perry also discloses the crop prediction engine can map combinations of field information inputs and farming operation inputs to crop productivity probability distributions (i.e. accuracy rates) based on one or more machine-learned relationships between combinations of portions of the crop growth information and corresponding crop productivities (para 0011). It is further noted that training of a machine learning model inherently involves a minimization of error and a maximization of accurate classification.
Perry further discloses the crop prediction module outputs a set of farming operations identified to result in an optimized crop production, such are a crop type to plant, a planting date range, dates and quantities of nitrogen application, watering timing instructions, and the crop prediction module can modify a crop growth program or a set of farming operations identified by a grower, and the crop prediction module outputs one or more farming operations in the set of farming operations directly to farming equipment (e.g., a smart tractor or sprinkler system) for implementation (i.e. generating output control data and controllably changing the cultivation condition based on output control data) (para 0163).
With respect to claim 21, Perry discloses the crop prediction engine can perform various machine learning operations and generate a crop prediction model to predict crop productions (para 0107; para 0166).
With respect to claim 22, Perry discloses the machine learning model could be a Bayesian Network model (para 0007).
With respect to claim 23-25 and 36, Perry discloses the training module can perform one or more machine learning operations to identify patterns or relationships within the training set of data based on feature values within the training set of data deemed potentially relevant to crop production associated with the field parameters (para 0115).
Perry further discloses the prediction model is trained on crop growth information and maps, for sets of land characteristics, one or more farming operations to crop productivities by performing one or more machine learning operations, such as a Bayesian Network model (para 0005; para 0007).
With respect to claims 26-30, Perry discloses normalizing (i.e. preprocessing) the crop growth information and training the models based on the normalized data and the crop prediction module determines an optimized crop production by comparing multiple predictions from multiple sets of farming operations and/or field parameters (i.e. choosing predictions with the greatest accuracy) (para 0004; para 0161). Perry also discloses the crop prediction engine can map combinations of field information inputs and farming operation inputs to crop productivity probability distributions based on one or more machine-learned relationships between combinations of portions of the crop growth information and corresponding crop productivities (para 0011). Perry further discloses normalizing the crop growth information can include one or more of: removing format-specific content from the crop growth information, removing or modifying portions of the crop growth information associated with values that fall outside of one or more predefined ranges, and scaling image information such that each image pixel represents a same distance (para 0006).
With respect to claims 31-35, Perry discloses the crop prediction module receives a request to generate an optimized crop production prediction for a field (which can include multiple fields or plots of land, adjacent or otherwise) and applies one or more crop prediction models data associated with the field to determine a set of farming operations to optimize a crop production for the field (para 0135).
Perry further discloses the crop prediction module also outputs a set of farming operations identified to result in an optimized crop production, such as crop type to plant, a planting date range, dates and quantities of nitrogen application, watering timing instructions, etc., and that the crop prediction module can modify a crop growth program or a set of farming operations identified by a grower, and can output the modified crop growth program or modified set of farming operations, for instance by highlighting the modifications made (para 0163).
Perry also discloses the crop prediction engine can map combinations of field information inputs and farming operation inputs to crop productivity probability distributions based on one or more machine-learned relationships between combinations of portions of the crop growth information and corresponding crop productivities (para 0011).
Perry also discloses the crop prediction engine can map combinations of field information inputs and farming operation inputs to crop productivity probability distributions based on one or more machine-learned relationships between combinations of portions of the crop growth information and corresponding crop productivities and that the crop prediction module can output a numerical value representing a predicted crop yield, such as the probability distribution (para 0011; para 0162).
Response to applicant’s arguments
Applicant states that the prior art to Perry fails to disclose an actual cultivation injury caused by cultivation as claimed, and cited para 0042 of the instant specification as support, (Applicant’s Arguments, p 14, para 4 – p 16, para 1).
It is respectfully submitted that this is not persuasive, as this is not commensurate with the scope of the claims. The claims nor the cited paragraph require that the injury be caused by cultivation, and the instant specification does not provide a definition for cultivation injury. The instant specification paragraph 0022 simple describes that the “injury in the cultivation of the plant may include at least one of a physiological injury such as blossom-end rot and fruit cracking, an injury due to a disease, or an injury due to a pest”. As discussed above, the prior art to Perry discloses crop quality in the agricultural database used for the model can refer to any aspect of a crop, including damage levels from mold, insect, heat, cold, frost, other material damage, color of leaves, etc. of past, present and predicted future crop type or plant variant planted and data describing its growth and development (i.e. cultivation injury) (para 0036-0037; para 0091-0092; claim 52). Even if the claims recited injury caused by cultivation, it can be argued that damage due to mold, insect, heat, cold, and frost, and damage in leaves that cause color change could be a result of cultivation practices. Therefore, Perry discloses the limitations of the instant claims and the rejection is maintained.
Conclusion
No claims allowed.
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.
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/N.D./ Examiner, Art Unit 1686
/Karlheinz R. Skowronek/ Supervisory Patent Examiner, Art Unit 1687