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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/11/2021 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
This Final Rejection is filed in response to Applicant Arguments/Remarks Made in an Amendment filed 01/27/2025.
Claims 1-20 remain pending.
Response to Arguments
Argument 1, Applicant argues in Applicant Arguments/Remarks Made in an Amendment filed 01/27/2025 pg. 2-3, that the application overcomes the 35 U.S.C. § 101 rejection because, “The claimed invention integrates the abstract idea into a practical application by providing a specific method for forecasting in-situ environmental conditions using nonlinear artificial neural networks-based models. This method involves several concrete steps that are implemented using a combination of hardware and software components, which together provide a technical solution to a real-world problem”.
Response to Argument 1, the examiner respectfully disagrees.
Regarding the specific data handling and analysis, these limitations represent steps of mere data gathering in which a human may read data from different in-situ devices and other environmental conditions. The receiving of such data from such devices and processing using processing devices merely represent using otherwise generic computers as a tool to perform the abstract idea and do not integrate the abstract idea into practical application.
Regarding advanced machine learning techniques, the cited techniques fall under algorithmic mathematical concepts and thus themselves encompass abstract ideas and therefore do not integrate the recited judicial exception into a practical application. Validation of the model using real-time in-situ data are elements related to mere data gathering and thus are insignificant extra-solution activity.
Regarding continuous improvement and adaptation, the inclusion of updating a model based on validation results does not place any limits on how the validating and updating may occur and given the broadest reasonable interpretation in light of the background, includes abstract ideas such as having a human mentally validate a model using written down observations of current environmental data. The transmission of a model is nothing more than mere instructions to implement an abstract idea on a generic computer as well as insignificant mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity.
Regarding integrations with user devices, the addition of user devices amounts to no more than mere instructions to apply the exception using a generic computer.
Regarding technical improvements, the limitations regarding machine learning forecasting techniques encompass abstract ideas such human evaluation of mathematical concepts, where the addition of machine learning is merely a generic instruction to “apply” the exception or to a mere indication of the field of use or technological environment in which the abstract idea is performed. While the applicant’s specification states that “desktop interfaces that allow the customer to ingest relevant in-situ data that is used in turn to improve the forecasts,” there is no improvement to the functioning of a computer nor to any other technology. At best, the claimed combination amounts to an improvement to the abstract idea of human evaluation of mathematical forecasts with the mere data gathering of in-situ environmental data, rather than to any technology. Thus, even when considering the elements in combination, the claim as a whole does not integrate the recited exception into a practical application.
Argument 2, Applicant argues in Applicant Arguments/Remarks Made in an Amendment filed 01/27/2025 pg. 3-4, that “The combination of Mewes and Hathi is not obvious because Mewes focuses on linear models, and there is no suggestion or motivation in Mewes to incorporate nonlinear models as taught by Hathi”, that “Zhou's teachings are not directly applicable to the context of in-situ environmental forecasting models. The combination of references does not provide a clear and convincing rationale for why one skilled in the art would combine these teachings to achieve the claimed invention ”.
Response to Argument 2, the examiner respectfully disagrees. In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, Mewes, Hathi, and Zhou are all in a similar field of endeavor of environmental forecast prediction. Mewes sets the groundwork a facilitating forecasting of in-situ environmental conditions using artificial neural networks-based models by using current local environmental condition data as ingested input data for a forecast modeling paradigm. Hathi supplements the forecasting methods of Mewes by providing a nonlinear time series models for artificial neural networks, which may utilize multi layer perceptron. One would have been motivated to combine Hathi with Mewes, and would have had a reasonable expectation of success, as the combination provides a more accurate representation of real-world data taken from in-situ input data that may be not linear in nature. Hathi even cites in para. [0024], that use of such a technique may reduce or eliminate overfitting, may increase a robustness of the machine learning model to noise, and/or the like. Zhou further elaborates on forecasting models, specifically in transmitting and storing such machine learning models for updates and retraining. One would have been motivated to combine transmission and storage of forecasting machine learning models of Zhou with the creation of non-linear forecasting models of Mewes-Hathi, and would have had a reasonable expectation of success, as the combination provides users with greater accessibility to view forecast models on less resource intensive devices.
Argument 3, Applicant argues in Applicant Arguments/Remarks Made in an Amendment filed 01/27/2025 pg. 4 that “There is no reference cited for Romano, and Romano can therefore not be a basis for rejecting the present application
Response to Argument 3, the examiner acknowledges a typographical error and has corrected the cited art to Hathi, to be consistent with the rest of the motivation to combine Hathi and Mewes.
Argument 4, Applicant argues in Applicant Arguments/Remarks Made in an Amendment filed 01/27/2025 pg. 4-6 regarding the claims 2-3, 6, 8-10, & 16 that the prior art Mewes, Hathi, Zhou, and Bose combinations that, “lacks a clear and convincing rationale for why one skilled in the art would combine these teachings to achieve the claimed invention” and that “Specifically, Mewes, Hathi, and Zhou focus on the analysis and integration of data, but do not suggest the specific preprocessing steps, such as data cleaning, as claimed”.
Response to Argument 4, The examiner respectfully disagrees for at least the reasons stated above. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Argument 5, Applicant argues in Applicant Arguments/Remarks Made in an Amendment filed 01/27/2025 pg. 4, regarding Claim 3 that, “Mewes does not explicitly disclose the receipt of in-situ environmental condition indications from user devices”.
Response to Argument 5, the examiner respectfully disagrees. The examiner notes that Mewes teaches in para. [0027], “The input data 110 includes meteorological and climatological data 111 which is comprised of one or more of in-situ weather data 112”. Thus the BRI for the limitation “receiving”, encompasses how the in-situ weather data is received as input data and used to diagnose, predict and forecast expected weather conditions.
Argument 6, Applicant argues in Applicant Arguments/Remarks Made in an Amendment filed 01/27/2025 pg. 5, regarding Claim 8 that, “Mewes does not explicitly disclose the specific process of incorporating these data types as claimed”, “Mewes does not explicitly disclose the specific process of generating updated input data in the context of in-situ environmental forecasting models”, and “Mewes does not explicitly disclose these steps in the context of the claimed invention”.
Response to Argument 6, The examiner respectfully disagrees. Mewes teaches in para. [0096-0098], that “additional datasets, whether generated internally, user-provided, instrument-derived, or otherwise obtained … such as elevation data … as they pertain to …network flow analyses, and more, may noticeably or significantly improve the accuracy, resolution, availability of variables, or quality of the analyses performed on the data pertaining to a field, region, …or area bounds (field, farm, township, parish, county, state, country, etc.)… they also include training and applying AI-based systems to translate weather … condition data … into … workability metrics based on past and current data collected from on-board data collection systems ”. The examiner notes additional datasets may be received from a variety of sources such as internal, external instrument-derived, or any other method. The examiner also notes that the BRI for “current weather forecast model data associated with the weather forecast model, one or more current environmental data associated with the one or more of the one or more local environmental conditions, the one or more regional environmental conditions, and the one or more global environmental conditions, and one or more current in-situ environmental data associated with the one or more in-situ environmental conditions”, encompasses a current weather paradigm model may be augmented by additional datasets including local area bound environmental conditions, such as in a farm location, additional datasets including regional environmental conditions such as in a township, additional datasets including global environmental conditions such as in a country, and that additional datasets include current in-situ environmental condition data that may be current instrument derived data from a field. The examiner notes that the BRI for the limitation, “incorporating, using the processing device, the current weather forecast model data, the one or more current environmental data, and the one or more current in-situ environmental data with the input data”, encompasses how Mewes incorporates all of the above data into a forecast model as the datasets are applied to the AI-based system as part of training the forecast model.
The examiner notes that Mewes teaches in para. [0045, 0088], The artificial intelligence module 173 may use this observed and reported data of field conditions … together with the associated input data 110, to build a more comprehensive dataset… the user can be furnished with a real-time feedback mechanism by which he or she can validate or correct that present indication of the trafficability or workability. Each time this information is provided, the associated predictive metadata, weather data… can be captured and stored alongside the user-indicated condition. This information can then be pooled over time, either within a field or across fields, and for a user or across a pool of users, to serve as the training dataset for the development of AI systems. Thus the BRI for the claim limitations, “revalidating, using the processing device, the nonlinear machine learning- based in-situ environmental forecasting model using the one or more current in-situ environmental data of the updated input data based on the retraining; and reupdating, using the processing device, the nonlinear machine learning-based in-situ environmental forecasting model based on the revalidating, wherein the generating of the updated nonlinear machine learning-based in-situ environmental forecasting model is further based on the reupdating”, encompass the cycles of user validation and updating using all the above data that is used as part of the training of the forecasting AI system.
Argument 7, Applicant argues in Applicant Arguments/Remarks Made in an Amendment filed 01/27/2025 pg. 5-6, regarding Claim 9 that, “Mewes does not explicitly disclose the specific process of generating a data retrieve indication in the context of in-situ environmental forecasting models”.
Response to Argument 7, the examiner respectfully disagrees and notes that Mewes teaches in para. [0047], “generating the indicators and forecasts for agricultural activity comprising the output data 150 for a particular field”. Thus the BRI for the limitation, “generating, using the processing device, a data retrieve indication based on at least one operational criterion, wherein the data retrieve indication corresponds to an instance for retrieving the current weather forecast model data”, encompasses that an indications and forecasts for a specific regional field criterion are generated using the current in-situ weather data.
Argument 8, Applicant argues in Applicant Arguments/Remarks Made in an Amendment filed 01/27/2025 pg. 6, regarding Claim 7 and 17 that, “the citations by the examiner do not match what is written in that publication”.
Response to Argument 8, The examiner acknowledges a typographical error in the Patent application No. cited for Jiang in claims 7 and 17. The correct citation is U.S. Patent Application Publication NO. 20170363774 “Jiang”, as cited in updated PTO-892.
Claim Rejections - 35 USC § 101
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 is a method type claim. The claim recites at least one step or act of determining a forecast based on environmental data. Claims 11 and 20 recite similar limitations. Therefore, claims 1-20 are directed to either a process, machine, manufacture or composition of matter.
With respect to claim 1:
1. (Original) A method for facilitating forecasting of in-situ environmental conditions using nonlinear artificial neural networks-based models, the method comprising: (A) receiving, using a communication device, weather forecast model data associated with a weather forecast model, one or more environmental data associated with one or more of one or more local environmental conditions, one or more regional environmental conditions, and one or more global environmental conditions, and one or more in-situ environmental data associated with one or more in-situ environmental conditions from at least one external device; (B) analyzing, using a processing device, the weather forecast model data, the one or more environmental data, and the one or more in-situ environmental data; (C) generating, using the processing device, input data based on the analyzing; (D) training, using the processing device, a nonlinear machine learning-based in- situ environmental forecasting model based on the input data using at least one machine learning technique; (E) validating, using the processing device, the nonlinear machine learning-based in-situ environmental forecasting model using the one or more in-situ environmental data of the input data based on the training; (F) updating, using the processing device, the nonlinear machine learning-based in-situ environmental forecasting model based on the validating; (G) generating, using the processing device, an updated nonlinear machine learning-based in-situ environmental forecasting model based on the updating; (H) generating, using the processing device, at least one in-situ forecast for at least one in-situ environmental condition based on the updated nonlinear machine learning- based in-situ environmental forecasting model; (I) transmitting, using the communication device, the at least one in-situ forecast to at least one user device; (J) and storing, using a storage device, the nonlinear machine learning-based in-situ environmental forecasting model and the updated nonlinear machine learning-based in-situ environmental forecasting model.
2A Prong 1:
Step (A) recites receiving different types of data, and represent applying the insignificant extra-solution activity of data input to the judicial exception.
Step (B-C, F-H) recite analyzing data, generating input data based on the analyzing, as well as generating an updated MLM for forecasting. These limitations represent step of mere data gathering in which a human may read data from different in-situ devices and other environmental conditions, write down such data, and use that written data to make predictions while updating predictions based on their own validations and observations of data. Under a broadest reasonable interpretation, fall under the abstract of idea of mental processes that can be performed in the human mind, or by a human using a pen a paper.
Step (D) recites elements of training a forecasting model. When given the broadest given their broadest reasonable interpretation in light of the background, the training uses machine learning algorithms and mathematical concepts such as support vector regression. These identified as mere instructions to apply the abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and do not integrate the abstract idea into practical application. Wherein it is noted that the BRI for Step (D) also encompasses abstract ideas such a mathematical concepts.
Step (E) recites validating, using the processing device, the nonlinear machine learning-based in-situ environmental forecasting model using the one or more in-situ environmental data of the input data based on the training. The claim does not provide any details about how the trained machine leaning model operates or how the validation is made, and the plain meaning of “validating” encompasses mental observations or evaluations, e.g., a computer programmer’s mental validation of a model in a data set.
Steps (I-J) recites the steps of transmitting and storing the forecast machine learning models and are insignificant extra-solution activity to the judicial exception.
Steps (A-J) are all recited as being performed by a processing device. The recited device is recited at a high level of generality, i.e., as a generic computer performing generic computer functions.
As discussed above, the broadest reasonable interpretation of steps (B-C, F-H) is that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion and step (D) falls within the mathematical concepts grouping of abstract ideas. See MPEP 2106.04(a)(2), subsection III.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
The limitations, “using a communication device”, “using a processing device”, and “using a storage device”, are mere instructions to apply the exception using a generic computer component. The recitation of a computer to perform limitations (A-J) amounts to no more than mere instructions to apply the exception using a generic computer component.
The limitation, “training, using the processing device, a nonlinear machine learning-based in- situ environmental forecasting model based on the input data using at least one machine learning technique”, are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. see MPEP 2106.05(f)
The limitations of storing, generating, updating, and transmitting the in-situ environmental forecasting model are adding insignificant extra-solution activity to the judicial exception. MPEP 2106.05(d)(II) indicate that merely “storing and retrieving information in memory” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed storing step is well-understood, routine, conventional activity is supported under Berkheimer).
2B: Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
Claim 11 is the system claim reciting similar limitations to Claim 1 and is rejected for similar reasons.
Claim 20 is the method claim reciting similar limitations to Claim 1 and is rejected for similar reasons.
As per Claim 2, the claim include additional mental steps similar to Claim 1, and therefore will be rejected for similar reasons. The limitation of, “wherein the at least one machine learning technique comprises a nonlinear regression, wherein the nonlinear regression comprises at least one of a feedforward neural network, a support vector regression, and a quantile regression”, encompasses abstract ideas such as mathematical concepts and do not integrate the recited judicial exception into a practical application. Thus Claim 2 is directed to an abstract idea.
As per Claim 3, the claim include additional mental steps similar to Claim 1, and therefore will be rejected for similar reasons. The limitations of, “receiving, using the communication device, at least one in-situ environmental condition indication from the at least one user device; and identifying, using the processing device, the at least one in-situ environmental condition associated with the at least one in-situ environmental condition indication, wherein the generating of the at least one in-situ forecast for the at least one in-situ environmental condition is further based on the identifying”, are mental steps of gathering data, updating, and generating data using generic computer components and do not integrate the recited judicial exception into a practical application. Thus Claim 3 is directed to an abstract idea.
As per Claim 4, the claim include additional mental steps similar to Claim 1, and therefore will be rejected for similar reasons. The limitations of, “wherein the at least one external device comprises one or more in-situ environmental sensors, wherein the one or more in-situ environmental sensors are disposed in one or more locations at one or more elevations, wherein the one or more in-situ environmental sensors are configured for generating the one or more in-situ environmental data associated with the one or more in-situ environmental conditions at the one or more elevations of the one or more locations, wherein the at least one in-situ forecast for the at least one in-situ environmental condition is associated with the one or more locations”, are elements related to mere data gathering and thus are insignificant extra-solution activity. Thus Claim 4 is directed to an abstract idea.
As per Claim 5, the claim include additional mental steps similar to Claim 1, and therefore will be rejected for similar reasons. The limitations of, “receiving, using the communication device, at least one user environmental data associated with one or more of the one or more local environmental conditions, the one or more regional environmental conditions, the one or more global environmental conditions, and the one or more in-situ environmental conditions from the at least one user device, wherein the generating of the input data is further based on the at least one user environmental data”, are elements related to mere data gathering and thus are insignificant extra-solution activity. Thus Claim 5 is directed to an abstract idea.
As per Claim 6, the claim include additional mental steps similar to Claim 1, and therefore will be rejected for similar reasons. The limitations of, “analyzing of the weather forecast model data, the one or more environmental data, and the one or more in-situ environmental data comprises preprocessing the weather forecast model data, the one or more environmental data, and the one or more in-situ environmental data, wherein the preprocessing comprises performing at least one data cleaning action on the weather forecast model data, the one or more environmental data, and the one or more in-situ environmental data, wherein the generating of the input data is further based on the preprocessing”, wherein the BRI of these steps in light of the specification includes mental steps that may be performed by the human mind such as how the least one data cleaning action may include error detection, handling and correction, data homogenization and file formatting compliance, and data integrity, and among other actions. Thus Claim 6 is directed to an abstract idea.
As per Claim 7, the claim include additional mental steps similar to Claim 1, and therefore will be rejected for similar reasons. The limitations of, “post-processing, using the processing device, the at least one in-situ forecast based on the generating of the at least one in-situ forecast, wherein the post- processing comprising performing at least one data quality control operation on the at least one in-situ forecast; generating, using the processing device, at least one processed in-situ forecast based on the post-processing; and transmitting, using the communication device, the at least one processed in- situ forecast to the at least one user device”, wherein the BRI of these steps in light of the specification includes mental steps that may be performed by the human mind such as the post-processing may include performing at least one data quality control operation, such as value checking in light of observed in-situ data. The elements of transmitting are adding insignificant extra-solution activity to the judicial exception, and thus Claim 7 is directed to an abstract idea.
As per Claim 8, the claim include additional mental steps similar to Claim 1, and therefore will be rejected for similar reasons. The limitations of, “receiving, using the communication device, current weather forecast model data associated with the weather forecast model, one or more current environmental data associated with the one or more of the one or more local environmental conditions, the one or more regional environmental conditions, and the one or more global environmental conditions, and one or more current in-situ environmental data associated with the one or more in-situ environmental conditions from the at least one external device; incorporating, using the processing device, the current weather forecast model data, the one or more current environmental data, and the one or more current in-situ environmental data with the input data; generating, using the processing device, updated input data based on the incorporating; retraining, using the processing device, the nonlinear machine learning-based in-situ environmental forecasting model based on the updated input data using the at least one machine learning technique; revalidating, using the processing device, the nonlinear machine learning- based in-situ environmental forecasting model using the one or more current in-situ environmental data of the updated input data based on the retraining; and reupdating, using the processing device, the nonlinear machine learning-based in-situ environmental forecasting model based on the revalidating, wherein the generating of the updated nonlinear machine learning-based in-situ environmental forecasting model is further based on the reupdating”, are elements related to mere data gathering using generic computer components and thus are insignificant extra-solution activity. Thus Claim 8 is directed to an abstract idea.
As per Claim 9, the claim include additional mental steps similar to Claim 1, and therefore will be rejected for similar reasons. The limitations of, “using the processing device, a data retrieve indication based on at least one operational criterion, wherein the data retrieve indication corresponds to an instance for retrieving the current weather forecast model data, the one or more current environmental data, and the one or more current in-situ environmental data from the at least one external device, wherein the at least one external device comprises the current weather forecast model data, the one or more current environmental data, and the one or more current in-situ environmental data at the instance, wherein the receiving of the current weather forecast model data, the one or more current environmental data, and the one or more current in-situ environmental data is based on the data retrieve indication”, are elements related to mere data gathering using generic computer components and thus are insignificant extra-solution activity. Thus Claim 9 is directed to an abstract idea.
As per Claim 10, the claim include additional mental steps similar to Claim 1, and therefore will be rejected for similar reasons. The limitations of, “preprocessing, using the processing device, the current weather forecast model data, the one or more current environmental data, and the one or more current in-situ environmental data, wherein the preprocessing comprises performing at least one data cleaning action on the current weather forecast model data, the one or more current environmental data, and the one or more current in-situ environmental data, wherein the incorporating of the current weather forecast model data, the one or more current environmental data, and the one or more current in-situ environmental data with the input data is further based on the preprocessing”, are elements related to mere data gather and wherein the BRI of these steps of preprocessing in light of the specification includes mental steps that may be performed by the human mind such as how the least one data cleaning action may include error detection, handling and correction, data homogenization and file formatting compliance, and data integrity, and among other actions. Thus Claim 10 is directed to an abstract idea.
Claims 12-19 are respectively the system claims reciting similar limitations to claims 2-10 and are rejected for similar reasons.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-5, 8-9, 11-15, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication NO. 20160247079 “Mewes”, in light of U.S. Patent Application Publication NO. 20200003919 “Hathi”, and further in light of U.S. Patent Application Publication NO. 20210089944 “Zhou”.
Claim 1:
Mewes teaches a method for facilitating forecasting of in-situ environmental conditions using nonlinear artificial neural networks-based models, the method comprising: receiving, using a communication device, weather forecast model data associated with a weather forecast model (i.e. para. [0027], weather information in the meteorological and climatological data 111 may be applied to one or more weather models 141 to generate such a profile, and/or diagnose, predict, or forecast localized weather conditions), one or more environmental data associated with one or more of one or more local environmental conditions, one or more regional environmental conditions, and one or more global environmental conditions (i.e. para. [0035], “Such sources of may include data from both in-situ and remotely-sensed observation platforms. For example, numerical weather models (NWP) and/or surface networks may be combined with data from weather radars and satellites to reconstruct the current weather conditions on any particular area to be analyzed… Examples of NWP models at least include … GFS (Global Forecast System)”, wherein local weather conditions in a particular area and global conditions may be used as input), and one or more in-situ environmental data associated with one or more in-situ environmental conditions from at least one external device (i.e. para. [0027], The input data 110 includes meteorological and climatological data 111 which is comprised of one or more of in-situ weather data 112); analyzing, using a processing device, the weather forecast model data, the one or more environmental data, and the one or more in-situ environmental data (i.e. para. [0024], “modeling framework 100 presents multiple approaches for simulating relationships between predictive data, various crop and observable outcomes, and is embodied in one or more systems and methods that at least in part include a model that analyzes weather information”, wherein analyzed weather information includes in-situ weather data, modeled weather data 123, and may further include other current-field level weather data, extended-range weather data, and historical, recent, current, predicted, and forecasted weather conditions, from a variety of different sources); generating, using the processing device, input data based on the analyzing (i.e. para. [0033], The plurality of data processing modules 132 include a data ingest component 140, which is configured to perform the ingest, retrieval, request, reception, acquisition or obtaining of input data 110, and initialize the various modeling paradigms disclosed herein … The data ingest component 140 may therefore determine additional input data 110 needed for the various modeling paradigms); training, using the processing device, a nonlinear machine learning-based in- situ environmental forecasting model based on the input data using at least one machine learning technique (i.e. para. [0044], modeling tool 100 is configured to utilize such models 142 to simulate an expected soil response to information comprised of the input data 110 and the diagnosed, predicted, and/or forecasted weather conditions … artificial intelligence 143, which is trained to associate and compare the various types of input data 110 and identify relationships in such input data 110 in a combined analysis); validating, using the processing device, the nonlinear machine learning-based in-situ environmental forecasting model using the one or more in-situ environmental data of the input data based on the training (i.e. para. [0088], the user can be furnished with a real-time feedback mechanism by which he or she can validate or correct that present indication of the trafficability or workability. Each time this information is provided, the associated predictive metadata, weather data… can be captured and stored alongside the user-indicated condition. This information can then be pooled over time, either within a field or across fields, and for a user or across a pool of users, to serve as the training dataset for the development of AI systems); updating, using the processing device, the nonlinear machine learning-based in-situ environmental forecasting model based on the validating (i.e. para. [0088], “the artificial intelligence systems contemplated in the present invention are capable of learning the relationships between workability, trafficability, and the input weather and soil condition data it has to work with at any given time and location”, wherein the user validation may be used as a further training dataset for updated AI models for forecasting); generating, using the processing device, an updated nonlinear machine learning-based in-situ environmental forecasting model based on the updating (i.e. para. [0090], As the user continues to provide feedback to the system, the number of data pairs associated with the user, and the user's farms and fields, continues to grow, thereby permitting the automated, ongoing redevelopment of artificial intelligence models); generating, using the processing device, at least one in-situ forecast for at least one in-situ environmental condition based on the updated nonlinear machine learning- based in-situ environmental forecasting model (i.e. para. [0078], “the process 400 forecasts time-varying expected weather conditions from meteorological and climatological data 111, at a geographical location(s)”, wherein an updated forecast model may be generated in a case where a user validations are used in further training and development of the AI system and the model is an in-situ model as it uses in-situ data in development);
While Mewes teaches using a neural network model to input weather forecasts, wherein the weather data may be non-linear, and in-situ environmental data through layers of training and validation to generate updated artificial neural network-based models, Mewes may not explicitly teach
nonlinear artificial neural networks-based models
However, Hathi teaches,
nonlinear artificial neural networks-based models (i.e. para. [0113], “with respect to nonlinear time series models, the time series to be analyzed and forecasted upon may be nonlinear, and may follow no statistical distribution. Examples of nonlinear models may include artificial neural networks, which may utilize multi layer perceptron”, wherein in-situ data may be analyzed and input into a nonlinear machine learning model).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to add nonlinear artificial neural networks-based models, to Mewes’ neural network for weather forecasting, with nonlinear artificial neural networks-based models, as taught by Hathi. One would have been motivated to combine Hathi with Mewes, and would have had a reasonable expectation of success, as the combination provides a more accurate representation of real-world data taken from in-situ input data that may be not linear in nature.
While Mewes-Hathi teach in-situ based nonlinear artificial neural networks-based models, Mewes-Hathi may not explicitly teach
transmitting, using the communication device, the at least one in-situ forecast to at least one user device; and storing, using a storage device, the nonlinear machine learning-based in-situ environmental forecasting model and the updated nonlinear machine learning-based in-situ environmental forecasting model,
However, Zhou teaches
transmitting, using the communication device, the at least one in-situ forecast to at least one user device (i.e. para. [0116], sending the forecast to an additional device to allow the additional device to display the forecast on a display of the additional device); and storing, using a storage device, the nonlinear machine learning-based in-situ environmental forecasting model and the updated nonlinear machine learning-based in-situ environmental forecasting model (i.e. para. [0136], “sending the forecast to an additional device to allow the additional device to display the forecast on a display of the additional device”, wherein it is noted device 300 may be a client device receiving forecasts sent by the forecast analysis platform, which may retrain and/or update the at least one machine learning model. For example, the forecast analysis platform may retrain and/or update the at least one machine learning model).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to add transmitting, using the communication device, the at least one in-situ forecast to at least one user device; and storing, using a storage device, the nonlinear machine learning-based in-situ environmental forecasting model and the updated nonlinear machine learning-based in-situ environmental forecasting model, to Mewes-Hathi’s neural network for weather forecasting, with how ANNs for forecasts may be updated, transferred, and subsequently stored on external user devices, as taught by Zhou. One would have been motivated to combine Zhou with Mewes-Hathi, and would have had a reasonable expectation of success, as the combination provides users with greater accessibility to view forecast models on less resource intensive devices.
Claim 2:
Mewes, Hathi, and Zhou teach the method of claim 1.
Hathi further teaches
wherein the at least one machine learning technique comprises a nonlinear regression (i.e. para. [0113], examples of linear models may include stochastic models such as auto regression), wherein the nonlinear regression comprises at least one of a feedforward neural network, a support vector regression, and a quantile regression (i.e. para. [0113], The artificial neural network may include a single hidden layer feed forward network).
Claim 3:
Mewes, Hathi, and Zhou teach the method of claim 1.
further comprising: receiving, using the communication device, at least one in-situ environmental condition indication from the at least one user device (i.e. para. [0027], “The input data 110 includes meteorological and climatological data 111 which is comprised of one or more of in-situ weather data 112”, wherein in-situ weather data may be a current weather condition); and identifying, using the processing device, the at least one in-situ environmental condition associated with the at least one in-situ environmental condition indication, wherein the generating of the at least one in-situ forecast for the at least one in-situ environmental condition is further based on the identifying (i.e. para. [0027], This meteorological and climatological data 111 is used to profile expected weather conditions for the particular field 102 to diagnose, predict and forecast expected weather conditions).
Claim 4:
Mewes, Hathi, and Zhou teach the method of claim 1.
Mewes further teaches wherein the at least one external device comprises one or more in-situ environmental sensors, wherein the one or more in-situ environmental sensors are disposed in one or more locations at one or more elevations, wherein the one or more in-situ environmental sensors are configured for generating the one or more in-situ environmental data associated with the one or more in-situ environmental conditions at the one or more elevations of the one or more locations, wherein the at least one in-situ forecast for the at least one in-situ environmental condition is associated with the one or more locations (i.e. para. [0031], “Other sources include sensors 121 that are configured on-board field and farm equipment to collect and transmit data representative of field conditions and soil properties and weather conditions”, wherein it is noted modeling of these processes is also subject to field-level variations in residue, elevation, moisture, and other factors such that an in-situ sensor may be at one or more elevations in one or more field locations).
Claim 5:
Mewes, Hathi, and Zhou teach the method of claim 1.
Mewes teaches further comprising receiving, using the communication device, at least one user environmental data associated with one or more of the one or more local environmental conditions (i.e. para. [0034], Localized weather conditions may be profiled from the meteorological and climatological data 111 to diagnose, predict, or forecast expected weather conditions at one or more geographical locations), the one or more regional environmental conditions, the one or more global environmental conditions, and the one or more in-situ environmental conditions from the at least one user device, wherein the generating of the input data is further based on the at least one user environmental data (i.e. para. [0096], “additional datasets, whether generated internally, user-provided, instrument-derived, or otherwise obtained … such as elevation data … as they pertain to …network flow analyses, and more, may noticeably or significantly improve the accuracy, resolution, availability of variables, or quality of the analyses performed on the data pertaining to a field, region, …or area bounds (field, farm, township, parish, county, state, country, etc.)”, wherein further environmental conditions related to regional state level or global country level conditions may be additionally added as datasets for training).
Claim 8:
Mewes, Hathi, and Zhou teach the method of claim 1.
Mewes teaches further comprising: receiving, using the communication device, current weather forecast model data associated with the weather forecast model, one or more current environmental data associated with the one or more of the one or more local environmental conditions, the one or more regional environmental conditions, and the one or more global environmental conditions, and one or more current in-situ environmental data associated with the one or more in-situ environmental conditions from the at least one external device (i.e. para. [0096], “additional datasets, whether generated internally, user-provided, instrument-derived, or otherwise obtained … such as elevation data … as they pertain to …network flow analyses, and more, may noticeably or significantly improve the accuracy, resolution, availability of variables, or quality of the analyses performed on the data pertaining to a field, region, …or area bounds (field, farm, township, parish, county, state, country, etc.)”, wherein further environmental conditions related to regional state level or global country level conditions may be additionally added as datasets for training and may come from external sensor devices); incorporating, using the processing device, the current weather forecast model data, the one or more current environmental data, and the one or more current in-situ environmental data with the input data (i.e. para. [0098], “they also include training and applying AI-based systems to translate weather … condition data … into … workability metrics based on past and current data collected from on-board data collection systems”, wherein current environmental data for local, regional, and global environmental conditions may be used as additional datasets for the forecast model);
generating, using the processing device, updated input data based on the incorporating (i.e. para. [0045], “The present invention contemplates that these relationships may be identified and developed in such a combined analysis by training the layer of artificial intelligence 143 to continually analyze to input data 110 using the observed and reported data of field conditions”, wherein additional data sets may be continually incorporated and update the training of the AI); retraining, using the processing device, the nonlinear machine learning-based in-situ environmental forecasting model based on the updated input data using the at least one machine learning technique; revalidating, using the processing device, the nonlinear machine learning- based in-situ environmental forecasting model using the one or more current in-situ environmental data of the updated input data based on the retraining; and reupdating, using the processing device, the nonlinear machine learning-based in-situ environmental forecasting model based on the revalidating, wherein the generating of the updated nonlinear machine learning-based in-situ environmental forecasting model is further based on the reupdating (i.e. para. [0045], The artificial intelligence module 173 may use this observed and reported data of field conditions … together with the associated input data 110, to build a more comprehensive dataset”, wherein the model is continuously updated as additional observed and reported data in incorporated and validated by users and externally instrumented devices reporting on in-situ conditions).
Claim 9:
Mewes, Hathi, and Zhou teach the method of claim 8.
Mewes teaches further comprising generating, using the processing device, a data retrieve indication based on at least one operational criterion, wherein the data retrieve indication corresponds to an instance for retrieving the current weather forecast model data (i.e. para. [0047], “The present invention therefore adopts a combined modeling approach for simulating the relationships between input data 110, predictive data and eventual outcomes, and may be thought of as performing one or more customized models for … generating the indicators and forecasts for agricultural activity comprising the output data 150 for a particular field”, wherein the BRI for a data retrieve indication encompasses an indication of that a forecast with a criterion for a particular area has been generated with current weather data), the one or more current environmental data, and the one or more current in-situ environmental data from the at least one external device, wherein the at least one external device comprises the current weather forecast model data, the one or more current environmental data, and the one or more current in-situ environmental data at the instance, wherein the receiving of the current weather forecast model data, the one or more current environmental data, and the one or more current in-situ environmental data is based on the data retrieve indication (i.e. para. [0035], “Such sources of may include data from both in-situ and remotely-sensed observation platforms. For example, numerical weather models (NWP) and/or surface networks may be combined with data from weather radars and satellites to reconstruct the current weather conditions on any particular area to be analyzed”, wherein current local, regional, and global weather data may be used).
Claim 11:
Claim 11 is the system claim reciting similar limitations to Claim 1 and is rejected for similar reasons.
Claim 12:
Claim 12 is the system claim reciting similar limitations to Claim 2 and is rejected for similar reasons.
Claim 13:
Claim 13 is the system claim reciting similar limitations to Claim 3 and is rejected for similar reasons.
Claim 14:
Claim 14 is the system claim reciting similar limitations to Claim 4 and is rejected for similar reasons.
Claim 15:
Claim 15 is the system claim reciting similar limitations to Claim 5 and is rejected for similar reasons.
Claim 18:
Claim 18 is the system claim reciting similar limitations to Claim 8 and is rejected for similar reasons.
Claim 19:
Claim 19 is the system claim reciting similar limitations to Claim 9 and is rejected for similar reasons.
Claim 20:
Claim 20 is the method claim reciting similar limitations to Claim 1 and is rejected for similar reasons.
Claim(s) 6, 10, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication NO. 20160247079 “Mewes”, in light of U.S. Patent Application Publication NO. 20200003919 “Hathi”, and further in light of U.S. Patent Application Publication NO. 20210089944 “Zhou” as applied to claims 8 and 18 above, and further in view of U.S. Patent Application Publication NO. 20160196527 “Bose”.
Claim 6:
Mewes, Hathi, and Zhou teach the method of claim 1.
Mewes, Hathi, and Zhou may not explicitly teach
wherein the analyzing of the weather forecast model data, the one or more environmental data, and the one or more in-situ environmental data comprises preprocessing the weather forecast model data, the one or more environmental data, and the one or more in-situ environmental data, wherein the preprocessing comprises performing at least one data cleaning action on the weather forecast model data, the one or more environmental data, and the one or more in-situ environmental data, wherein the generating of the input data is further based on the preprocessing.
However, Bose teaches
wherein the analyzing of the weather forecast model data, the one or more environmental data, and the one or more in-situ environmental data comprises preprocessing the weather forecast model data, the one or more environmental data, and the one or more in-situ environmental data, wherein the preprocessing comprises performing at least one data cleaning action on the weather forecast model data, the one or more environmental data, and the one or more in-situ environmental data, wherein the generating of the input data is further based on the preprocessing (i.e. para. [0145], Fig. 17, “Preprocessing logic 1702 receives and preprocesses weather observation … Preprocessing in this context may involve de-noising or otherwise cleaning the data and/or performing feature extraction”, wherein it is noted that the cleaned data may be used as inputs in a learning engine that is a predictive probabilistically programmed machine learning technique).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to add wherein the analyzing of the weather forecast model data, the one or more environmental data, and the one or more in-situ environmental data comprises preprocessing the weather forecast model data, the one or more environmental data, and the one or more in-situ environmental data, wherein the preprocessing comprises performing at least one data cleaning action on the weather forecast model data, the one or more environmental data, and the one or more in-situ environmental data, wherein the generating of the input data is further based on the preprocessing, to Mewes-Hathi-Zhou’s neural network for weather forecasting that takes in-situ weather data, with how weather data to be input into a predictive machine learning model may be preprocessed and cleaned before input, as taught by Bose. One would have been motivated to combine Bose with Mewes-Hathi-Zhou, and would have had a reasonable expectation of success, as the combination better prepares raw data into a more suitable format for input.
Claim 10:
Mewes, Hathi, and Zhou teach the method of claim 8.
Mewes teaches further comprising (i.e. para. [0096], “additional datasets, whether generated internally, user-provided, instrument-derived, or otherwise obtained … such as elevation data … as they pertain to …network flow analyses, and more, may noticeably or significantly improve the accuracy, resolution, availability of variables, or quality of the analyses performed on the data pertaining to a field, region, …or area bounds (field, farm, township, parish, county, state, country, etc.)”, wherein further environmental conditions related to regional state level or global country level conditions may be additionally added as datasets for training and may come from external sensor devices).
While Mewes, Hathi, and Zhou teach training a using a current weather forecast model based on one or more current environmental data, and the one or more current in-situ environmental data, Mewes, Hathi, and Zhou may not explicitly teach
Preprocessing… wherein the preprocessing comprises performing at least one data cleaning action on the current weather forecast model data
However, Bose teaches
Preprocessing… wherein the preprocessing comprises performing at least one data cleaning action on the current weather forecast model data (i.e. para. [0145], Fig. 17, “Preprocessing logic 1702 receives and preprocesses weather observation … Preprocessing in this context may involve de-noising or otherwise cleaning the data and/or performing feature extraction”, wherein it is noted that the cleaned data may be used as inputs in a learning engine that is a predictive probabilistically programmed machine learning technique).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to add Preprocessing… wherein the preprocessing comprises performing at least one data cleaning action on the current weather forecast model data, to Mewes-Hathi-Zhou’s neural network for weather forecasting that takes in-situ weather data, with how weather data to be input into a predictive machine learning model may be preprocessed and cleaned before input, as taught by Bose. One would have been motivated to combine Bose with Mewes-Hathi-Zhou, and would have had a reasonable expectation of success, as the combination better prepares raw data into a more suitable format for input.
Claim 16:
Claim 16 is the system claim reciting similar limitations to Claim 6 and is rejected for similar reasons.
Claim(s) 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication NO. 20160247079 “Mewes”, in light of U.S. Patent Application Publication NO. 20200003919 “Hathi”, and further in light of U.S. Patent Application Publication NO. 20210089944 “Zhou”, as applied to claims 8 and 18 above, and further in view of U.S. Patent Application Publication NO. 20170363774 “Jiang”.
Claim 7:
Mewes, Hathi, and Zhou teach the method of claim 1.
While Mewes, Hathi, and Zhou teach an in-situ based forecast, Mewes, Hathi, and Zhou may not explicitly teach further comprising: post-processing, using the processing device, the at least one in-situ forecast based on the generating of the at least one in-situ forecast, wherein the post- processing comprising performing at least one data quality control operation on the at least one in-situ forecast; generating, using the processing device, at least one processed in-situ forecast based on the post-processing; and transmitting, using the communication device, the at least one processed in- situ forecast to the at least one user device.
However, Jiang teaches
post-processing, using the processing device, the at least one in-situ forecast based on the generating of the at least one in-situ forecast, wherein the post- processing comprising performing at least one data quality control operation on the at least one in-situ forecast (i.e. para. [0014], performing post-processing techniques on each such weather projection module to generate weather projection fields for such system operating parameter); generating, using the processing device, at least one processed in-situ forecast based on the post-processing (i.e. para. [0024], “results verification to verify the accuracy and reliability of each such weather projection model against observation data, subject to a system configurer's pre-defined set of accuracy and reliability parameters; and integrating one or more weather projection fields to yield a weather projection product, which weather projection product may be presented to a system user”, wherein the post-processed results may integrated into and result in the generation of an updated weather projection forecast); and transmitting, using the communication device, the at least one processed in- situ forecast to the at least one user device (i.e. para. [0031], The weather projection products of the weather projection systems, which may be updated at varying time intervals, may be transferred to a system-wide information management system, to which some or all system users may have access).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to add post-processing, using the processing device, the at least one in-situ forecast based on the generating of the at least one in-situ forecast, wherein the post- processing comprising performing at least one data quality control operation on the at least one in-situ forecast; generating, using the processing device, at least one processed in-situ forecast based on the post-processing; and transmitting, using the communication device, the at least one processed in- situ forecast to the at least one user device, to Mewes-Hathi-Zhou’s neural network for weather forecasting that takes in-situ weather data, with post-processing is incorporated into the weather forecasting techniques, as taught by Jiang. One would have been motivated to combine Jiang with Mewes-Hathi-Zhou, and would have had a reasonable expectation of success, as the combination improves the accuracy of a predictive model by reducing the noise and false positive within the projection.
Claim 17:
Claim 17 is the system claim reciting similar limitations to Claim 7 and is rejected for similar reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Application Publication NO. 20200264313 “Newman”, teaches in para. [0031], that in situ instrument data 11 may represent one or more measurements of meteorological characteristics as determined by in-situ instruments of a met tower (e.g., a cup anemometer, a sonic anemometer, a weather vane, etc.). Using the techniques described herein, statistical error correction module 10 may generate meteorological characteristic error model 12 based on corrected meteorological data 9 and in situ instrument data 11. For instance, statistical error correction module 10 may apply machine learning techniques to generate meteorological characteristic error model 12. Given a set of raw LIDAR-based corrected meteorological data, meteorological characteristic error model 12 may be usable to make a prediction.
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 DAVID H TAN whose telephone number is (571)272-7433. The examiner can normally be reached M-F 7:30-4:30.
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/D.T./Examiner, Art Unit 2145
/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145
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