DETAILED ACTION
Examiner Remarks
After considering Applicant’s Remarks submitted on 06/30/2026, Examiner agrees with Applicant that the amended claim limitations now claim the disclosed improvement from Applicant’s Specification complying with MPEP §2106.05(a). Accordingly, Examiner has withdrawn the 101 rejection.
Response to Arguments
Applicant argues that the prior art of Lu does not teach the amended claim limitation of a layer of nodes of a plurality of clusters, as found in the independent claims. See pg., 11 of Applicant’s Remarks submitted on 06/30/2026.
Respectfully, Examiner disagrees. As MPEP §2111.01(I) details “[t]he plain meaning of a term means the ordinary and customary meaning given to the term by those of ordinary skill in the art at the relevant time…the best source for determining the meaning of a claim term is the specification - the greatest clarity is obtained when the specification serves as a glossary for the claim terms. Phillips v. AWH Corp., 415 F.3d 1303, 1315, 75 USPQ2d 1321, 1327 (Fed. Cir. 2005) (en banc) ("[T]he specification ‘is always highly relevant to the claim construction analysis. Usually, it is dispositive; it is the single best guide to the meaning of a disputed term.’" (quoting Vitronics Corp. v. Conceptronic Inc., 90 F.3d 1576, 1582 (Fed. Cir. 1996)).” With this guidance and/or case law in mind, Examiner was able to find paras. [0053-0056] of Applicant’s Specification that detailed the amended claim element of a layer of nodes of a plurality of clusters. Paras. [0053-0056] of Applicant’s Specification states that “a plurality of
clusters is set as nodes for a second layer…a plurality of nodes of environmental conditions in the first layer belongs to each node of clusters may be indicated by an arrow.”(Emphasis added). Accordingly, under the Broadest Reasonable Interpretation, in light of Applicant’s Specification, the prior art of Lu teaches the claim element of a layer of nodes of a plurality of clusters. See the current Office Action for the detailed teaching.
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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JP 2021-124987, filed on 07/30/2021.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) are:
a cultivation condition acquisition unit configured to acquire a cultivation condition under which a plant is cultivated;
a trouble acquisition unit configured to acquire a trouble occurrence situation in cultivation of the plant;
a model generation unit configured to generate, by using the cultivation condition and the trouble occurrence situation, a model for predicting one of a cultivation condition or a trouble from the other;
an estimation unit configured to estimate, by using the model, a cultivation condition for suppressing occurrence of a trouble in cultivation of the plant.
an output unit configured to output data....
in claim 1.
a preprocessing unit configured to perform preprocessing on data of at least one of the cultivation condition or the trouble occurrence situation,
wherein the model generation unit is configured to generate, by using the preprocessed data, a model for predicting one of the cultivation condition or the trouble from the other.
in claim 2.
wherein the preprocessing unit is configured to perform the preprocessing including at least one of associating the data of the cultivation condition with the data of the trouble occurrence situation on a time axis, complementing the data of the at least one of the cultivation condition or the trouble occurrence situation, processing an outlier of the data of the at least one of the cultivation condition or the trouble occurrence situation, or performing rounding processing of the data of the at least one of the cultivation condition or the trouble occurrence situation.
in claim 3.
an extraction unit configured to extract a feature amount of data of at least one of the cultivation condition or the trouble occurrence situation,
wherein the model generation unit is configured to generate the model based on the feature amount.
in claim 4.
an extraction unit configured to extract a feature amount of the data of the at least one of the cultivation condition or the trouble occurrence situation,
wherein the model generation unit is configured to generate the model based on the feature amount.
in claim 5.
wherein the extraction unit is configured to extract, as the feature amount, at least one of an integrated value, a differential value, or data obtained by dissociating a daytime component and a nighttime component with respect to the data of the at least one of the cultivation condition or the trouble occurrence situation.
in claim 6.
wherein the model generation unit is configured to cluster data of at least one of the cultivation condition or the trouble occurrence situation and generate the model of the Bayesian network model structure.
in claim 9.
wherein the cultivation condition acquisition unit is configured to acquire, as the cultivation condition, plant data indicating an environmental condition under which the plant is cultivated and a state of the cultivated plant
in claim 12.
wherein the cultivation condition acquisition unit is configured to acquire, as the cultivation condition, plant data indicating an environmental condition under which the plant is cultivated and a state of the cultivated plant.
in claim 13.
an estimation unit configured to estimate, by using a model for predicting one of a cultivation condition under which a plant is cultivated or a trouble in cultivation of the plant from the other, a cultivation condition for suppressing occurrence of a trouble in cultivation of the plant;
and an output unit configured to output data indicating the estimated cultivation condition.
in claim 17.
a cultivation condition acquisition unit configured to acquire the cultivation condition;
a trouble acquisition unit configured to acquire the trouble occurrence situation;
and a model update unit configured to update the model by using the cultivation condition and the trouble occurrence situation.
in claim 18.
a cultivation condition acquisition unit configured to acquire a cultivation condition under which a plant is cultivated;
a trouble acquisition unit configured to acquire a trouble occurrence situation in cultivation of the plant;
a model generation unit configured to generate, by using the cultivation condition and the trouble occurrence situation, a model for predicting one of a cultivation condition or a trouble from the other;
an estimation unit configured to estimate, by using the model, a cultivation condition for suppressing occurrence of a trouble in cultivation of the plant;
an output unit configured to output data...
in claim 20.
Because these claim limitation(s) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.1
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.
The factual inquiries 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 1-6, 9, and 12-20 are rejected under 35 U.S.C. 103 as being unpatentable over Perry et al., US 2019/0050948 Al(“Perry”) in view of Weldemariam et al., US 11,120,552 B2(“Weldemariam”) and in view of Lu W., Disease risk forecasting with Bayesian learning networks: Application to grape powdery mildew (Erysiphe necator) in vineyards. Agronomy. 2020 Apr 28(“Lu”).
Regarding claim 1, Perry teaches a cultivation assistance system for assisting with cultivation of a plant, the cultivation assistance system comprising:
a cultivation condition acquisition unit configured to acquire a cultivation condition under which a plant is cultivated(Perry, paras. 0054-0077, see also fig. 1, “The sensor data sources 114 are one or more sources of data taken from sensors describing past or current measurements associated with crop production[a cultivation condition acquisition unit configured to acquire a cultivation condition under which a plant is cultivated].”);
a model generation unit configured to generate, by using machine learning the cultivation condition [and the trouble occurrence situation], a model for predicting a cultivation condition [from an occurrence of a trouble or the occurrence of the trouble from the cultivation condition]wherein the model is a computer model(Perry, para. 0077, see also figs 1 and 4, “The crop prediction system 125 receives data from the external databases 112, sensor data sources 114, and image data sources 116, and performs machine learning operations[by using machine learning] on the received data to produce one or more crop prediction models[a model generation unit configured to generate, the cultivation condition a model for predicting one of a cultivation condition; wherein the model is a computer model].”);2, 3
and an output unit configured to output data indicating the estimated cultivation
condition to a display apparatus [such that the estimated cultivation condition can be
applied to the cultivation of the plant in order to suppress the occurrence of the trouble
without requiring a corresponding chemical treatment to be applied](Perry, para. 0140, see also figs., 1 and 7 “[T]he crop prediction module 425 identifies a crop variant and a set of farming operations corresponding to the identified crop variant, for instance for display on the grower client device 102[and an output unit configured to output data indicating the estimated cultivation condition to a display apparatus].”),4
wherein the model is of a Bayesian network model structure, and wherein the Bayesian network model structure has at least a layer of nodes of data of the cultivation condition [and a layer of at least one node for the trouble occurrence situation](Perry, para. 0109, “For example, the crop prediction engine 155 can apply a Bayesian network to information describing plots of land within 500 meters of a body of water and corresponding rice production[wherein the model is of a Bayesian network model structure, and has at least a layer of nodes of data of the cultivation condition] in order to train a crop prediction model that maps proximity to water to rice production.”).5
While Perry teaches a model generation unit configured to generate, by using the cultivation condition, a model for predicting one of a cultivation condition, and an output unit configured to output data indicating the estimated cultivation condition to a display apparatus, Perry does not teach:
a trouble acquisition unit configured to acquire a trouble occurrence situation in cultivation of the plant; and the trouble occurrence situation from an occurrence of a trouble or the occurrence of the trouble from the cultivation condition; and an estimation unit configured to estimate, a cultivation condition for suppressing occurrence of the trouble in the cultivation of the plant; such that the estimated cultivation condition can be applied to the cultivation of the plant in order to suppress the occurrence of the trouble without requiring a corresponding chemical treatment to be applied; and wherein the cultivation assistance system automatically estimates, one or more recommendation values for one or more cultivation conditions for suppressing the occurrence of the trouble in the cultivation of the plant.
However, Weldemariam teaches:
a trouble acquisition unit configured to acquire a trouble occurrence situation in cultivation of the plant(Weldemariam, cols. 11-12, see also fig. 3B, “The feature extractor 308 obtains photographic or video images of crops and identifies or extracts crop related features from the images, such as crop size, crop color, visual spots, virus/insects, and the like, as described more fully above[a trouble acquisition unit configured to acquire]. For example, the low magnification images may show that the crops are maturing at different rates within different locations within the farm. Alternatively, a high magnification image might show an insect infestation and enable the identity of the insects to be determined[a trouble occurrence situation in cultivation of the plant].”);
[a model generation unit configured to generate, by using the cultivation condition] and the trouble occurrence situation, [a model for predicting a cultivation condition] from an occurrence of a trouble or the occurrence of the trouble from the cultivation condition [wherein the model is a computer model] (Weldemariam, cols. 11-12, see also fig. 3B, “For example, the low magnification images may show that the crops are maturing at different rates within different locations within the farm. Alternatively, a high magnification image might show an insect infestation and enable the identity of the insects to be determined[and the trouble occurrence situation from an occurrence of a trouble or the occurrence of the trouble from the cultivation condition].”);6 7
and an estimation unit configured to estimate, [by using the model,] a cultivation condition for suppressing occurrence of the trouble in cultivation of the plant(Weldemariam, cols. 11-12, see also fig. 3B, “The productivity forecaster 324 estimates the productivity for a crop based on the crop knowledge graph 380, water uptake patterns, social characteristics, crop type, visual analytics (to, for example, detect disease and insect infestation), weather, and the like[and an estimation unit configured to estimate, a cultivation condition]… [t]he alert and notification unit 336 issues recommendations to users based on results generated by the system 300. As described above, the recommendations may suggest that a farmer pick crops early, may warn of the detection of blight or insects infesting the crops and therefore recommend spraying a pesticide[for suppressing occurrence of a trouble in cultivation of the plant].”)8
[and an output unit configured to output data indicating the estimated cultivation
condition to a display apparatus] such that the estimated cultivation condition can be applied to the cultivation of the plant in order to suppress the occurrence of the trouble without requiring a corresponding chemical treatment to be applied(Weldemariam, cols. 8-9, see also fig. 3A, “FIG. 3A is a graph of an example crop blueprint 390 for a tomato crop, in accordance with an example embodiment. Each column of the crop blueprint 390 corresponds to a growth stage of the crop. In one example embodiment, the growth stages are establishment, vegetative growth, flowering, fruit set, and maturity... for example, mature red tomatoes will
retain a high quality for approximately four to seven days if stored at 90 to 95 percent relative humidity and at a temperature of 46° F. to 50° F. Fruit borers (insects), however, cause up to 70 percent of fruit loss. Therefore, this may indicate that, when the crop is infested with fruit borers,
either the fruit should be picked on time or early to avoid the loss of the fruit. If the health or quality of the crop drops from an expected level L for the current stage of crop growth, various actions are recommended, triggered, performed, or any combination thereof by the system.
Examples of actions include: recommending that the farmer pick the crops early, if possible[such that the estimated cultivation condition can be applied to the cultivation of the plant in order to suppress the occurrence of the trouble without requiring a corresponding chemical treatment to be applied]”)9
and wherein the cultivation assistance system automatically estimates, [by using the model], one or more recommendation values for one or more cultivation conditions for suppressing the occurrence of the trouble in the cultivation of the plant(Weldemariam, col. 13-14, see also fig. 4, “The action may be triggered by a condition, such as the health or quality of the crop in relation to an expected health or quality level L for the current stage of crop growth[and wherein the cultivation assistance system automatically estimates,]… [t]he actions include… recommending the application of chemicals (such as pesticides,
herbicides, insecticides, fungicides, and the like)[ one or more recommendation values for one or more cultivation conditions for suppressing the occurrence of the trouble in the cultivation of the plant]….”).10
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Perry with the teachings of Weldemariam the motivation to do so would be to use deep learning methods to positively intervene during the crop growing process to produce higher crop yields(Weldemariam, col. 1, “Crop quality assessments are important for managing risks during the growing season and enabling price premiums after harvest… [i]n addition, there is an absence of uniform guidelines for farmers to use on how to grow crops. Principles of the invention provide techniques for crop grading via deep learning… [to] determin[e] a crop health status based on the one or more crop related features, an environmental context, a growth stage of the crop, and a farm cohort by using a computerized deep learning system to perform an automated growth stage analysis; and at least one of recommending, triggering, and performing one or more actions.”).
Perry in view of Weldemariam do not teach: a layer of nodes of a plurality of clusters, and a layer of at least one node for the trouble occurrence situation
However, Lu teaches:
a layer of nodes of a plurality of clusters, and a layer of at least one node for the trouble occurrence situation(Lu, pgs., 18-19, see also figs., 6 and 7 and Table 2, “The representative DAG for the best-performing model under supervised (Case 2) and algorithm (Case 2) learning is shown in Figures 6 and 7. In the case of supervised learning, the DAG is a plant stage based network... DI [disease incidence][ and a layer of at least one node for the trouble occurrence situation] is independently affected by a set of factors including…primary infection rate(PIR), secondary infection rate (SIR)…dispersal rate (DR)…past DI (DIP); and genes cultivar types (Type)[ a layer of nodes of a plurality of clusters]. The network structure learned from supervised learning shows causal relationships based on existing knowledge, with dispersal rate (DR) of grape PM spores being influenced by wind-speed conditions, secondary infection (SIR), and temperature (Figure 6).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Perry in view of Weldemariam with the teachings of Lu the motivation to do so would be to construct an accurate prediction model for implementing a fungicide spray program to control plant diseases for suppression purposes(Lu, pg., 15, “A fungicide spray program was identified for maximizing spray efficiency for disease control based on the Bayesian network model and forecasting windows... [t]he program guides grape advisor or growers by providing the best times to spray fungicide for PM control in relation to disease risk (future disease incidence), local weather, and regional climate variability.”).
Regarding claim 2, Perry in view of Weldemariam and Lu teaches the cultivation assistance system according to claim 1, comprising:
a preprocessing unit configured to perform preprocessing on data of at least one of the cultivation condition or the trouble occurrence situation(Perry, paras. 0103-0106, “For a particular type of data, the normalization module 145 selects a common format, normalizes received data of the particular type into the common format, and stores the normalized data within the geographic database 135 and the agricultural database 140.…[f]or instance, the normalization module 145 can receive and normalize an updated set of historic temperature data, and the database interface module 150 can replace the previous historic temperature data stored within the geographic database 135 with the updated normalized temperature data[a preprocessing unit configured to perform preprocessing on data of at least one of the cultivation condition].”),11
wherein the model generation unit is configured to generate, by using the preprocessed data, a model for predicting the cultivation condition from the occurrence of the trouble or the occurrence of the trouble from the cultivation condition(Perry, paras. 0106-0107, “For instance, the database interface module 150 receives normalized data from the normalization module 145 and stores the normalized data in the geographic database 135 and the agricultural database 140… [t]he database interface module 150 can query the "soil acidity" column within the geographic database 135 to identify plots of land associated with a below threshold soil acidity, and can query the agricultural database 140 to obtain fertilization information (such as type of fertilizers applied, quantity of fertilization applied, data of application, and resulting crop production) associated with the identified plots of land. The crop prediction engine can then provide the retrieved fertilization information to the crop prediction engine 155, which in tum can apply, for instance, a neural network to the fertilization information to generate a crop prediction model mapping low soil acidity and fertilization operations to crop production[generate, by using the preprocessed data, a model for predicting the cultivation condition from the occurrence of the trouble or the occurrence of the trouble from the cultivation condition].”).12
Regarding claim 3, Perry in view of Weldemariam and Lu teaches the cultivation assistance system according to claim 2, wherein the preprocessing unit is configured to perform the preprocessing including at least one of associating the data of the cultivation condition with the data of the trouble occurrence situation on a time axis, complementing the data of the at least one of the cultivation condition or the trouble occurrence situation, processing an outlier of the data of the at least one of the cultivation condition or the trouble occurrence situation, or performing rounding processing of the data of the at least one of the cultivation condition or the trouble occurrence situation(Perry, para. 0103-0104, “In addition, the normalization module 145 can "clean" various types of data, for instance by upscaling/downscaling image data, by removing outliers from quantitative or measurement data[processing an outlier of the data of the at least one of the cultivation condition], and interpolating sparsely populated portions of datasets. Based on the data format and corresponding method of normalization, the normalization module 145 can apply one or more normalization operations including but not limited to: standardizing the crop growth information to a common spatial grid and common units of measure; interpolating data using operations that include Thiessen polygons, kriging, isohyetal, and inverse distance weighting[complementing the data of the at least one of the cultivation condition]; detecting and correcting inconsistent data such as erroneously abrupt changes in a time-series of measurements that are physically implausible; associating geographic locations with pixels in an image; identifying missing values that are encoded in different ways by different data sources; imputing missing values by estimating them using values of nearest neighbors and/or using multiple imputation; and the like.”).13
Regarding claim 4, Perry in view of Weldemariam and Lu teaches the cultivation assistance system according to claim 1, comprising: an extraction unit configured to extract a feature amount of data of at least one of the cultivation condition or the trouble occurrence situation, wherein the model generation unit is configured to generate the model based on the feature amount(Perry, para. 0077, “The crop prediction system 125 receives data from the external databases 112, sensor data sources 114, and image data sources 116, and performs machine learning operations on the received data to produce one or more crop prediction models. The data from these data sources can be combined, and a standard feature set can be extracted from the combined data[an extraction unit configured to extract a feature amount of data of at least one of the cultivation condition], enabling crop prediction models to be generated across different temporal systems, different spatial coordinate systems, and measurement systems[generate the model based on the feature amount].”).14
Regarding claim 5, Perry in view of Weldemariam and Lu teaches the cultivation assistance system according to claim 2, comprising: an extraction unit configured to extract a feature amount of the data of the at least one of the cultivation condition or the trouble occurrence situation, wherein the model generation unit is configured to generate the model based on the feature amount(Perry, para. 0077, “The crop prediction system 125 receives data from the external databases 112, sensor data sources 114, and image data sources 116, and performs machine learning operations on the received data to produce one or more crop prediction models. The data from these data sources can be combined, and a standard feature set can be extracted from the combined data, enabling crop prediction models to be generated across different temporal systems, different spatial coordinate systems, and measurement systems.”).15
Regarding claim 6, Perry in view of Weldemariam and Lu teaches the cultivation assistance system according to claim 4, wherein the extraction unit is configured to extract, as the feature amount, at least one of an integrated value, a differential value, or data obtained by dissociating a daytime component and a nighttime component with respect to the data of the at least one of the cultivation condition or the trouble occurrence situation(Perry, paras. 0102-104, “Data calculated based on other agricultural information, including…a daily light integral[extract, as the feature amount, at least one of an integrated value], photosynthetically active radiation[or data obtained by dissociating a daytime component and a nighttime component with respect to the data of the at least one of the cultivation condition]… the normalization module 145 selects a common format, normalizes received data of the particular type into the common format….”).16
Regarding claim 9, Perry in view of Weldemariam and Lu teaches the cultivation assistance system according to claim 1, wherein the model generation unit is configured to cluster data of at least one of the cultivation condition or the trouble occurrence situation and generate the model of the Bayesian network model structure(Perry, para. 0109, “The crop prediction engine 155 can request data from the various external data sources described herein for storage within the geographic database 135 and the agricultural database 140, and can perform the machine learning operations on the stored data[cluster data of at least one of the cultivation condition]. For example, the crop prediction engine 155 can apply a Bayesian network[and generate the model of the Bayesian network model structure] to information describing plots of land within 500 meters of a body of water and corresponding rice production in order to train a crop prediction model that maps proximity to water to rice production.”).17
Regarding claim 12, Perry in view of Weldemariam and Lu teaches the cultivation assistance system according to claim 1, wherein the cultivation condition acquisition unit is configured to acquire, as the cultivation condition, plant data indicating an environmental condition under which the plant is cultivated and a state of the cultivated plant(Perry, paras. 0170-0171, see also fig. 7, “In the example of FIG. 7, the grower 610 accesses the crop prediction system 125 via a grower client device 102 on Jul. 5, 2018, and transmits a request to the crop prediction system for an updated crop production prediction and an updated set of farming operations to perform to optimize the crop production of the planted field 705A. The request includes information describing a current state of the planted field 705A, including a crop variant planted in the field and any farming operations that have been performed on the field[acquire, as the cultivation condition, plant data indicating an environmental condition under which the plant is cultivated and a state of the cultivated plant].”).
Regarding claim 13, Perry in view of Weldemariam and Lu teaches the cultivation assistance system according to claim 2, wherein the cultivation condition acquisition unit is configured to acquire, as the cultivation condition, plant data indicating an environmental condition under which the plant is cultivated and a state of the cultivated plant(Perry, paras. 0170-0171, see also fig. 7, “In the example of FIG. 7, the grower 610 accesses the crop prediction system 125 via a grower client device 102 on Jul. 5, 2018, and transmits a request to the crop prediction system for an updated crop production prediction and an updated set of farming operations to perform to optimize the crop production of the planted field 705A. The request includes information describing a current state of the planted field 705A, including a crop variant planted in the field and any farming operations that have been performed on the field.”).
Regarding claim 14, Perry in view of Weldemariam and Lu teaches the cultivation assistance system according to claim 1, wherein the cultivation condition comprises one or more cultivation conditions, and the cultivation assistance system comprises one or more sensors for measuring the one or more cultivation conditions(Perry, para. 0054, “The sensor data sources 114 are one or more sources of data taken from sensors describing past or current measurements associated with crop production that can be used by machine learning processes of the crop prediction system 125 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[one or more cultivation conditions, and the cultivation assistance system comprises one or more sensors for measuring the one or more cultivation conditions].”).
Regarding claim 15, Perry in view of Weldemariam and Lu teaches the cultivation assistance system according to claim 1, wherein the cultivation condition comprises at least one environmental condition and the estimation unit is configured to estimate, by using the model, a range for the at least one environmental condition for suppressing the occurrence of the trouble in the cultivation of the plant (Weldemariam, col. 9, “The assessment of the health of the crop can use a decision tree[the estimation unit is configured to estimate, by using the model] induction technique to generate new classification rules based on an analysis of crop features obtained by monitoring crops and their properties, where each rule set is given as a classifier. Models generated from decision trees are stored in a graph-based knowledge representation for easy interpretation as it also uses ontology to model contextual information. For example, mature red tomatoes will retain a high quality for approximately four to seven days if stored at 90 to 95 percent relative humidity[at least one environmental condition] and at a temperature of 46° F. to 50° F[a range for the at least one environmental condition for suppressing the occurrence of the trouble in the cultivation of the plant].”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Perry with the above teachings of Weldemariam for the same rationale stated at Claim 1.
Regarding claim 16, Perry in view of Weldemariam and Lu teaches the cultivation assistance system according to claim 15, wherein the plant is cultivated in an environment having a nighttime temperature, and wherein the at least one environmental condition is the nighttime temperature of the environment of the plant(Lu, pg., 8, “The GEFSR ensemble consists of 1 control forecast and 10 perturbed ensemble members, with archived reforecasts available from December 1984 until the present. Reforecasts are recorded at 3-hourly intervals for lead times from 0 to 72 h...[f]or PM disease risk forecasting, a set of available reforecasted weather and climate variables were selected from the GEFSR archive (i.e., minimum temperature, maximum temperature[wherein the plant is cultivated in an environment having a nighttime temperature, and wherein the at least one environmental condition is the nighttime temperature of the environment of the plant]....”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Perry in view of Weldemariam with the above teachings of Lu for the same rationale stated at Claim 1.
Regarding claim 17, Perry teaches a cultivation assistance system for assisting with cultivation of a plant, the cultivation system comprising:
a model generation unit configured to generate, by using machine learning, the
cultivation condition, [and the trouble occurrence situation, a model for predicting a cultivation
condition from an occurrence of a trouble or the occurrence of the trouble from the cultivation
condition] wherein the model is a computer model (Perry, para. 0077, see also figs 1 and 4, “The crop prediction system 125 receives data from the external databases 112, sensor data sources 114, and image data sources 116, and performs machine learning operations on the received data to produce one or more crop prediction models[a model generation unit configured to generate, by using machine learning, the cultivation condition wherein the model is a computer model]);18
an estimation unit configured to estimate, by using the model, [a cultivation condition for suppressing the occurrence of the trouble in the cultivation of the plant](Perry, paras. 0138-0139, see also fig. 4, “[A] crop prediction model P used by the crop prediction module 425 can be represented as: crop prediction =
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… [a]s used herein, a " field parameter" is any type of field information that describes a geographic or agricultural characteristic associated with the land, the environment, or the planting, growing, and harvesting of a crop… [t]he crop prediction module 425 applies the prediction model P to the field parameters and a set of farming operations to generate a prediction of crop production for the field parameters and the set of farming operations[an estimation unit configured to estimate].”);19, 20
and an output unit configured to output data indicating the estimated cultivation condition to a display apparatus [such that the estimated cultivation condition can be
applied to the cultivation of the plant in order to suppress the occurrence of the trouble
without requiring the application of a corresponding chemical treatment](Perry, para. 0135, “The crop prediction module 425 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… the request is received via a GUI generated by the interface module 130 and displayed on a client device of third party[and an output unit configured to output data indicating the estimated cultivation condition to a display apparatus]….”).21
wherein the model is of a Bayesian network model structure, and wherein the Bayesian network model structure has at least a layer of nodes of data of the cultivation condition [and a layer of at least one node for the trouble occurrence situation](Perry, para. 0109, “For example, the crop prediction engine 155 can apply a Bayesian network to information describing plots of land within 500 meters of a body of water and corresponding rice production[wherein the model is of a Bayesian network model structure, and has at least a layer of nodes of data of the cultivation condition] in order to train a crop prediction model that maps proximity to water to rice production.”).22
While Perry teaches an estimation unit configured to estimate, Perry does not teach:
a cultivation condition for suppressing the occurrence of the trouble in the cultivation of the plant; such that the estimated cultivation condition can be applied to the cultivation of the plant in order to suppress the occurrence of the trouble without requiring the application of a corresponding chemical treatment; and wherein the cultivation assistance system automatically estimates, one or more recommendation values for one or more cultivation conditions for suppressing the occurrence of the trouble in the cultivation of the plant
However, Weldemariam teaches:
[an estimation unit configured to estimate, by using the model], a cultivation condition for suppressing the occurrence of the trouble in the cultivation of the plant(Weldemariam, cols. 11-12, see also fig. 3B, “The productivity forecaster 324 estimates the productivity for a crop based on the crop knowledge graph 380, water uptake patterns, social characteristics, crop type, visual analytics (to, for example, detect disease and insect infestation), weather, and the like… [t]he alert and notification unit 336 issues recommendations to users based on results generated by the system 300. As described above, the recommendations may suggest that a farmer pick crops early, may warn of the detection of blight or insects infesting the crops and therefore recommend spraying a pesticide[a cultivation condition for suppressing the occurrence of the trouble in the cultivation of the plant].”).23
[and an output unit configured to output data indicating the estimated cultivation condition to a display apparatus] such that the estimated cultivation condition can be
applied to the cultivation of the plant in order to suppress the occurrence of the trouble
without requiring the application of a corresponding chemical treatment(Weldemariam, cols. 8-9, see also fig. 3A, “FIG. 3A is a graph of an example crop blueprint 390 for a tomato crop, in accordance with an example embodiment. Each column of the crop blueprint 390 corresponds to a growth stage of the crop. In one example embodiment, the growth stages are establishment, vegetative growth, flowering, fruit set, and maturity... for example, mature red tomatoes will retain a high quality for approximately four to seven days if stored at 90 to 95 percent relative humidity and at a temperature of 46° F. to 50° F. Fruit borers (insects), however, cause up to 70 percent of fruit loss. Therefore, this may indicate that, when the crop is infested with fruit borers, either the fruit should be picked on time or early to avoid the loss of the fruit. If the health or quality of the crop drops from an expected level L for the current stage of crop growth, various actions are recommended, triggered, performed, or any combination thereof by the system. Examples of actions include: recommending that the farmer pick the crops early, if possible[such that the estimated cultivation condition can be applied to the cultivation of the plant in order to suppress the occurrence of the trouble without requiring the application of a corresponding chemical treatment])24
and wherein the cultivation assistance system automatically estimates, [by using the model], one or more recommendation values for one or more cultivation conditions for suppressing the occurrence of the trouble in the cultivation of the plant(Weldemariam, col. 13-14, see also fig. 4, “The action may be triggered by a condition, such as the health or quality of the crop in relation to an expected health or quality level L for the current stage of crop growth[and wherein the cultivation assistance system automatically estimates,]… [t]he actions include… recommending the application of chemicals (such as pesticides,
herbicides, insecticides, fungicides, and the like)[ one or more recommendation values for one or more cultivation conditions for suppressing the occurrence of the trouble in the cultivation of the plant]….”).25
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Perry with the teachings of Weldemariam the motivation to do so would be to use deep learning to positively intervene during the crop growing process to produce higher crop yields(Weldemariam, col. 1, “Crop quality assessments are important for managing risks during the growing season and enabling price premiums after harvest… [i]n addition, there is an absence of uniform guidelines for farmers to use on how to grow crops. Principles of the invention provide techniques for crop grading via deep learning… [to] determin[e] a crop health status based on the one or more crop related features, an environmental context, a growth stage of the crop, and a farm cohort by using a computerized deep learning system to perform an automated growth stage analysis; and at least one of recommending, triggering, and performing one or more actions.”).
Perry in view of Weldemariam do not teach: and the trouble occurrence situation, a model for predicting a cultivation condition from an occurrence of a trouble or the occurrence of the trouble from the cultivation condition; a layer of nodes of a plurality of clusters, and a layer of at least one node for the trouble occurrence situation.
However, Lu teaches:
and the trouble occurrence situation, a model for predicting a cultivation condition from an occurrence of a trouble or the occurrence of the trouble from the cultivation condition(Lu, pgs., 18-19, see also figs., 6 and 7 and Table 2, “The representative DAG for the best-performing model under supervised (Case 2) and algorithm (Case 2) learning is shown in Figures 6 and 7[a cultivation condition from an occurrence of a trouble]. In the case of supervised learning, the DAG is a plant stage based network[a model for predicting]... DI [disease incidence][and the trouble occurrence situation] is independently affected by a set of factors including: precipitation (TP), primary infection rate(PIR), secondary infection rate (SIR), wind-speed (WS) influenced dispersal rate (DR), plant stage(PS), the degree-days based disease risk assessment model (Pmaxxacc3), latent period (LP), past DI (DIP); and genes cultivar types (Type). The network structure learned from supervised learning shows causal relationships based on existing knowledge, with dispersal rate (DR) of grape PM spores being influenced by wind-speed conditions, secondary infection (SIR), and temperature (Figure 6)[ the occurrence of the trouble from the cultivation condition].”);26
a layer of nodes of a plurality of clusters, and a layer of at least one node for the trouble occurrence situation(Lu, pgs., 18-19, see also figs., 6 and 7 and Table 2, “The representative DAG for the best-performing model under supervised (Case 2) and algorithm (Case 2) learning is shown in Figures 6 and 7. In the case of supervised learning, the DAG is a plant stage based network... DI [disease incidence][ and a layer of at least one node for the trouble occurrence situation] is independently affected by a set of factors including…primary infection rate(PIR), secondary infection rate (SIR)…dispersal rate (DR)…past DI (DIP); and genes cultivar types (Type)[ a layer of nodes of a plurality of clusters]. The network structure learned from supervised learning shows causal relationships based on existing knowledge, with dispersal rate (DR) of grape PM spores being influenced by wind-speed conditions, secondary infection (SIR), and temperature (Figure 6).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Perry in view of Weldemariam with the teachings of Lu the motivation to do so would be to construct an accurate prediction model for implementing a fungicide spray program to control plant diseases for suppression purposes(Lu, pg., 15, “A fungicide spray program was identified for maximizing spray efficiency for disease control based on the Bayesian network model and forecasting windows... [t]he program guides grape advisor or growers by providing the best times to spray fungicide for PM control in relation to disease risk (future disease incidence), local weather, and regional climate variability.”).
Regarding claim 18, Perry in view of Weldemariam and Lu teaches the cultivation assistance system according to claim 1, further comprising:
a model update unit configured to update the model by using the cultivation condition(Perry, para. 0118, see also fig. 4, “The training module 410 may periodically update
crop prediction models in response to a triggering condition[a model update unit configured to update the model by using the cultivation condition].”)
and the trouble occurrence situation(Weldemariam, col. 13, see also fig. 4, “A crop blueprint is generated or updated (operation 416)… [t]he blueprint includes, for example, computed values such as the health of the crop, the assessed growth stage of the crop, implications of the environment on the health of the crop, and the like[and the trouble occurrence situation].”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Perry with the above teachings of Weldemariam for the same rationale stated at Claim 1.
Referring to independent claim 19, it is rejected on the same basis as
independent claim 1 since they are analogous claims.
Regarding claim 20, Perry teaches a computer-readable, non-transitory recording medium having recorded thereon a program for causing a computer to function as(Perry, para. 0182, “The storage device 1008 is any non-transitory computer-readable storage medium, such as a hard drive, compact disk read-only memory (CD-ROM), DVD, or a solid-state memory device. The memory 1006 holds instructions and data used by the processor 1002.”) and for all other claim limitations of claim 20 they are rejected on the same basis as independent claim 1 since they are analogous claims.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADAM C STANDKE whose telephone number is (571)270-1806. The examiner can normally be reached Gen. M-F 9-9PM EST.
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/Adam C Standke/
Primary Examiner
Art Unit 2129
1 Examiner Remarks: After reading through Applicant’s disclosure submitted on 07/14/2022, paras. 0039-0050 of Applicant’s Specification and fig. 2 discloses the algorithm that transforms the sensors and general computer into a special purpose computer programed to perform the disclosed algorithm. See In Aristocrat Techs. Australia PTY Ltd. v. Int’l Game Tech., 521 F.3d 1328, 1336-37 (Fed. Cir. 2008). Accordingly, Examiner has not made a rejection under 35 U.S.C. 112(b) and 35 U.S.C 112(a) in regards to 35 U.S.C 112(f).
2 Examiner Remarks: According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
3 Examiner Remarks: The claim limitations that are not in bold and contained in square brackets (i.e. []) are claim limitations not taught by the prior art of Perry
4 Examiner Remarks: The claim limitations that are not in bold and contained in square brackets (i.e. []) are claim limitations not taught by the prior art of Perry
5 Examiner Remarks: The claim limitations that are not in bold and contained in square brackets (i.e. []) are claim limitations not taught by the prior art of Perry
6 Examiner Remarks: The claim limitations that are not in bold and contained in square brackets (i.e. []) are claim limitations taught by the prior art of Perry
7 According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
8 Examiner Remarks: The claim limitations that are not in bold and contained in square brackets (i.e. []) are claim limitations taught by the prior art of Perry
9 Examiner Remarks: The claim limitations that are not in bold and contained in square brackets (i.e. []) are claim limitations taught by the prior art of Perry
10 Examiner Remarks: The claim limitations that are not in bold and contained in square brackets (i.e. []) are claim limitations taught by the prior art of Perry
11 Examiner Remarks: According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
12 Examiner Remarks: According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
13 Examiner Remarks: According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
14 Examiner Remarks: According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
15 Examiner Remarks: According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
16 Examiner Remarks: According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
17 Examiner Remarks: According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
18 Examiner Remarks: The claim limitations that are not in bold and contained in square brackets (i.e. []) are claim limitations not taught by the prior art of Perry
19 Examiner Remarks: According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
20 Examiner Remarks: The claim limitations that are not in bold and contained in square brackets (i.e. []) are claim limitations not taught by the prior art of Perry
21 Examiner Remarks: The claim limitations that are not in bold and contained in square brackets (i.e. []) are claim limitations not taught by the prior art of Perry
22 Examiner Remarks: The claim limitations that are not in bold and contained in square brackets (i.e. []) are claim limitations not taught by the prior art of Perry
23 Examiner Remarks: The claim limitations that are not in bold and contained in square brackets (i.e. []) are claim limitations taught by the prior art of Perry
24 Examiner Remarks: The claim limitations that are not in bold and contained in square brackets (i.e. []) are claim limitations taught by the prior art of Perry
25 Examiner Remarks: The claim limitations that are not in bold and contained in square brackets (i.e. []) are claim limitations taught by the prior art of Perry
26 Examiner Remarks: According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
Prosecution Insights