DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . The Response to Election/Restriction filed 02/04/2026 has been received and considered. Claims 1-5, 7-19, and 24 are elected without traverse and presented for examination. Claim 21 is canceled. Claim 26 is new. Claims 1-5, 7-19, 24, and 26 are pending.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.83(a) because the features disclosed in the description and claims should be illustrated in the drawings in a form of graphical drawing symbol or a labeled representation. Element numbers drawn to empty boxes does not provide adequate labeling for Figure(s) 1-4.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because:
Reference character “111” has been used to designate both "a data interface 111" in Fig. 1 and "data processing unit 111" in Fig. 3.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following:
The specification lacks section headings: “Field of the Invention”, “Background of the Invention”, “Brief Summary of the Invention”, "Brief Description of the Several Views of the Drawing(s)", and “Detailed Description of the Invention”.
Arrangement of the Specification
As provided in 37 CFR 1.77(b), the specification of a utility application should include the following sections in order. Each of the lettered items should appear in upper case, without underlining or bold type, as a section heading. If no text follows the section heading, the phrase “Not Applicable” should follow the section heading:
(a) TITLE OF THE INVENTION.
(b) CROSS-REFERENCE TO RELATED APPLICATIONS.
(c) STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT.
(d) INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC (See 37 CFR 1.52(e)(5) and MPEP 608.05. Computer program listings (37 CFR 1.96(c)), “Sequence Listings” (37 CFR 1.821(c)), and tables having more than 50 pages of text are permitted to be submitted on compact discs.) or
REFERENCE TO A “MICROFICHE APPENDIX” (See MPEP § 608.05(a). “Microfiche Appendices” were accepted by the Office until March 1, 2001.)
(e) BACKGROUND OF THE INVENTION.
(1) Field of the Invention.
(2) Description of Related Art including information disclosed under 37 CFR 1.97 and 1.98.
(f) BRIEF SUMMARY OF THE INVENTION.
(g) BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S).
(h) DETAILED DESCRIPTION OF THE INVENTION.
Claim Interpretation
Office personnel are to give claims their "broadest reasonable interpretation" in light of the supporting disclosure. In re Morris, 127 F.3d 1048, 1054-55, 44 USPQ2d 1023, 1027-28 (Fed. Cir. 1997). Limitations appearing in the specification but not recited in the claim are not read into the claim. In re Prater, 415 F.2d 1393, 1404-05, 162 USPQ 541,550-551(CCPA 1969). See *also In re Zletz, 893 F.2d 319,321-22, 13 USPQ2d 1320, 1322(Fed. Cir. 1989) ("During patent examination the pending claims must be interpreted as broadly as their terms reasonably allow").... The reason is simply that during patent prosecution when claims can be amended, ambiguities should be recognized, scope and breadth of language explored, and clarification imposed.... An essential purpose of patent examination is to fashion claims that are precise, clear, correct, and unambiguous. Only in this way can uncertainties of claim scope be removed, as much as possible, during the administrative process.
Claims recite "and/or". The claims reciting "and/or" were interpreted as “or”.
Claim Objections
Claim 1, line(s) 9 refer to the term “the obtained observation data”, it would be better as “the obtained plant observation data” to avoid any possible antecedent issues.
Appropriate correction or clarification is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 7-11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
Claim 7 recites the limitation "the soil moisture indicator" in line(s) 2. There is insufficient antecedent basis for this limitation in the claim. There is no "soil moisture indicator" anteceding this limitation in the claim.
As to claim(s) 8-11, they are objected for the same deficiency.
Appropriate correction or clarification is required.
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 1-5, 7-19, 24, and 26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claim 1, Step 1: a method (process = 2019 PEG Step 1 = yes)
Independent claim 1, Step 2A, Prong One: claim recites:
for determining a plant protection treatment plan of an agricultural plant…
predicting (S130), by a computational model (113) executed by the data processing unit, based on input data at least comprising the obtained observation data and the obtained weather data, a time-related disease probability of the agricultural plant, and determining (S140), by the computational model (113), based on at least the predicted disease probability, at least one plant protection treatment parameter to be included into the plant protection treatment plan
These limitations are substantially drawn to mental concepts: observation, evaluation, judgment, opinion, but for the recitation of generic computer components. Information and/or data also fall within the realm of abstract ideas because information and data are intangible. See Electric Power Group1 (Electric Power hereinafter): “Information… is an intangible”.
As to the predicting limitations, predicting under its broadest reasonable interpretation, is a mental concept. Predictions are mental in nature. See for example in the Specification:
“the disease probability may be predicted in quantitative value. In other words, the computational model may predict a value that is assigned to a certain probability value or range with which the disease may occur at the plant at all or the course of the disease may have spread beyond a certain threshold. The quantitative value may be between e.g. 0 to 1, 0 to 100, etc., for indicating the disease probability”
As to the determining limitations, these limitations, as drafted and under a broadest reasonable interpretation, can be characterized as entailing a user analyzing (observations, evaluations) and deciding/determining (judgments), i.e., processing information and/or data, that can be performed in the human mind or by a human using a pen and paper. As to these limitations, the terms "determining… at least one plant protection treatment parameter" are not elaborated but merely repeated in the Application description.
If a claim limitation, under its broadest reasonable interpretation, covers mental processes, then it falls within the "(c) Mental processes" grouping of abstract ideas (2019 PEG Step 2A, Prong One: Abstract Idea Grouping? = Yes, (c) Mental processes—concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
Independent claim 1, Step 2A, Prong Two: The claim recites the additional element a data processing unit (111), which provides conventional computer implementation.
As to the limitations “obtaining (S110), by the data processing unit, plant observation data indicative for a current state of health of the agricultural plant or of a reference plant” and "obtaining (S120), by the data processing unit, weather data associated with a location at which the agricultural plant is cultivated", these limitations describe the concept of “mere data gathering”, which corresponds to the concepts identified as abstract ideas by the courts. Data gathering, including when limited to particular content does not change its character as information, is also within the realm of abstract ideas. Data gathering has not been held by the courts to be enough to qualify as “significantly more”. See Electric Power. See also MPEP § 2106.05(g).
This judicial exception is not integrated into a practical application (2019 PEG Step 2A, Prong Two: Additional elements that integrate the Judicial exception/Abstract idea into a practical application? = NO).
Independent claim 1, Step 2B: As discussed with respect to Step 2A, Prong two, the claim recites the additional element a data processing unit (111) at a high level of generality and as performing generic computer functions routinely used in computer applications. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. The use of a computer to implement the abstract idea of a mathematical algorithm has not been held by the courts to be enough to qualify as “significantly more”. The conventional computer implementation is described in the specification (underline emphasis added):
"The system 100 comprises a first device 110 adapted for determining a plant protection treatment plan of the agricultural plant, as will be described in more detail below. The first device 110 may be a suitable type of computer… the first device 110 may form or may be part of a computing cloud, a server, or the like. In other embodiments, the first device 110 may be a local computer device" (see page 17, lines 16-18).
As discussed with respect to Step 2A, Prong two, claim 1 recites data gathering, these limitations are recited at a high level of generality; and therefore, remain insignificant extra-solution activity even upon reconsideration.
Thus, taken alone the individual additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the additional elements taken individually. There is no indication that their combination improves the functioning of a computer itself or improves any other technology (underline emphasis added). Therefore, the claim does not amount to significantly more than the abstract idea itself (2019 PEG Step 2B: NO).
Independent claim 24, Step 1: a system (system = 2019 PEG Step 1 = yes)
Independent claim 24, Step 2A, Prong One: claim recites:
predict, by use of a computational model (113) executed by the first data processing unit (111), based on
These limitations are substantially drawn to mental concepts but for the recitation of generic computer components. (See Independent claim 1, Step 2A, Prong One above). If a claim limitation, under its broadest reasonable interpretation, covers mental processes, then it falls within the "(c) Mental processes" grouping of abstract ideas (2019 PEG Step 2A, Prong One: Abstract Idea Grouping? = Yes, (c) Mental processes—concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
Independent claim 24, Step 2A, Prong Two: As to the limitations “for treating an agricultural plant based on a plant protection treatment plan assigned to the agricultural plant", they are no more than intended use.
The claim recites the additional elements a system, a data processing unit (111), and a second data processing unit (121), which provide conventional computer implementation.
As to the limitations “obtained observation data and obtained weather data” and "obtain the output data from the first data processing unit (111)", these limitations describe the concept of “mere data gathering”, which corresponds to the concepts identified as abstract ideas by the courts. Data gathering, including when limited to particular content does not change its character as information, is also within the realm of abstract ideas. Data gathering has not been held by the courts to be enough to qualify as “significantly more”. (See Independent claim 1, Step 2A, Prong Two above).
As to the limitations “providing output data at least comprising the at least one plant protection treatment parameter", they are insignificant extra-solution activity – data outputting.
As to the limitations "process the obtained output data to use the at least one plant protection treatment parameter", they represent no more than just “apply it” limitations, because transformation of information and/or data is not statutory. They invoke computers or other machinery merely as a tool to perform an existing process.
This judicial exception is not integrated into a practical application (2019 PEG Step 2A, Prong Two: Additional elements that integrate the Judicial exception/Abstract idea into a practical application? = NO).
Independent claim 24, Step 2B: As discussed with respect to Step 2A, Prong two, the intended use limitations remain intended use even upon reconsideration, because no actual treating of an agricultural plant is ever performed in the body of the claim.
As discussed with respect to Step 2A, Prong two, the claim recites the additional elements a system, a data processing unit (111), and a second data processing unit (121), at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. (See Independent claim 1, Step 2B above).
As discussed with respect to Step 2A, Prong two, claim 24 recites data gathering, these limitations are recited at a high level of generality; and therefore, remain insignificant extra-solution activity even upon reconsideration.
As discussed with respect to Step 2A, Prong two, the limitations identified as data outputting are insignificant extra-solution activity. See MPEP 2106.05(g)(3). As to these limitations, they are not elaborated but merely repeated in the Application description.
As discussed with respect to Step 2A, Prong two, the limitations identified as just “apply it” because transformation of information or data is not statutory, information and/or data also fall within the realm of abstract ideas because information and data are intangible. See Electric Power.
Thus, taken alone the individual additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the additional elements taken individually. There is no indication that their combination improves the functioning of a computer itself or improves any other technology (underline emphasis added). Therefore, the claim does not amount to significantly more than the abstract idea itself (2019 PEG Step 2B: NO).
Dependent claims, Step 2A, Prong One: The claim limitations further the mental concepts of their independent claims. (See Independent claim 1, Step 2A, Prong One above). If a claim limitation, under its broadest reasonable interpretation, covers mental processes, then it falls within the "(c) Mental processes" grouping of abstract ideas (2019 PEG Step 2A, Prong One: Abstract Idea Grouping? = Yes, (c) Mental processes—concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
Dependent claims, Step 2A Prong two:
As to the limitations “12… obtaining a biomass indicator associated with the location at which the agricultural plant is cultivated, wherein the biomass indicator is additionally provided to the computational model (113) as additional input data for predicting the disease probability”, "14… wherein the plant observation data is obtained and/or processed”, and "26… obtaining, by the data processing unit (111), scouting information and/or user feedback collected during a cultivation season of the agricultural plant", these limitations describe the concept of “mere data gathering”, which corresponds to the concepts identified as abstract ideas by the courts. (See Independent claim 1, Step 2A, Prong Two above).
As to the limitations "18… wherein the computational model (113) utilizes a neural network adapted to output data in response to the input plant observation data and weather data" and "26… wherein the computational model (113) is adapted to changed conditions of cultivation of the agricultural plant for determining the plant protection treatment plan, wherein the adapting comprises… calibrating an output of the computational model (113) during the cultivation season based on the obtained scouting information and/or user feedback, wherein the calibrating excludes adjusting, by using backpropagation, parameters or weights of the computational model (113)", they represent no more than just “apply it” limitations, because they recite only the idea of a solution or outcome, i.e., they fail to recite details of how a solution to a problem is accomplished.
As to the limitations “19… wherein the at least one plant protection treatment parameter and/or the plant protection treatment plan is provided as a computer-readable dataset adapted to be executed by a data processing device of a robotic device to apply a plant protection agent at a specific date or time", they are insignificant extra-solution activity – data outputting.
This judicial exception is not integrated into a practical application of the exception (2019 PEG Step 2A, Prong Two: Additional elements that integrate the Judicial exception/Abstract idea into a practical application? = NO).
Dependent claims Step 2B:
As discussed with respect to Step 2A, Prong two, the data gathering limitations are recited at a high level of generality; and therefore, remain insignificant extra-solution activity even upon reconsideration.
As discussed with respect to Step 2A, Prong two, limitations reciting only the idea of a solution or outcome are just “apply it” limitations, because they fail to recite details of how a solution to a problem is accomplished. See MPEP 2106.05(f)(1). As to the limitations "18… wherein the computational model (113) utilizes a neural network adapted to output data in response to the input plant observation data and weather data", the specification reads:
'the computational model may comprise or may be formed as a recurrent neural network (RNN), where connections between nodes form a directed graph along a temporal sequence. This can further improve the learning and/or prediction abilities of the computational model.
Alternatively or additionally, the computational model may comprise or may be formed as an Long short-term memory (LSTM) architecture, which is an artificial recurrent neural network (RNN) architecture'
As to the limitations "26… calibrating an output of the computational model (113) during the cultivation season based on the obtained scouting information and/or user feedback, wherein the calibrating excludes adjusting, by using backpropagation, parameters or weights of the computational model (113)", the specification merely reads:
'the obtained scouting information and/or user feedback may be used to adapt and/or calibrate the computational model during cultivation season of the plant. This can further improve the learning and/or prediction abilities of the computational model'
As discussed with respect to Step 2A, Prong two, the limitations identified as data outputting are insignificant extra-solution activity. See MPEP 2106.05(g)(3).
Therefore, the claims do not amount to significantly more than the abstract idea itself (2019 PEG Step 2B: NO).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) 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.
Examiner would like to point out that any reference to specific figures, columns and lines should not be considered limiting in any way, the entire reference is considered to provide disclosure relating to the claimed invention.
Claims 1-5, 7-19, 24, and 26 are rejected under 35 U.S.C. 103(a) as being unpatentable over Lori J. Wiles, (Wiles hereinafter), U.S. Patent 9563852, taken in view of James Ethington, (Ethington hereinafter), U.S. Pre–Grant publication 20160078375 (see IDS dated 09/08/2022).
As to claim 1, Wiles discloses a method for determining a plant protection treatment (see "apply the risk assessment profile 145 to identify suitable windows of opportunity for… applying treatments, to avoid or mitigate damage from infestation" in col. 15, lines 10-14), the method carried out by a data processing unit (111) (see "input data 110 is applied to… data processing modules 132 within a computing environment 130 that also includes… processors 134 and… software and hardware components… to execute program instructions or routines to perform the functions of the crowd-sourced pest and disease model 100" in col. 9, lines 57-65), and the method comprising the steps of: obtaining (S110), by the data processing unit, plant observation data indicative for a current state of health of the agricultural plant or of a reference plant (see "Crowd-sourced observations in pest presence data 111 may take many forms, and may be reported in multiple ways. For example, users may input indications or reports of a pest presence via an application or other electronic interface" in col. 5, lines 46-50), obtaining (S120), by the data processing unit, weather data associated with a location at which the agricultural plant is cultivated (see "Input data 110 also comprises meteorological and climatological data 114, which at least includes recent and current field-level weather data and short-term weather forecast data for both reporting fields 104 and targeted fields 106" in col. 7, lines 13-16), predicting (S130), by a computational model (113) (see "a crowd-sourced pest and disease model 100 for predicting a presence of, and profiling a risk assessment for, a pest or disease 102 in a particular, or targeted field 106" in col. 4, lines 42-45) executed by the data processing unit, based on input data at least comprising the obtained observation data and the obtained weather data, a time-related disease probability of the agricultural plant (see "assigning weighted vectors of the field variables, where weights denote the importance of each variable identified in the pest-environment relationship. The unsupervised learning engine 144 models these weighted vectors of field variables by performing statistical analyses 146 and other mathematical processes 147 to estimate a probability that targeted fields 106 will be infested. The unsupervised learning engine 144 generates a risk assessment profile 145 based on this estimate. The profile 145 is applied to generate the pest occurrence prediction 122 as output data 120… As additional crowd-sourced observations containing pest presence data 111 are ingested for fields 104, the unsupervised learning engine 144 refines its risk assessment profile 145 by continually modeling the weighted vectors of field variables that aid in identifying predictors 160 from descriptors 161… performing a customized modeling approach for assessing a field-specific risk, and for predicting a pest infestation or presence in a field 106, for a particular time period" in col. 11, line 59 to col. 12, line 24), and determining (S140), by the computational model (113), based on at least the predicted disease probability, at least one plant protection treatment parameter to be included into the plant protection treatment (see "risk assessment profile 145… to generate specific predictions 122 of pest infestation in targeted fields 106, and one or both of these may be further used to suggest, recommend, or generate one or more management actions, either before or after infestation, to address a pest infestation and/or mitigate the impact" in col. 15, line 66 to col. 16, line 4; "apply the risk assessment profile 145 to identify suitable windows of opportunity for… applying treatments, to avoid or mitigate damage from infestation" in col. 15, lines 10-14).
Wiles does not disclose, but Ethington discloses (see “[0082]… pest advisor… to recommend… treatment methods to respond to such pest and disease risks”).
Wiles and Ethington are analogous art because they are related to agricultural modeling.
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Ethington with Wiles, because Ethington points out that his "[0192]… field health advisor module 424 receives and processes field image data to determine, identify, and provide index values of biomass health", and as a result, Ethington reports that "[0192] Agricultural intelligence computer system 150 is also configured to provide information regarding the health and quality of areas of fields 120… Field health advisor module 424 identifies crop health quality over the course of the season and uses such crop health determinations to recommend scouting or investigation in areas of poor field health".
As to claim 2, Wiles discloses wherein the input data further comprises a soil moisture indicator, obtained by the data processing unit and associated with the location at which the agricultural plant is cultivated (see "Input data 110 also includes field data 112 for both reporting fields 104 and targeted fields 106. Field data 112 includes various field characteristics, such as field, soil and/or crop-related management actions taken… Field data 112 may further include water-related information such as field drainage characteristics, groundwater, watershed and aquifer data, and information on prior and recent irrigation practice" in col. 6, lines 15-29).
As to claim 3, Wiles discloses wherein the at least one plant protection treatment parameter comprises a treatment period or a treatment time (see "risk assessment profile 145… to generate specific predictions 122 of pest infestation in targeted fields 106, and one or both of these may be further used to suggest, recommend, or generate one or more management actions, either before or after infestation, to address a pest infestation and/or mitigate the impact. Additionally, many different users and uses of this output data 120 are possible. Output data 120 may therefore be used to perform several functions, either directly or through other systems, hardware, software, devices, services (such as the advisory services 150 described below), and application programming interfaces 170. Examples of management actions include notifications to begin scouting targeted fields 106 to confirm presence of a pest 102" in col. 15, line 66 to col. 16, line 13; "apply the risk assessment profile 145 to identify suitable windows of opportunity for… applying treatments, to avoid or mitigate damage from infestation" in col. 15, lines 10-14).
As to claim 4, Wiles discloses wherein the at least one plant protection treatment parameter comprises a date or time window when the controllability of the disease with certain plant protection measures is above a minimum threshold (see "risk assessment profile 145… to generate specific predictions 122 of pest infestation in targeted fields 106, and one or both of these may be further used to suggest, recommend, or generate one or more management actions, either before or after infestation, to address a pest infestation and/or mitigate the impact. Additionally, many different users and uses of this output data 120 are possible. Output data 120 may therefore be used to perform several functions, either directly or through other systems, hardware, software, devices, services (such as the advisory services 150 described below), and application programming interfaces 170. Examples of management actions include notifications to begin scouting targeted fields 106 to confirm presence of a pest 102, and/or to confirm that the density or extent warrants a control action" in col. 15, line 66 to col. 16, line 14).
As to claim 5, Wiles discloses wherein the location at which the agricultural plant is cultivated is a field, the field is divided into a number of sub-fields (see "Input data 110 also includes field data 112 for both reporting fields 104 and targeted fields 106… Field characteristics may also include landscape information such as an identification of vegetation in areas adjacent to a planted crop, and soils information for the reporting fields 104 and targeted fields 106, as well as cropping history and pest presence in the fields and surrounding landscape in previous seasons. Field characteristics may further include information regarding land adjacent to reporting and/or targeted fields" in col. 6, lines 15-48), and wherein the disease probability is predicted for at least a part of the number of sub-fields in a sub-field specific manner (see "a crowd-sourced pest and disease model 100 for predicting a presence of, and profiling a risk assessment for, a pest or disease 102 in a particular, or targeted field 106" in col. 4, lines 42-45), and wherein the at least one plant protection treatment parameter is determined in a sub-field specific manner (see "risk assessment profile 145… to generate specific predictions 122 of pest infestation in targeted fields 106, and one or both of these may be further used to suggest, recommend, or generate one or more management actions, either before or after infestation, to address a pest infestation and/or mitigate the impact. Additionally, many different users and uses of this output data 120 are possible. Output data 120 may therefore be used to perform several functions, either directly or through other systems, hardware, software, devices, services (such as the advisory services 150 described below), and application programming interfaces 170. Examples of management actions include… Notifications may also be provided directly to farm equipment operating in a targeted field 106. For example, a notification may be provided directly to tillage equipment to adjust or stop tilling of a targeted field 106, or to irrigation equipment operating in a targeted field 106 to adjust a timing or type of artificial precipitation used, or a direction of application" in col. 15, line 66 to col. 16, line 29).
As to claim 7, Wiles discloses wherein the soil moisture indicator comprises a soil moisture value associated with one or more soil depths (see "Input data 110 also includes field data 112 for both reporting fields 104 and targeted fields 106. Field data 112 includes various field characteristics, such as field, soil and/or crop-related management actions taken… Field data 112 may further include water-related information such as field drainage characteristics, groundwater, watershed and aquifer data, and information on prior and recent irrigation practice… Field characteristics may also include… soils information for the reporting fields 104 and targeted fields 106" in col. 6, lines 15-38).
As to claim 8, Wiles discloses wherein the soil moisture indicator comprises a soil type (see "Input data 110 also includes field data 112 for both reporting fields 104 and targeted fields 106. Field data 112 includes various field characteristics, such as field, soil and/or crop-related management actions taken… Field data 112 may further include water-related information such as field drainage characteristics, groundwater, watershed and aquifer data, and information on prior and recent irrigation practice… Field characteristics may also include… soils information for the reporting fields 104 and targeted fields 106" in col. 6, lines 15-38).
As to claim 9, Wiles discloses wherein the soil moisture indicator is modelled based on at least a soil type and the weather data (see "Soil data 117 may be imported from one or more external database collections, such as for example the USDA NRCS Soil Survey Geographic (SSURGO) dataset containing background soil information as collected by the National Cooperative Soil Survey, or from one or more models configured to profile soil structure and composition" in col. 8, lines 36-41).
As to claim 10, Wiles discloses wherein the soil moisture indicator is at least partly derived from a remote measurement performed to the location at which the agricultural plant is cultivated (see "Input data 110 also includes field data 112 for both reporting fields 104 and targeted fields 106. Field data 112 includes various field characteristics, such as field, soil and/or crop-related management actions taken… Field data 112 may further include water-related information such as field drainage characteristics, groundwater, watershed and aquifer data, and information on prior and recent irrigation practice… Field characteristics may also include… soils information for the reporting fields 104 and targeted fields 106… Input data 110 may also include GPS data 113 that enables the crowd-sourced pest and disease model 100 to correlate reporting fields 104 and targeted fields 106. Such GPS data 113 enables GPS receivers to determine positional coordinates and/or boundaries of both reporting fields 104 and targeted fields 106 and their proximity to each other. This allows the crowd-sourced pest and disease model 100 to determine a geographical correlation for profiling the risk assessment 145 and prediction of pest occurrence 122 in targeted fields 106, based on the reported pest presence data 111 in reporting fields 104" in col. 6, lines 15-59).
As to claim 11, Wiles discloses wherein the soil moisture indicator is at least partly derived from a local measurement performed at the location at which the agricultural plant is cultivated (see "Input data 110 also includes field data 112 for both reporting fields 104 and targeted fields 106. Field data 112 includes various field characteristics, such as field, soil and/or crop-related management actions taken… Field data 112 may further include water-related information such as field drainage characteristics, groundwater, watershed and aquifer data, and information on prior and recent irrigation practice… Field characteristics may also include… soils information for the reporting fields 104 and targeted fields 106… Input data 110 may also include GPS data 113 that enables the crowd-sourced pest and disease model 100 to correlate reporting fields 104 and targeted fields 106. Such GPS data 113 enables GPS receivers to determine positional coordinates and/or boundaries of both reporting fields 104 and targeted fields 106 and their proximity to each other. This allows the crowd-sourced pest and disease model 100 to determine a geographical correlation for profiling the risk assessment 145 and prediction of pest occurrence 122 in targeted fields 106, based on the reported pest presence data 111 in reporting fields 104" in col. 6, lines 15-59).
As to claim 12, Ethington discloses obtaining a biomass indicator associated with the location at which the agricultural plant is cultivated (see "[0192]… field health advisor module 424 receives and processes field image data to determine, identify, and provide index values of biomass health"), wherein the biomass indicator is additionally provided to the computational model (113) as additional input data for predicting the disease probability (see "[0031]… agricultural intelligence computer… provides a user with… agricultural intelligence services for the land tract or field region identified by the field definition data. Such agricultural intelligence services may be used to recommend courses of action for the user to undertake… recommendation services include… a pest advisor, a field health advisor").
As to claim 13, Wiles discloses wherein predicting the disease probability of the agricultural plant further comprises: predicting, by the computational model (113), a disease progression window in which a probable course of disease of the agricultural plant over a period of time is computed and being indicative for the disease probability to a specific time within the disease progression window, wherein the predicted disease probability is extracted from the disease progression window (see "assigning weighted vectors of the field variables, where weights denote the importance of each variable identified in the pest-environment relationship. The unsupervised learning engine 144 models these weighted vectors of field variables by performing statistical analyses 146 and other mathematical processes 147 to estimate a probability that targeted fields 106 will be infested. The unsupervised learning engine 144 generates a risk assessment profile 145 based on this estimate. The profile 145 is applied to generate the pest occurrence prediction 122 as output data 120… As additional crowd-sourced observations containing pest presence data 111 are ingested for fields 104, the unsupervised learning engine 144 refines its risk assessment profile 145 by continually modeling the weighted vectors of field variables that aid in identifying predictors 160 from descriptors 161… performing a customized modeling approach for assessing a field-specific risk, and for predicting a pest infestation or presence in a field 106, for a particular time period" in col. 11, line 59 to col. 12, line 24).
As to claim 14, Wiles discloses wherein the plant observation data is obtained and/or processed leaf-layer-specific (see "Input data 110 may further include crop and planting data 116, such as crop type, seed type, planting data… crop canopy and soil conditions over time, relative maturity… crop temperature, crop moisture, seed moisture, plant depth" in col. 8, lines 7-22).
As to claim 15, Wiles discloses wherein the plant observation data is weighted for or classified into different leaf layers of the agricultural plant or the reference plant, based on the different leaf layer's effect to the yield of the agricultural plant, and wherein the disease probability is predicted based on the weighted or classified plant observation data (see "Input data 110 may further include crop and planting data 116, such as crop type, seed type, planting data… crop canopy and soil conditions over time, relative maturity… crop temperature, crop moisture, seed moisture, plant depth… and any other crop and plant information that may be used to profile the risk assessment 145, and the pest occurrence predictions 122 forming the output data 120" in col. 8, lines 7-22).
As to claim 16, Wiles discloses wherein the plant observation data comprises one or more of: field data, observed infestation data (see "Crowd-sourced observations in pest presence data 111 may take many forms, and may be reported in multiple ways. For example, users may input indications or reports of a pest presence via an application or other electronic interface" in col. 5, lines 46-50).
As to claim 17, Wiles discloses wherein the predicted disease probability indicates or comprises one or more of a disease severity (see "provide a crop and soil conditions advisory 151 regarding targeted fields 106… Such an advisory service 151 may model possible crop and soil damage from infestation of a pest 102 and provide analytics of such damage" in col. 16, lines 38-43), .
As to claim 18, Wiles discloses wherein the computational model (113) utilizes a neural network adapted to output data in response to the input plant observation data and weather data (see "unsupervised machine learning in implementing each of these steps… example of unsupervised machine learning to perform these steps uses clustering. Suitable clustering methods include… neural network clustering" in col. 11, lines 7-36).
As to claim 19, Wiles discloses wherein the at least one plant protection treatment parameter and/or the plant protection treatment (see "risk assessment profile 145… to generate specific predictions 122 of pest infestation in targeted fields 106, and one or both of these may be further used to suggest, recommend, or generate one or more management actions, either before or after infestation, to address a pest infestation and/or mitigate the impact. Additionally, many different users and uses of this output data 120 are possible. Output data 120 may therefore be used to perform several functions, either directly or through other systems, hardware, software, devices, services (such as the advisory services 150 described below), and application programming interfaces 170. Examples of management actions include… Notifications may also be provided directly to farm equipment operating in a targeted field 106. For example, a notification may be provided directly to tillage equipment to adjust or stop tilling of a targeted field 106, or to irrigation equipment operating in a targeted field 106 to adjust a timing or type of artificial precipitation used, or a direction of application" in col. 15, line 66 to col. 16, line 29).
Ethington discloses (see “[0082]… pest advisor… to recommend… treatment methods to respond to such pest and disease risks”).
As to claim 24, Wiles discloses a system for treating an agricultural plant based on a plant protection treatment (see "apply the risk assessment profile 145 to identify suitable windows of opportunity for… applying treatments, to avoid or mitigate damage from infestation" in col. 15, lines 10-14), comprising: a first data processing unit (111) (see "input data 110 is applied to… data processing modules 132 within a computing environment 130 that also includes… processors 134 and… software and hardware components… to execute program instructions or routines to perform the functions of the crowd-sourced pest and disease model 100" in col. 9, lines 57-65), adapted to: predict, by use of a computational model (113) (see "a crowd-sourced pest and disease model 100 for predicting a presence of, and profiling a risk assessment for, a pest or disease 102 in a particular, or targeted field 106" in col. 4, lines 42-45) executed by the first data processing unit (111), based on obtained observation data (see "assigning weighted vectors of the field variables, where weights denote the importance of each variable identified in the pest-environment relationship. The unsupervised learning engine 144 models these weighted vectors of field variables by performing statistical analyses 146 and other mathematical processes 147 to estimate a probability that targeted fields 106 will be infested. The unsupervised learning engine 144 generates a risk assessment profile 145 based on this estimate. The profile 145 is applied to generate the pest occurrence prediction 122 as output data 120… As additional crowd-sourced observations containing pest presence data 111 are ingested for fields 104, the unsupervised learning engine 144 refines its risk assessment profile 145 by continually modeling the weighted vectors of field variables that aid in identifying predictors 160 from descriptors 161… performing a customized modeling approach for assessing a field-specific risk, and for predicting a pest infestation or presence in a field 106, for a particular time period" in col. 11, line 59 to col. 12, line 24) and obtained weather data (see "Input data 110 also comprises meteorological and climatological data 114, which at least includes recent and current field-level weather data and short-term weather forecast data for both reporting fields 104 and targeted fields 106" in col. 7, lines 13-16), a time-related disease probability of the agricultural plant, and determine, by use of the computational model (113), based on at least the predicted disease probability, at least one plant protection treatment parameter to be included in the plant protection treatment (see "apply the risk assessment profile 145 to identify suitable windows of opportunity for… applying treatments, to avoid or mitigate damage from infestation" in col. 15, lines 10-14), and providing output data at least comprising the at least one plant protection treatment parameter (see "risk assessment profile 145… to generate specific predictions 122 of pest infestation in targeted fields 106, and one or both of these may be further used to suggest, recommend, or generate one or more management actions, either before or after infestation, to address a pest infestation and/or mitigate the impact" in col. 15, line 66 to col. 16, line 4), and a second data processing unit (121), adapted to: obtain the output data from the first data processing unit (111), and process the obtained output data to use the at least one plant protection treatment parameter (see "risk assessment profile 145… to generate specific predictions 122 of pest infestation in targeted fields 106, and one or both of these may be further used to suggest, recommend, or generate one or more management actions, either before or after infestation, to address a pest infestation and/or mitigate the impact. Additionally, many different users and uses of this output data 120 are possible. Output data 120 may therefore be used to perform several functions, either directly or through other systems, hardware, software, devices, services (such as the advisory services 150 described below), and application programming interfaces 170" in col. 15, line 66 to col. 16, line 10).
Wiles does not disclose, but Ethington discloses (see “[0082]… pest advisor… to recommend… treatment methods to respond to such pest and disease risks”).
As to claim 26, Wiles discloses wherein the computational model (113) is adapted to changed conditions of cultivation of the agricultural plant for determining the plant protection treatment (see "a crowd-sourced pest and disease model 100 for predicting a presence of, and profiling a risk assessment for, a pest or disease 102 in a particular, or targeted field 106" in col. 4, lines 42-45), wherein the adapting comprises: obtaining, by the data processing unit (111), (see "Crowd-sourced observations in pest presence data 111 may take many forms, and may be reported in multiple ways. For example, users may input indications or reports of a pest presence via an application or other electronic interface" in col. 5, lines 46-50); calibrating an output of the computational model (113) during the cultivation season based on the obtained scouting information and/or user feedback, wherein the calibrating excludes adjusting, by using backpropagation, parameters or weights of the computational model (113), and wherein predicting (S130) the time-related disease probability is performed using the computational model (113) having the calibrated output (see "assigning weighted vectors of the field variables, where weights denote the importance of each variable identified in the pest-environment relationship. The unsupervised learning engine 144 models these weighted vectors of field variables by performing statistical analyses 146 and other mathematical processes 147 to estimate a probability that targeted fields 106 will be infested. The unsupervised learning engine 144 generates a risk assessment profile 145 based on this estimate. The profile 145 is applied to generate the pest occurrence prediction 122 as output data 120… As additional crowd-sourced observations containing pest presence data 111 are ingested for fields 104, the unsupervised learning engine 144 refines its risk assessment profile 145 by continually modeling the weighted vectors of field variables that aid in identifying predictors 160 from descriptors 161… The model 100 estimates the probability that targeted fields 106 will become infested by calculating a similarity of each un-infested, targeted field 106 to a reporting, or infested, field 104. As noted above, these calculations can be accomplished in several different ways, and using several different methods (or, an ensemble of different methods)" in col. 11, line 59 to col. 12, line 30), and wherein determining (S140) the at least one plant protection treatment parameter is performed based on at least the predicted time-related disease probability (see "apply the risk assessment profile 145 to identify suitable windows of opportunity for… applying treatments, to avoid or mitigate damage from infestation" in col. 15, lines 10-14).
Ethington discloses (see “[0082]… pest advisor… to recommend… treatment methods to respond to such pest and disease risks”).
Conclusion
Examiner would like to point out that any reference to specific figures, columns and lines should not be considered limiting in any way, the entire reference is considered to provide disclosure relating to the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUAN CARLOS OCHOA whose telephone number is (571)272-2625. The examiner can normally be reached Mondays, Tuesdays, Thursdays, and Fridays 9:30AM - 7:00 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Renee Chavez can be reached at 571-270-1104. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JUAN C OCHOA/Primary Examiner, Art Unit 2186
1 Electric Power Group, LLC v. Alstom S.A., 119 USPQ2d 1739 Fed. Cir. 2016