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 .
Election/Restrictions
Applicant's election with traverse of Group I in the reply filed on 07/21/2025 is acknowledged. The traversal is on the ground that there is unity of invention between the groups highlighted by the method steps listed in common between the two groups. This is not found persuasive because Group 2 is drawn to the physical processor that is configured to execute the steps of the method, and therefore is distinct from Groups I and III. Additionally, it is noted that Applicant has not argued against the refence that teaches the unifying features between the two inventions.
The requirement is still deemed proper and is therefore made FINAL.
Claims 1-5, 8, 10, 12-15, 18, 20, 24-25, 27, 30, 32, 36 and 55 are pending. Claims 13-15, 18, 20, 24-25, 27, 30, 32 and 36 are withdrawn as being drawn to nonelected subject matter.
Claims 1-5, 8, 10, 12 and 55 are examined herein on the merits.
Claim Rejections - 35 USC § 102
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.
Claims 1, 2, 4-5, 8, 12 and 55 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Parmley et al (2019 Nature Research Scientific Reports 9:17132).
The claims are drawn to a computer-implemented method for use in allocating test protocols associated with a plant breeding pipeline to a plurality of test locations comprising executing by a computing device a first stage machine learning prediction model based on protocol data for a plurality of test protocols for a current test experiment to generate a first stage output wherein the first stage is trained based on historical allocation data and, based on the first stage output, executing by the computing device a second stage optimization model to generate second stage output wherein the second stage output includes an allocation plan that comprises one or more of the plurality of test locations and storing the output in a memory that is accessible, wherein the MLPM is further based on test location data and wherein the test location data identifies one or more characteristics of each test location, harvesting the plants (claim 4), wherein the historical data includes one or more requirements for one or more historical test protocols wherein the plurality of allocation prediction scores represent probabilities that the test locations satisfy the test protocol, further comprising updating the historical allocation data, and further reserving one or more resources at the test locations identified.
Parmley et al teach a machine learning approach for prescriptive plant breeding wherein Random Forest (which is a machine learning prediction tool) was used to train models for seed yield prediction using a plurality of test protocols including row spacing, seeding density and training historical data such as canopy temperature, leaf area index and light interception, wherein the combination of variables was combined to maximize resource allocation (see data, particularly Figures 1 and 2 and Table 1), each of the locations is disclosed (see materials and methods) and harvesting was inherent in measuring seed yield.
Claim Rejections - 35 USC § 102
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.
Claims 1-5, 8, 12 and 55 are rejected under 35 U.S.C. 102(a)(1) as being Anticipated by Adebiyi et al (2020 Hindawi Scientifica 2020 pages 1-12 Machine Learning-Based Predictive Farmland Optimization and Crop Monitoring System published May 11, 2020).
The claims are drawn to a computer-implemented method for use in allocating test protocols associated with a plant breeding pipeline to a plurality of test locations comprising executing by a computing device a first stage machine learning prediction model based on protocol data for a plurality of test protocols for a current test experiment to generate a first stage output wherein the first stage is trained based on historical allocation data and, based on the first stage output, executing by the computing device a second stage optimization model to generate second stage output wherein the second stage output includes an allocation plan that comprises one or more of the plurality of test locations and storing the output in a memory that is accessible, wherein the MLPM is further based on test location data and wherein the test location data identifies one or more characteristics of each test location, harvesting the plants (claim 4), wherein the historical data includes one or more requirements for one or more historical test protocols wherein the plurality of allocation prediction scores represent probabilities that the test locations satisfy the test protocol, further comprising updating the historical allocation data, and further reserving one or more resources at the test locations identified and further comprising generating at least one interactive user interface representative of the allocation plan and displaying by the computing device the at least one interactive interface.
Adebiyi et al teach a computer-implemented method using machine learning applied to historical data including irrigation, spacing, nutrient requirements, location, temperature using Random Forest to generate output (see materials and methods 3.1) wherein the output is generated in a dataset on a mobile application (which is a user interface displayed, wherein this output is optimized using an optimization model (see figures 15 and 16, for example), wherein the allocation data was used to generate outputs and predictions to determine locations specifically (see bottom of page 8).
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 10 is rejected under 35 U.S.C. 103 as being unpatentable over Adebiyi et al 2020 (Hindawi Scientifica 2020 pages 1-12 Machine Learning-Based Predictive Farmland Optimization and Crop Monitoring System published May 11, 2020).
The claim is drawn to the above method wherein the first stage MLPM includes a recurrent neural network trained based on historical allocation data
Adebiyi et al teach a computer-implemented method using machine learning applied to historical data including irrigation, spacing, nutrient requirements, location, temperature using Random Forest to generate output (see materials and methods 3.1) wherein the output is generated in a dataset on a mobile application (which is a user interface displayed, wherein this output is optimized using an optimization model (see figures 15 and 16, for example), wherein the allocation data was used to generate outputs and predictions to determine locations specifically (see bottom of page 8). Adebiyi et al also teach in a review of existing technologies that a number of studies have used neural networks including with Random forest and with crop yield prediction (See Table 1).
Although Adebiyi et al do not specifically incorporate neural network training explicitly, it is clear that this was a well-known approach at the time of filing and available for plant breeding pipelines. It appears this is a design choice that would have readily been available to one of ordinary skill in the art.
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRENT T PAGE whose telephone number is (571)272-5914. The examiner can normally be reached M-F 7-4 EST.
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/BRENT T PAGE/Primary Examiner, Art Unit 1663
/Amjad Abraham/SPE, Art Unit 1663