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's response filed 12/8/2025 has been fully considered. The following rejections
and/or objections are either reiterated or newly applied.
Status of Claims
Claims 1, 3-15, and 17-19 pending and examined on the merits.
Claims 2, 16, and 20 canceled.
Priority
The instant application claims the benefit of priority to U.S. Provisional Application No. 63/110,343 filed on 11/5/2020. Thus, the effective filing date of the claims is 11/5/2020.
The applicant is reminded that amendments to the claims and specification must comply with 35 U.S.C. § 120 and 37 C.F.R. § 1.121 to maintain priority to an earlier-filed application. Claim amendments may impact the effective filing date if new subject matter is introduced that lacks support in the originally filed disclosure. If an amendment adds limitations that were not adequately described in the parent application, the claim may no longer be entitled to the priority date of the earlier filing.
Information Disclosure Statement
The IDS forms filed on 12/8/2025 and 1/9/2026 have been entered and considered. A signed copy of the corresponding 1449 forms has been included with this Office action.
Claim Objections
The objection to claim 7 withdrawn in view of Applicant's claim amendments filed on 12/8/2025. The objection to claims 16 and 20 are moot in view of Applicant canceling these claims in the claim amendments filed on 12/8/2025.
Withdrawn Rejections
35 USC § 112(a)
The rejection of claim 16 under 35 U.S.C. 112(a) is moot in view of Applicant canceling this claim in the claim amendments filed on 12/8/2025.
35 USC § 112(b)
The rejection of claims 1, 9, and 19 under 35 U.S.C. 112(b) withdrawn in view of Applicant's claim amendments filed on 12/8/2025. The rejection of claim 16 under 35 U.S.C. 112(b) is moot in view of Applicant canceling this claim in the claim amendments filed on 12/8/2025.
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, 3-15, and 17-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental process, a mathematical concept, organizing human activity, or a law of nature or natural phenomenon without significantly more. In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claims 1 and 19: “selecting, by the processor, one or more machine learning models based on the obtained data” provides an evaluation (selecting a model based on obtained data) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“the one or more machine learning models were previously trained, using data that correlates other data and determined one or more infection biomarkers of one or more other plants” provides mathematical calculations (training a machine learning model) that are considered a mathematical concept, which is an abstract idea.
“determining predicted likelihoods that the one or more other plants are developing the latent infection” provides mathematical calculations (determining predicted likelihoods on infection) that are considered a mathematical concept, which is an abstract idea.
“applying the one or more machine learning models to the data” provides mathematical calculations (applying a machine learning model) that are considered a mathematical concept, which is an abstract idea.
“determining, by the processor, that the plant has a latent infection based on the output exceeding a predetermined threshold range” provides an evaluation (determining infection status based on a threshold range) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“determining, by the processor, one or more operations to mitigate the latent infection in the plant” provides an evaluation (determining a mitigation operation) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“determining [] (i) a modified harvest schedule that is earlier in a growing season; (ii) instructions to apply a predetermined amount of pesticide pre-harvest; and (iii) when the plant and the one or more other similarly sourced plants are nearing an end of respective healthy production lifespans” provides logical reasoning (determining a modified harvest schedule, instructions for applying pesticide, and nearing end of productive lifespan) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“the one or more machine learning models were previously trained, using data from the training data set, to determine (i)-(iii)” provides mathematical calculations (training a machine learning model) that are considered a mathematical concept, which is an abstract idea.
“generating an instruction” provides an evaluation (generating instructions requires evaluating received data) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 4: “generating an alert message” provides an evaluation (generating a message) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 5: “determining, based on the generated output, that an anti-microbial treatment should be prescribed” provides logical reasoning (determining prescription of a treatment) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claims 6-8: “generating instructions” provides an evaluation (generating instructions) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
These recitations are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or are mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. Additionally, while claims 19 and 20 recite performing some aspects of the analysis on a “system comprising: one or more processors; and one or more computer-readable storage devices having stored thereon instructions that, when executed by the one or more processors, cause the one or more processors to perform operations”, there are no additional limitations that indicate that this requires anything other than carrying out the recited mental processes or mathematical concepts in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental processes” grouping of abstract ideas. As such, claims 1, 3-15, and 17-19 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The judicial exceptions listed above are not integrated into a practical application because the claims do not recite an additional element or elements that reflects an improvement to technology. Specifically, the claims recite the following additional elements:
Claim 1: “obtaining, by a processor, data describing a level of expression of one or more infection biomarkers present in the plant” provides insignificant extra-solution activities (obtaining data are pre-solution activities involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
“generate output indicating a likelihood that the plant is developing a latent infection pre-harvest” provides insignificant extra-solution activities (outputting data are post-solution activities involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
“inputting, from a training data set and into the one or more machine learning models, (i) non-invasive measurements of the one or more other plants, (ii) invasive measurements of the one or more other plants, (iii) known plant information for the one or more other plants, and (iv) affirmative infection identifications for the one or more other plants” provides insignificant extra-solution activities (inputting data are pre-solution activities involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
“outputting the one or more machine learning models for runtime use” provides insignificant extra-solution activities (outputting data are post-solution activities involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
“generating, by the processor, output indicating the likelihood that the plant is developing the latent infection” provides insignificant extra-solution activities (outputting data are post-solution activities involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
“outputting, by the processor, an indication that the plant has the latent infection and the one or more determined operations” provides insignificant extra-solution activities (outputting data are post-solution activities involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 4: “transmitting the generated alert message to the user device” provides insignificant extra-solution activities (transmitting data are post-solution activities involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
Claims 6-8: “transmitting the instructions” provides insignificant extra-solution activities (transmitting data are pre-solution activities involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 9 as interpreted above: “performing nucleic acid sequencing on extracted plant products to obtain nucleic acid sequence data” provides insignificant extra-solution activities (performing sequencing to obtain data is a pre-solution activity involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application
Claim 15: “encoding, by the processor, the obtained data into a data structure for input” and “providing, by the processor, the encoded data structure as input” provides insignificant extra-solution activities (encoding and providing data as input is a pre-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 19: “one or more processors; and one or more computer-readable storage devices having stored thereon instructions that, when executed by the one or more processors, cause the one or more processors to perform operations” provides insignificant extra-solution activities (running instructions on generic computer components) that do not serve to integrate the judicial exceptions into a practical application.
The steps for obtaining, inputting, outputting, transmitting, and manipulating data are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application because they are pre- and post-solution activities involving data gathering and manipulation steps (see MPEP 2106.04(d)(2)). Furthermore, the limitations regarding implementing program instructions do not indicate that they require anything other than mere instructions to implement the abstract idea in a generic way or in a generic computing environment. As such, this limitation equates to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. Therefore, claims 1, 3-15, and 17-19 are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application, or equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment.
As discussed above, there are no additional elements to indicate that the claimed “system comprising: one or more processors; and one or more computer-readable storage devices having stored thereon instructions that, when executed by the one or more processors, cause the one or more processors to perform operations” requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. Additionally, the limitations for obtaining, inputting, outputting, transmitting, and manipulating data are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application. Furthermore, no inventive concept is claimed by these limitations as they are well-understood, routine, and conventional.
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1, 3-15, and 17-19 are not patent eligible.
Response to Arguments under 35 USC § 101
Applicant’s arguments filed 12/8/2025 are fully considered but they are not persuasive.
Applicant asserts that "the amended claims are not directed to a judicial exception such as an abstract idea of a mental process (Remarks 12/8/2025 Page 2). Examiner notes that amended independent claims 1 and 19 merely incorporate the limitations of claims 2 and 20 directly which have been explained as simply more judicial exceptions of the invention, and therefore are not additional elements that serve to integrate the judicial exceptions into a practical application (as indicated above). Applicant also argues "claim 1 cannot be performed in the human mind since it involves features that can only be performed by execution of system and based on instructions for performing operations by a device based on output indications that a plan[t] has a latent infection", and that "the claim language as a whole must be considered under the 101 analysis" (Remarks 12/8/2025 Pages 3-4). Examiner notes that while the output of the judicial exceptions are for use by a device to perform an operation, claims 4, 7, and 8 merely generate the instructions, and then transmits them to a "device" that has no particular physical characteristics (just functions). Therefore, the device is an additional element that simply provides insignificant extra-solution activities (running instructions on generic computer components) that do not serve to integrate the judicial exceptions into a practical application.
Applicant notes that "even if the claims were to be considered to recite an abstract idea [], the claim limitations impose meaningful limits on any such abstract idea and integrate the abstract idea into a practical application", and further asserts that the claim limitations are an improvement to a particular technological environment, that of mitigating latent plant infections (Remarks 12/8/2025 Pages 4-5). The Examiner notes that there is no particular treatment specified in the claims, and the determination of a modified harvest schedule, applying some predetermined amount of pesticide, nearing end of productive lifespan, and determining that an anti-microbial treatment should be prescribed are all generically claimed (i.e. these limitations are not imposing meaningful limits on the judicial exceptions).
Therefore, the rejection of claims 1 and 19 under 35 USC 101 is maintained. All other claims depend from these independent claims; therefore, their rejection is likewise maintained.
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.
Claims 1, 3-5, 9, 13, 15, and 17-19 rejected under 35 U.S.C. 103 as being unpatentable over Perry et al. (US-20190050948) in view of Layer et al. (US-20160132640) and Marticorena et al. (US-20170364816).
Regarding claims 1 and 19, Layer teaches a method for identifying pre-harvest latent infection in a plant (Para.0077 "rapid classification of unknown DNA samples enables a wide range of applications [] where quick turn-around time and minimal computational analysis requirements are vital. Such applications may include, [], agricultural settings (e.g., daily testing of crops and/or meat products for E. coli contamination)").
Layer also teaches obtaining data describing a level of expression of one or more infection biomarkers present in the plant, the infection biomarkers indicating a likelihood of infection in the plant (Para.0086 "Extensions of the versatile approaches of various embodiments of the present invention algorithm, method, system and computer readable medium shall include the capability to estimate the relative abundance of each species or genus by integrating tracking the presence of each sample k-mer among a set of reference k-mer catalogs from hundreds of relevant species or genera.").
Layer also provides that the data indicates differences between a read sequence of the plant and a reference genome of a healthy plant of a same type as the plant (Para.0005 "A staple of DNA analysis is the alignment and comparison of molecular sequences from an experimental sample to databases containing sequences from thousands of organisms in order to determine the most closely related species or strain. Alignment-based DNA identification techniques explicitly identify similarities between every sequence in the experimental sample and every sequence in the database."). As evidenced by Layer, it would be trivial for one of ordinary skill in the art to determine sequence differences between a sequence read and a reference genome from the same species or cultivar.
Layer does not explicitly teach training a machine learning model on the biomarkers, inputting, from a training data set and into the one or more machine learning models, (i) non-invasive measurements of the one or more other plants, (ii) invasive measurements of the one or more other plants, (iii) known plant information for the one or more other plants, and (iv) affirmative infection identifications for the one or more other plants, determining predicted likelihoods that the one or more other plants are developing the latent infection pre-harvest based on inputting (i)-(iv) into the one or more machine learning models, outputting the one or more machine learning models for runtime use, generating output indicating the likelihood that the plant is developing the latent infection pre-harvest based on applying the one or more machine learning models to the data, determining that the plant has a latent infection based on the output exceeding a predetermined threshold range, determining one or more operations to mitigate the latent infection in the plant, determining one or more operations to mitigate the latent infection in the plant comprising determining, for the plant and one or more other similarly sourced plants, at least one of: (i) a modified harvest schedule that is earlier in a growing season; (ii) instructions to apply a predetermined amount of pesticide pre-harvest; and (iii) when the plant and the one or more other similarly sourced plants are nearing an end of respective healthy production lifespans, nor outputting an indication that the plant has the latent infection and the one or more determined operations.
However, Perry teaches training a machine learning model on the biomarkers, inputting, from a training data set and into the one or more machine learning models, (i) non-invasive measurements of the one or more other plants, (ii) invasive measurements of the one or more other plants, (iii) known plant information for the one or more other plants, and (iv) affirmative infection identifications for the one or more other plants, determining predicted likelihoods that the one or more other plants are developing the latent infection pre-harvest based on inputting (i)-(iv) into the one or more machine learning models, outputting the one or more machine learning models for runtime use (Para.0143 "In some embodiments, a crop prediction model can be an ensemble machine learning model including one or more of a convolutional neural network, a spatial regression operation, a random forest classifier, and a partial least square regression operation trained on one or more of crop reflectance data, crop tissue samples, rainfall information, irrigation information, soil moisture information, solar radiation information, temperature information, and nitrogen application information", para.0010 "In an embodiment, accessing field information collected from the sensors includes one or more of: soil temperature, air temperature, soil moisture, leaf temperature, leaf wetness, and spectral data over multiple wave length bands reflected from or absorbed by ground. [], a soil wetness index, a number of cumulative growing degree days, a chlorophyll content, evapotranspiration, a daily light integral [].", and para.0091-92 "Examples of agricultural information stored by the agricultural database 140 can include: [] fungal infection damage").
Perry also teaches determining one or more operations to mitigate the latent infection in the plant (Para.0011 "In an embodiment, applying the crop prediction engine to the accessed field information comprises identifying, based on the one or more machine-learned relationships, the second set of farming operations" and para.0012 "The second set of operation can include one or more of: a seeding rate operation, a seeding date range operation, an operation to not plant a crop, an operation to plant a different type of crop than the first type of crop, and a fertilizer application operation. In an embodiment, the fertilizer application operation specifies an application of one or more macronutrient and/or micronutrient. In an embodiment, the second set of operations includes one or more of: a seeding depth operation, a harvest date range operation, a seed treatment operation, a foliar treatment operation, a floral treatment operation, a soil treatment operation, a reseeding operation, a microbial composition application operation, an insecticide application operation, an herbicide application operation, and a pesticide application operation.").
Perry also provides outputting an indication that the plant has the latent infection and the one or more determined operations (Para.0005 "Based on an output of the prediction model, the system selects a set of farming operations that maximize crop productivity and modifies a user interface displayed by a client device of the user to display a crop growth program based on the selected set of farming operations.").
Perry also teaches determining one or more operations to mitigate the latent infection in the plant comprising determining, for the plant and one or more other similarly sourced plants, at least one of: (i) a modified harvest schedule that is earlier in a growing season; (ii) instructions to apply a predetermined amount of pesticide pre-harvest; and (iii) when the plant and the one or more other similarly sourced plants are nearing an end of respective healthy production lifespans (Para.0011 "In an embodiment, applying the crop prediction engine to the accessed field information comprises identifying, based on the one or more machine-learned relationships, the second set of farming operations" and para.0012 "The second set of operation can include one or more of: a seeding rate operation, a seeding date range operation, an operation to not plant a crop, an operation to plant a different type of crop than the first type of crop, and a fertilizer application operation. In an embodiment, the fertilizer application operation specifies an application of one or more macronutrient and/or micronutrient. In an embodiment, the second set of operations includes one or more of: a seeding depth operation, a harvest date range operation, a seed treatment operation, a foliar treatment operation, a floral treatment operation, a soil treatment operation, a reseeding operation, a microbial composition application operation, an insecticide application operation, an herbicide application operation, and a pesticide application operation.").
However, Marticorena teaches generating output indicating the likelihood that the plant is developing the latent infection pre-harvest based on applying the one or more machine learning models to the data (Para.0027 "The present invention develops an infestation suitability model that is initiated by selecting, from all available crop management and weather data about infested fields, that data which is estimated to provide appropriate correlations with pest presence.")
Marticorena also teaches determining that the plant has a latent infection based on the output exceeding a predetermined threshold range (Para.0083 "Part of the process is to set a threshold [Threshold Discriminating Value (TDV)] for the single number that is the cutoff between likely to have fungus established sufficiently to be readily discerned by a scout and unlikely to have fungus established to the state of being scout discernible. Thus, the threshold may be set initially for a new growing season based on a threshold that worked well across a number of grid boxes for that crop/fungus pair in the prior year or years").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Perry as taught by Layer in order to rapidly identify species in a sample for determining or predicting disease or other conditions (para.0003 "This invention relates generally to the field of rapid identification and classification of unknown samples. More specifically, the invention is directed towards the method and system for identifying a species, subspecies, and/or strain of an unknown sample for determining or predicting the status of materials, diseases and conditions.). One skilled in the art would have a reasonable expectation of success because both methods are using nucleic acid sequencing for disease detection.
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Perry and Layer as taught by Marticorena in order to predict a particular infection on a crop at a specific location and point in time (abstract "A method to predict whether a particular fungus will be observed by a scout checking on a particular crop at a particular location on a particular date."). One skilled in the art would have a reasonable expectation of success because both methods use machine learning models for predicting plant infection.
Regarding claim 3, Perry in view of Layer and Marticorena teach the methods of Claims 1 and 2 on which this claim depends. Perry also teaches the one or more other similarly sourced plants include plants within a same zone as the plant (Para.0121 "The farming operations store 420 stores data describing various farming operations that can be performed by a grower on a field. [] The farming operations store 420 can provide information describing farming operations for use as inputs to crop prediction models, for instance by the crop prediction module 425 when attempting to identify a set of farming operations that optimize crop production.", and para.0132 "Operations describing portions or management zones of a field for one or more of: planting, applying fertilizers, applying nutrients, applying water, applying agricultural chemicals, applying irrigation or drainage operations, applying a microbial composition, applying a crop treatment, and the like").
Regarding claim 4, Perry in view of Layer and Marticorena teach the methods of Claim 1 on which this claim depends. Perry also teaches outputting an indication that the plant has the latent infection and the one or more determined operations comprises: generating an alert message that, when processed by a user device, causes the user device to output an alert notifying a user of the user device to perform one or more of the determined operations; and transmitting the generated alert message to the user device (Para.0005 "Based on an output of the prediction model, the system selects a set of farming operations that maximize crop productivity and modifies a user interface displayed by a client device of the user to display a crop growth program based on the selected set of farming operations.").
Regarding claim 5, Perry in view of Layer and Marticorena teach the methods of Claim 1 on which this claim depends. Perry also teaches determining one or more operations to mitigate the latent infection in the plant comprises: determining, based on the generated output, that an anti-microbial treatment should be prescribed for one or more other similarly sourced plants pre-harvest (Para.0128 "Operations describing field or crop treatments, including one or more of: a seed treatment operation, a foliar treatment operation, a floral treatment operation, an irrigation operation, a soil treatment operation, a reseeding operation, a microbial composition application operation, [], a fungicide application operation, an antibacterial operation []").
Regarding claim 9, Perry in view of Layer and Marticorena teach the methods of Claim 1 on which this claim depends. Layer also teaches the obtained data is generated by a nucleic acid sequencer based on sequencing plant products that are extracted from the plant (Para.0042 "Moreover, the system may include a genome sequencer device configured sequencing information from the unknown sample 265 to provide sequenced information. The sequencer device may be stationary or portable, or a combination of stationary and portable.").
Regarding claim 13, Perry in view of Layer and Marticorena teach the methods of Claim 1 on which this claim depends. Layer also teaches the one or more machine learning models include at least one of a binary logistic regression model, logistic model tree, random forest classifier, L2 regularization, partial least squares, and convolutional neural networks (Para.0041 "In an example, the machine learning may include one or more of any combination of the following: Naïve Bayes Classifier, Neural Networks, Decision Trees, Generalized Linear Models, Nearest Neighbors, Support Vector Machines, or “ensemble” methods such as Random Forests that combine the predictions of multiple supervised machine learning models. Still yet, the training may be accomplished through simulation.").
Regarding claim 15, Perry in view of Layer and Marticorena teach the methods of Claim 1 on which this claim depends. Layer also teaches encoding the obtained data into a data structure for input to the one or more machine learning models and providing the encoded data structure as input to the one or more machine learning models (Para.0040 "Still referring to FIG. 1, the constructing of k-mer profiles may be implemented with a probabilistic data structure. For instance, the probabilistic data structure may be one or more of any combination of the following: set of Bloom Filters, CountMin Sketch, Bitstate Hashing, and Hash Compaction; or other types as desired or required. [] In an example, the supervised learning algorithm comprises one or more of: machine learning or probabilistic selection; or other types as desired or required.").
Regarding claim 17, Perry in view of Layer and Marticorena teach the methods of Claim 1 on which this claim depends. Perry also teaches selecting machine learning models based on one or more prediction features identified from the obtained data, the one or more prediction features including at least one of a growing region of the plant, environmental conditions, plant type, stage of growth of the plant, non-invasive measurements of the plant, invasive measurements of the plant, dry matter content, gene expression, outgassed volatile components of the plant, a growing zone, and read sequence data of the plant (Para.0143 "In some embodiments, a crop prediction model can be an ensemble machine learning model including one or more of a convolutional neural network, a spatial regression operation, a random forest classifier, and a partial least square regression operation trained on one or more of crop reflectance data, crop tissue samples, rainfall information, irrigation information, soil moisture information, solar radiation information, temperature information, and nitrogen application information").
Regarding claim 18, Perry in view of Layer and Marticorena teach the methods of Claim 1 on which this claim depends. Perry also teaches the known plant information includes at least one of historic growing information about the one or more other plants, a growing season length, soil conditions, levels of precipitation, amounts of sunlight, environmental temperatures, a growing region, and a plant type (Para.0010 "In an embodiment, accessing field information collected from the sensors includes one or more of: soil temperature, air temperature, soil moisture, leaf temperature, leaf wetness, and spectral data over multiple wave length bands reflected from or absorbed by ground. In an embodiment, field information collected from the sensors is used to compute additional field information, including one or more of: a ratio of soil to air temperature, a ratio of leaf to air temperature, a soil wetness index, a number of cumulative growing degree days, a chlorophyll content, evapotranspiration, a daily light integral, a daily minimum temperature, a daily mean temperature, a daily maximum temperature, and a change in the normalized difference vegetation index.").
Claims 6 and 7 rejected under 35 U.S.C. 103 as being unpatentable over Perry et al. (US-20190050948) in view of Layer et al. (US-20160132640) and Marticorena et al. (US-20170364816) as applied to claims 1-5, 9, 13, 15, and 17-20 above, and further in view of Cutter et al. (US-20170258005).
Perry et al. in view of Layer et al. and Marticorena et al. are applied to claims 1-5, 9, 13, 15, and 17-20.
Regarding claim 6, Perry in view of Layer and Marticorena teach the method of Claim 5 on which this claim depends.
Layer nor Marticorena explicitly teaches determining one or more operations to mitigate the latent infection in the plant comprises: generating instructions that, when processed by an irrigation controller, cause the irrigation controller to automatically disperse a liquid including the anti-microbial treatment.
However, Perry teaches operations describing fungicide application and antibacterial operations (Para.0128 "Operations describing field or crop treatments, including one or more of: a seed treatment operation, a foliar treatment operation, a floral treatment operation, an irrigation operation, a soil treatment operation, a reseeding operation, a microbial composition application operation, [], a fungicide application operation, an antibacterial operation []").
Perry, Layer, nor Marticorena explicitly teaches transmitting the instructions to the irrigation controller.
However, Cutter teaches communicating stored data over a network when it is determined necessary to execute one or more functions (Para.0112 "For example, computing device 900 may communicate via network 71020 in FIG. 10A and data may be stored within network servers 1006 and transmitted back to computing device 1200 via network 1020 if it is determined that such stored data is necessary to execute one or more functions described herein.").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Perry, Layer, and Marticorena as taught by Cutter in order to determine a field health issue and remedy, and deploy the remedy autonomously (abstract "A comparison to a baseline may facilitate detection of a field health issue. When an issue with field health is determined, a remedy may be deployed. Deploying a remedy may use an unmanned vehicle, such as an unmanned aerial vehicle, or a liquid distribution manifold."). One skilled in the art would have a reasonable expectation of success because these approaches are focused on plant health by detecting issues such as infection, environmental conditions, etc.) and mitigating them (deploying anti-microbials, irrigation, etc.
Regarding claim 7, Perry in view of Layer and Marticorena teach the methods of Claim 1 on which this claim depends.
Perry, Layer, nor Marticorena explicitly teaches determining one or more operations to mitigate the latent infection in the plant comprising generating instructions that, when processed by a robotic device, cause the robotic device to (i) navigate to a location of the plant and (ii) disperse an anti-microbial treatment onto the plant and one or more other similarly sourced plants; and transmitting the instructions to the robotic device.
However, Cutter also teaches networked drones and pods (robotic devices) that navigates to a location of a plant or plants and is capable of monitoring and treating the plants (para.0021 "FIG. 1 illustrates a networked system 100 for monitoring, analyzing, and treating agricultural fields. As illustrated, the system includes pods, sensors, and unmanned devices (e.g., drones) to gather and communicate information related to agricultural fields. In aspects of the technology, the system uses drones, agricultural equipment, and pods as nodes in the network, each of which may be capable of monitoring field health, treating one or more plants, and communicating with other drones/pods.").
Claim 8 rejected under 35 U.S.C. 103 as being unpatentable over Perry et al. (US-20190050948) in view of Layer et al. (US-20160132640) and Marticorena et al. (US-20170364816) as applied to claims 1-5, 9, 13, 15, and 17-20 above, and further in view of Russel et al. (US-20190029178).
Perry et al. in view of Layer et al. and Marticorena et al. are applied to claims 1-5, 9, 13, 15, and 17-20.
Regarding claim 8, Perry in view of Layer and Marticorena teach the method of Claim 1 on which this claim depends.
Perry, Layer, nor Marticorena explicitly teaches determining one or more operations to mitigate the latent infection in the plant comprises: generating instructions that, when processed by a robotic device, cause the robotic device to (i) navigate to a location of the plant and (ii) harvest one or more plant products from the plant and one or more other similarly sourced plants before an expected harvesting timeframe.
However, Russel teaches automated harvesting plants using a robotic device capable of maneuvering to a plant’s location (Claim 12 "A method of harvesting crops with a robotic harvester, the method including the steps of: attaching a gripper to the crop using a robotic arm; decoupling the gripper from the robotic arm so that the robotic arm is operable to move while the gripper remains attached to the crop; moving the robotic arm to a position where a cutting mechanism of the robotic harvester can target a stem or stalk of the crop; and cutting the stem or stalk with the cutting mechanism while the gripper is decoupled and attached to the crop.").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Perry, Layer, and Marticorena as taught by Russel in order to automate the process of harvesting to improve efficiency by saving labor and providing less damaged produce (para.0002 "Harvesting of high value crops, such as capsicums, is a labour-intensive task that occurs multiple times during the growing season. Automation of the harvesting task may result in a significant labour saving and may provide gentler handling of the fruit"). One skilled in the art would have a reasonable expectation of success because these inventions involve harvesting crops via automated methods.
Claims 10 and 14 rejected under 35 U.S.C. 103 as being unpatentable over Perry et al. (US-20190050948) in view of Layer et al. (US-20160132640) and Marticorena et al. (US-20170364816) as applied to claims 1-5, 9, 13, 15, and 17-20 above, and further in view of Chalupowicz et al. (Chalupowicz et al. (2019), Diagnosis of plant diseases using the Nanopore sequencing platform. Plant Pathol, 68: 229-238. https://doi.org/10.1111/ppa.12957).
Perry et al. in view of Layer et al. and Marticorena et al. are applied to claims 1-5, 9, 13, 15, and 17-20.
Regarding claim 10, Perry in view of Layer and Marticorena teach the method of Claim 9 on which this claim depends.
Perry, Layer, nor Marticorena explicitly teaches the plant products include at least one of a sample of bark, leaf, flower, and fruit.
However, Chalupowicz teaches the plant products include at least one of a sample of bark, leaf, flower, and fruit (Pages 2-3 col 2-1 last-first paragraph "Two genomic DNA extraction protocols were used dependingon the plant tissue. DNA from leaf tissue was extracted withthe NucleoSpin Plant II Midi kit (Macherey-Nagel) following themanufacturer’s instructions for CTAB cell lysis-based buffer. TheMasterPure Complete DNA Purification kit (Epicentre) was used to isolate total DNA from seeds, lignified stems and fruits.").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Perry, Layer, and Marticorena as taught by Chalupowicz in order to apply the sequencing techniques for plant disease diagnosis (Page 8 col 1-2 last-first paragraph "The applicability of the Nanopore sequencing technique for diagnosis of plant diseases has not been examined with symptomless plants, and has not been tested with different pathogen levels in the tissues."). One skilled in the art would have a reasonable expectation of success because the methods are employing sequencing technologies for disease detection.
Regarding claim 14, Perry in view of Layer and Marticorena teach the methods of Claim 1 on which this claim depends.
Perry, Layer, nor Marticorena explicitly teaches the plant does not include visible signs of infection.
However, Chalupowicz also teaches the plant does not include visible signs of infection (Page 8 col 1-2 last-first paragraph "The applicability of the Nanopore sequencing technique for diagnosis of plant diseases has not been examined with symptomless plants, and has not been tested with different pathogen levels in the tissues. These points should be tested in further experiments.").
Claims 11 and 12 rejected under 35 U.S.C. 103 as being unpatentable over Perry et al. (US-20190050948) in view of Layer et al. (US-20160132640), Marticorena et al. (US-20170364816), and Chalupowicz (Chalupowicz et al. (2019), Diagnosis of plant diseases using the Nanopore sequencing platform. Plant Pathol, 68: 229-238. https://doi.org/10.1111/ppa.12957) as applied to claims 1-5, 9, 10, 13-15, and 17-20 above, and further in view of Aksenov et al. (Aksenov et al., Volatile Organic Compounds (VOCs) for Noninvasive Plant Diagnostics. American Chemical Society. Pest Management with Natural Products. Chapter 6, pp 73-95. published September 25, 2013. DOI: 10.1021/bk-2013-1141.ch006).
Perry et al. in view of Layer et al. and Marticorena et al. are applied to claims 1-5, 9, 13, 15, and 17-20.
Regarding claim 11, Perry in view of Layer and Marticorena teach the method of Claim 9 on which this claim depends.
Perry, Layer, nor Marticorena explicitly teaches the plant products are nondestructively sampled from the plant.
However, Aksenov teaches the plant products are nondestructively sampled from the plant (Page 9 paragraph 2 "In addition to the pre-concentration of VOCs onto sorbents, they can be directly collected by pumping the air off the plant into a canister or bag.").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Perry, Layer, and Marticorena as taught by Aksenov in order to noninvasively monitor plant health (page 1 abstract "Volatile organic compounds (VOCs) are produced by all plant systems, and present a possible noninvasive window through which we can monitor plant health"). One skilled in the art would have a reasonable expectation of success because all approaches are concerned with monitoring of plant health.
Regarding claim 12, Perry in view of Layer and Marticorena teach the methods of Claim 9 on which this claim depends.
Perry, Layer, nor Marticorena explicitly teaches the plant products include volatile components off-gassed by the plant.
However, Aksenov also teaches the plant products include volatile components off-gassed by the plant (Page 2 paragraph 1 "The expressed VOCs can be collected and measured using analytical methods to provide a momentary snapshot of the plant’s health status at any given time").
Response to Arguments under 35 USC § 103
Applicant’s arguments filed 12/8/2025 are fully considered but they are not persuasive.
Applicant argues that "none of Perry, Layer, and Marticorena has been shown to teach or suggest" each and every element of the amended claims (Remarks 12/8/2025 Page 6), and specifically that Perry "does not collect all these four types of data for one or more plants, and as such cannot be reasonably appreciated to have inputted such data into" machine learning models for their training (Remarks 12/8/2025 Page 7). Examiner notes that Perry does in fact teach or suggest using all these four data types (and more) for training of machine learning models (Para.0143 "In some embodiments, a crop prediction model can be an ensemble machine learning model including one or more of a convolutional neural network, a spatial regression operation, a random forest classifier, and a partial least square regression operation trained on one or more of crop reflectance data, crop tissue samples, rainfall information, irrigation information, soil moisture information, solar radiation information, temperature information, and nitrogen application information", para.0010 "In an embodiment, accessing field information collected from the sensors includes one or more of: soil temperature, air temperature, soil moisture, leaf temperature, leaf wetness, and spectral data over multiple wave length bands reflected from or absorbed by ground. [], a soil wetness index, a number of cumulative growing degree days, a chlorophyll content, evapotranspiration, a daily light integral [].", and para.0091-92 "Examples of agricultural information stored by the agricultural database 140 can include: [] fungal infection damage").
Therefore, the rejection of claims 1 and 19 under 35 USC 103 is maintained. All other claims depend from these independent claims; therefore, their rejection is likewise maintained.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-28 of US-20210151127 in view of Chalupowicz et al. (Chalupowicz et al. (2019), Diagnosis of plant diseases using the Nanopore sequencing platform. Plant Pathol, 68: 229-238. https://doi.org/10.1111/ppa.12957) and Aksenov et al. (Aksenov et al., Volatile Organic Compounds (VOCs) for Noninvasive Plant Diagnostics. American Chemical Society. Pest Management with Natural Products. Chapter 6, pp 73-95. published September 25, 2013. DOI: 10.1021/bk-2013-1141.ch006). Although the claims at issue are not identical, they are not patentably distinct from each other because both involve a method for identifying a latent infection in a plant product, obtaining and using nucleic acid sequencing data as infection biomarkers, and determining a difference in read sequence. They both also encode data for a machine learning approach, train a ML model (binary logistic regression, random forest classifier, L2 regularization, etc.) to determine an infection likelihood. Both also involve determining and performing mitigation operations, determining and deploying an anti-microbial treatment, and a robotic device that is able to maneuver, treat, and harvest plants. Finally, they both output data in the form of instructions and alert messages to a user device, and they both teach that the tested plant has no visible signs of infection.
While US-20210151127 does not explicitly teach the plant products include at least one of a sample of bark, leaf, flower, and fruit, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Chalupowicz as described above for claim 10 of the instant application, in order to apply the sequencing techniques for plant disease diagnosis (Page 8 col 1-2 last-first paragraph "The applicability of the Nanopore sequencing technique for diagnosis of plant diseases has not been examined with symptomless plants, and has not been tested with different pathogen levels in the tissues."). One skilled in the art would have a reasonable expectation of success because both methods are employing sequencing technologies for disease detection.
While US-20210151127 does not explicitly teach the plant products are nondestructively sampled from the plant, nor the plant products include volatile components off-gassed by the plant, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Aksenov as described above for claims 11 and 12 of the instant application, in order to noninvasively monitor plant health (page 1 abstract "Volatile organic compounds (VOCs) are produced by all plant systems, and present a possible noninvasive window through which we can monitor plant health"). One skilled in the art would have a reasonable expectation of success because both approaches are concerned with monitoring of plant health.
Response to Arguments under Double Patenting
Applicant’s arguments filed 12/8/2025 are fully considered but they are not persuasive.
Applicant merely does not concede the merits of the rejections. There are no other arguments. Therefore, the rejection of claims 1, 3-15, and 17-19 on the ground of nonstatutory double patenting is maintained.
Conclusion
No claims are allowed.
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 TH REE-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 finaI action.
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/R.A.P./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686