DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This is a Final Action in response to the claims filed on 11/24/2025.
Claims 1 – 20 are currently pending in this application
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/24/2025 has been acknowledged. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. The initialed and dated copy of Applicant’s IDS form 1449 is attached to the instant Office action.
Response to Remarks
Examiner’s Response to Remarks:
§ 101 Rejection
§ 103 Rejection.
Examiner’s Response to § 101 Rejection.
Applicant argues the claimed invention is not an abstract idea applied to a generic computer, but rather a specific technical solution that improves how dispatch computer systems process and respond to operational data.
Examiner respectfully disagrees. Applicant’s claim 1 is directed to a method, which is statutory
category. However, claim 1 recites the abstract idea of mathematical concepts. Particularly claim 1 recites mathematical relationships. For example, converting the command into an event wherein converting includes: transforming the data in the command to an event format including an event type and a parameter value of the event type; evaluating conditional expressions in the subset of workflows using the parameter value of the event; and evaluating a conditional expression of a workflow of the subset of workflows evaluates to true all recite mathematical relationships. Accordingly claim 1 recites an abstract idea. Claims 8 and 15 are substantially similar and recite the same subject matter and abstract idea as claim 1.
The additional elements recited are an API, converting, using a processing unit, the command into an event wherein converting includes: transforming the data in the command from an API command format to an event format including an event type and a parameter value of the event type, a workflow datastore, electronically, and electronically using the API. However these additional elements are considered generic computer components; and the additional elements do not integrate the judicial exception into a practical application. The additional elements are also recited at a high level of generality (i.e., transforming the data in the command from an API command format to an event format) and amount to no more than mere instructions to apply the exception using generic computer components. Claim 1 recites evaluating a conditional expression of a workflow of the subset of workflows evaluates to true, however this is not significantly more than the recited judicial exception. Also the command is merely for storing data. See Applicant’s Spec. ¶¶ 0050 – 0051, and 0056. The claim does not recite additional elements individually nor in combination that amount to significantly more than the judicial exception, as the claims merely provide instructions to implement an abstract idea on a computer. Here the Applicant is resolving a business problem of managing completion of agricultural tasks, as there is no improvement to the computer nor is there an improvement to a technological field. The dependent claims are rejected by virtue of depending on the independent claims. Accordingly, all pending claims are rejected under 35 U.S.C. § 101.
Examiner’s Response to § 103 Rejection.
Applicant argues a prima facie case of obviousness cannot be maintained using the cited references.
Examiner respectfully disagrees. A new search was necessitated due to the amendments to the
independent claims. Examiner has applied new art to the independent claims. All pending claims are
rejected under 35 U.S.C. § 103.
Claim Rejections – 35 U.S.C. §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 – 20, are rejected under 35 U.S.C. §101 because the claimed invention is directed towards an abstract idea without significantly more.
Claims 1, 8, and 15:
receiving, a command identifying a change in a value of data in an agricultural task;
based on the event type of the event, retrieving a subset of workflows;
evaluating conditional expressions in the subset of workflows using the parameter value of the event;
based on the evaluating, determining a conditional expression of a workflow of the subset of workflows evaluates to true;
and in response to the determining, issuing a command in accordance with an action of the workflow of the subset of workflows.
The limitations of claim 1, under its broadest reasonable interpretation recite mathematical concepts where the claim involves evaluating conditional expressions between parameters. For example, the claim observes a command identifying a change in an agricultural task; based on the event type of the event, retrieving a subset of workflows; evaluating conditional expressions in the subset of workflows using the parameter value of the event; based on the evaluating, determining a conditional expression of a workflow of the subset of workflows evaluates to true; and in response to the determining, issuing a command in accordance with an action of the workflow of the subset of workflows. However, this is merely mathematical relationships where the claim evaluates the conditional expressions to true and merely analyzes and manipulates data into another format and then stores the data with the command. Claims 8 and 15 are substantially similar and recite the same subject matter as claim. Accordingly, claims 1, 8, and 15 recite the abstract idea of mathematical relationships.
The dependent claims encompass the same abstract ideas as well. For example, claims 2, 9, and 16 are directed towards observing the event type is defined as part of a data model of a set of data models; claims 3, 10, and 17 are directed towards evaluating a query using the data model and event type as filter parameters and the workflow datastore as a target; evaluating the query; and observing identifications of the subset of workflows as a result of executing the query; claims 4, 11, and 18 are directed towards observing the data model is a field operation data model for the agricultural task; claims 5, 12, and 19 are directed towards observing the event type is a completion event; and claims 6, 13, and 20 are directed towards observing the command identifying a change in an agricultural task includes an organizational identification and wherein the organizational identification is included as a filter parameter of the query. Accordingly, the dependent claims encompass the same abstract idea.
These judicial exceptions are not integrated into a practical application. Claim 1 recites the additional elements of an API, converting, using a processing unit, the command into an event wherein converting includes: transforming the data in the command from an API command format to an event format including an event type and a parameter value of the event type, a workflow datastore, electronically, and electronically using the API. In addition to reciting the additional elements of claim 1, claim 8 also recites the additional elements of a non-transitory computer-readable storage medium, and a processor. In addition to reciting the additional elements of claim 1, claim 15 also recites the additional elements of a system, a processing unit, a storage device and an application programming interface. The additional elements of a system, a storage device, a non-transitory computer-readable storage medium, and a processor are generic computer components as per Applicant’s Spec. shown below:
“[0021] Client device 104 may be a computing device which may be, but is not limited to, a smartphone, tablet, laptop, multi-processor system, microprocessor-based or programmable consumer electronics, game console, set-top box, or another device that a user utilizes to communicate over a network. In various examples, a computing device includes a display module (not shown) to display information (e.g., in the form of specially configured user interfaces). In some embodiments, computing devices may comprise one or more of a touch screen, camera, keyboard, microphone, or Global Positioning System (GPS) device.”
and thus are not practically integrated nor significantly more.
The claims do not include additional elements that are sufficient to amount significantly more than the judicial exception. Each of the additional limitations are no more than mere instructions to apply the exception using generic computer components (e.g., processor). The combination of these additional elements are no more than mere instructions to apply the exception using generic computer components (e.g., a processing unit). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Accordingly, the additional elements do not integrate the abstract ideas into a practical application because the additional elements do not impose meaningful limits on practicing the idea, and the claims are directed to an abstract idea.
Dependent claims 2 – 7, 9 – 14, and 16 – 20, when analyzed both individually and in combination are also held to be ineligible for the same reasons above, and the additional recited limitations fail to establish that the claims are not directed to an abstract idea. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at these limitations as an ordered combination and individually add nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amount to significantly more than the abstract idea itself. Therefore, claims 1 – 20 are not patent eligible.
Claim Rejections – 35 U.S.C. §103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness
rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness
Claims 1 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Coolidge, Michael et al. (AU 2018/252343 A1) hereinafter “Coolidge” in view of Amin, Gopal et al. (U.S. Patent No. 11,256,557) hereinafter “Amin” in view of Chowdhary, Girish et al. (U.S. Publication No. 2021/0158041) hereinafter “Chowdhary”.
Claims 1, 8, and 15:
A computer-implemented method comprising: receiving, over an API, a command identifying a change in a value of data in an agricultural task; Coolidge teaches in pg. 5, lines 3 – 10, a workflow tracking program to enable real-time monitoring of delivery of the work order steps as they are in progress where tracking to enable real-time monitoring may be likened to identifying a change; Coolidge teaches in pg. 38, lines 16 – 17, receiving user’s instructions, and collaborating with the server application programs for agricultural enterprise management where instructions may be likened to command; Coolidge teaches in claim 44, the instructions, when loaded into the memory of the single computerized platform and executed, further cause the processing structure to perform actions comprising at least one of: estimating potential yields of the selected crop based on the generated scouting report; and assessing whether or not an agronomic prescription should be prepared in order to improve crop growth and development.
While Coolidge teaches monitoring and tracking workflow, updating tasks, and what if analyses, and Coolidge is similar to Amin where Amin teaches workflows carried out through an application programming interface and Amin further teaches the following:
electronically evaluating conditional expressions in the subset of workflows using the parameter value of the event; Amin teaches in col. 2, lines 57 – 67, and col. 3, lines 1 – 8, use the generated code in multiple software processes or threads that run concurrently to process different subsets of the overall set of records to be processed. The number of processes can be dynamically determined based on various factors, such as the number of records in the data set, thresholds or preferences, constraints on execution of the data processing, and so on. For example, the system can have a maximum limit for a number of records to be handled by a single analysis process or thread (e.g., a single executing software process or software thread). The system can determine how many different processes or threads are needed to analyze the records in a set and still fit within the maximum limit on records per process or thread. The system then divides the data set into subsets, starts the determined number of software processes or threads, and evaluates the subsets in parallel using the different processes or threads. Each of the different software processes or threads can run the generated code that provides an optimized application of the rules. Amin teaches in col. 4, lines 35 – 39, in some implementations, the user interface includes interactive controls for a user to (i) define rule elements that each include a condition to be evaluated based on the values of one or more data fields of a record, and (ii) relationships among the rule elements. Amin teaches in col. 13, lines 47 – 67, and col. 14, lines 1 – 3, The process 300 includes determining a number of concurrent processes or threads to execute (308). With the information that indicates characteristics of the data set 360 (e.g., the number and type of data records) and the rule set 350 (e.g., the number and type of rules to be applied), the system 110 can determine the level of computational resources needed to process the rule set in an efficient manner and with an acceptable time frame. In some cases, the system has a set of processing criteria 364 that indicate predetermined parameters for the execution of analysis tasks. This can include thresholds, default parameter values, and other elements that specify how tasks should be executed. For example, the system may indicate a maximum threshold 366 for the number of records to be processed in each software process or thread. In the example, this is set at a maximum of 100 records per process or thread. As a result, the system 110 would determine that a data set with 100 or fewer records should be analyzed using a single analysis process or thread; a data set with 101 to 200 records should be analyzed using two analysis processes or threads; and so on. In the example, the system determines 368 that four processes or threads should be used. This is determined to keep the execution time limited and to perform the task efficiently;
based on the event type of the event, retrieving a subset of workflows from a workflow datastore; Amin teaches in col. 2, lines 17 – 33, Generating the code for rules “just-in-time” in response to requests to apply rules ensures that the current versions of the rules are used each time. The system can provide a multi-user, multi-tenant system that enables different remote users to update and change a shared set of rules (e.g., for a task, organization, account, etc.). When a user initiates application of rules to a data set, the system temporarily blocks further changes to the involved rules for the duration of the processing. The system retrieves the current, most up-to-date rules from the database and generates the code from them, ensuring that each request to apply rules uses an optimized version of the correct, current set of rules. After the request is fulfilled, the system unblocks the associated rules and allows further changes to be made. The code that was generated for the previous request can be discarded, and new optimized code can be generated for the next request based on the rules specified by that request. Amin teaches in col. 3, lines 44 – 50, the request corresponds to a particular subset of the data processing rules defined for the particular user or account, and obtaining the set of data processing rules comprises: generating a database request specifying the particular subset; and executing the database request to retrieve the particular subset of the data processing rules. Amin teaches in col. 12, lines 17 – 31, a process 300 and example series of operations for efficient processing of rule-based computing workflows. The process 300 shows an example of more detailed steps that a computer system 110 may carry out to perform steps 210-214 in Fig. 2. These operations can be performed dynamically, in response to receiving a request for rule processing by the audit manager 122. In some implementations, the compilation of rules is done each time a new processing request is received, performed just-in-time, ensuring that the most up-to-date set of rules is compiled and used in the analysis. Then, the compiled rules are discarded (e.g., deleted or invalidated) so that they are not used again for another processing request, but that the new processing requests each have their own sets of rules compiled.
based on the evaluating, determining a conditional expression of a workflow of the subset of workflows evaluates to true; Amin teaches in col. 10, lines 50 – 64, the interface enables a user to select or define an operator to apply to a data element, as well as to select or enter a reference for comparison. For example, the reference may be a fixed value (e.g., 5 or 80%) or may be derived from the value of another data element. For example, a user may enter that data element DATE1 is less than or equal to data element DATE2, where DATE1 and DATE2 are different dates in the type of data set the rule is applied to. In applying the rules to a specific record, the specific values of the data elements for that record (e.g., 2/3/20 for the DATE1 field and 2/5/20 for the DATE2 field) are used and compared. The interface can also include fields enabling a user to specify the desired result of the operation for the rule to be satisfied, e.g., whether a particular rule should be true or false for a successful result;
and in response to the determining, electronically issuing a command using the API in accordance with an action of the workflow of the subset of workflows; Amin teaches in FIG. 3 is a diagram illustrating a flow chart for a process 300 and example series of operations for efficient processing of rule-based computing workflows. The process 300 shows an example of more detailed steps that a computer system 110 may carry out to perform steps 210-214 in FIG. 2. These operations can be performed dynamically, in response to receiving a request for rule processing by the audit manager 122. In some implementations, the compilation of rules is done each time a new processing request is received, performed just-in-time, ensuring that the most up-to-date set of rules is compiled and used in the analysis. Then, the compiled rules are discarded (e.g., deleted or invalidated) so that they are not used again for another processing request, but that the new processing requests each have their own sets of rules compiled.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a computer-implemented cloud-based agricultural enterprise management system and methods of Coolidge with methods, systems, and apparatus, including computer programs encoded on computer-storage media, for efficiently processing of rule-based computing workflows of Amin to assist businesses with implementing a system that includes a database, an API, and interactive controls to create and edit rules in workflows to carry out functions and process subsets of sets of records (Amin, Spec. col. 3, lines 9 – 28).
While Coolidge teaches monitoring and tracking workflow, application programming on computing devices, updating tasks, and what if analyses, and Amin teaches a data set into and subsets of sets of records, application programming interface, and computing workflows and Amin is similar to Chowdhary where Amin and Chowdhary transform data and Coolidge and Chowdhary provides executable instructions for agricultural tasks and Chowdhary further teaches the following:
converting, using a processing unit, the command into an event wherein converting includes: transforming the data in the command from an API command format to an event format including an event type and a parameter value of the event type; Chowdhary teaches in ¶ 0059, The ROI (Region of Interest) moves with the camera (see the ROI shown as the vertical rectangle in the middle section of Fig. 23A). As the ROI scans across the row of corn plants, the model returns a positive signal when corn is present in the ROI and a negative signal when corn is absent from the ROI. Chowdhary teaches in ¶ 0138, a device is provided comprising: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, perform operations, the operations comprising: obtaining video data from a single monocular camera, wherein the video data comprises a plurality of frames, wherein the single monocular camera is attached to a ground mobile robot that is travelling along a lane defined by a row of crops, wherein the row of crops comprises a first plant stem, and wherein the plurality of frames include a depiction of the first plant stem; obtaining robot velocity data from one or more encoders, wherein the one or more encoders are attached to the ground mobile robot that is travelling along the lane; performing foreground extraction on each of the plurality of frames of the video data, wherein the foreground extraction results in a plurality of foreground images; and determining, based upon the plurality of foreground images and based upon the robot velocity data, an estimated width of the first plant stem.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a computer-implemented cloud-based agricultural enterprise management system and methods of Coolidge and methods, systems, and apparatus, including computer programs encoded on computer-storage media, for efficiently processing of rule-based computing workflows of Amin with an image processing algorithm for crop stem width estimation designed to extract the foreground in the presence of significant leaf and stem clutter, view of other rows, and varying lighting of Chowdhary to assist businesses with implementing a system that includes a processing system that transforms data in an agricultural environment (Chowdhary, Spec. ¶ 0218).
Claims 2, 9, and 16:
Coolidge, Amin, and Chowdhary teach claims 1, 8 and 15. Coolidge further teaches the following:
wherein the event type is defined as part of a data model of a set of data models; Coolidge teaches in pg. 7, lines 20 – 23, and pg. 8, lines 1 – 2, the incorporation at least two separate sets of modelling algorithms that integrate and then correlate and further model the mathematical processing of each of the current and historical data sets for each of the modules comprising the agricultural enterprise system, to enable delivery to the producer’s dashboard display enable realtime high-level push analytics updates on the current crop production status relative to global weather patterns, global commodity market fluctuations based on current supply and demand data, coupled with risk identification and analysis;
Claims 4, 11, and 18:
Coolidge, Amin, and Chowdhary teach claims 1, 8, and 15. Coolidge further teaches the following:
wherein the data model is a field operation data model for the agricultural task; Coolidge teaches in pg. 27, lines 5 – 10, a plurality of algorithms for SWOT analyses during a crop production cycle to optimize production.
Claims 5, 12, and 19:
Coolidge, Amin, and Chowdhary teach claims 1, 8, and 15. Coolidge further teaches the following:
wherein the event type is a completion event; Applicant’s Spec. provides no definition for a completion event; Examiner interprets completion event to be likened to application event; Coolidge teaches in pg. 9, lines 14 – 18, application events comprises of recording the application data of the received one or more first application events in a mobile app of the user and associating the recorded application data with the first task; and recording the application data of the received one or more first application events in the one or more cloud-based databases and associating the recorded application data with the first task.
Claims 6, 13, and 20:
Coolidge, Amin, and Chowdhary teach claims 1, 8, and 15. Coolidge further teaches the following:
wherein the command identifying a change in an agricultural task includes an organizational identification and wherein the organizational identification is included as a filter parameter of the query; Coolidge teaches in pg. 42, lines 10 – 12, Example 3. Use of the Task Module by a service provider to create an agronomic prescription from an in-season crop performance assessment event recorded by a producer in a selected field where an in-season crop performance assessment event is likened to the command identifying a change in an agricultural task. Coolidge teaches in pg. 42, lines 18 – 24, and pg. 43, lines 1 – 3, the producer would have input their observations into the Crop Management app and generate a scouting report uploaded into the agricultural enterprise management system; The producer would have elected to deliver a notification via a suitable communication means 213A such as an email, a text message, an in-app notification, and/or the like, to their service provider (e.g., an agronomist) that a scouting report had been uploaded to the present agricultural enterprise management system. The agronomist would then review 215 the scouting report input 216 and based on the report contents, determine if an agronomic prescription is required where the agronomist review may be likened to organizational identification.
Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Coolidge, Michael et al. (AU 2018/252343 A1) hereinafter “Coolidge” in view of Amin, Gopal et al. (U.S. Publication No. US 11,256,557) hereinafter “Amin” in view of Chowdhary, Girish et al. (U.S. Publication No. 2021/0158041) hereinafter “Chowdary” in view of Bursey, Brent (U.S. Publication No. 2009/0089078) hereinafter “Bursey”.
Claims 3, 10, and 17:
Coolidge, Amin, and Chowdhary teach claims 1, 8, and 15. Coolidge further teaches the following:
wherein retrieving the subset of workflows from a workflow datastore includes: generating a query using the data model and event type as filter parameters; Coolidge teaches in pg. 19, lines 16 – 18, graphical presentation of the current and historical data and data subsets residing within the modules that may be filtered and/or navigated using a content search field based on defined query string parameters; Coolidge teaches in pg. 40, lines 12 – 13, parsing historical data records; Coolidge teaches in pg. 40, lines 20 – 21, historical data may be correlated with realtime feeds;
While Coolidge, Amin, and Chowdhary teach claims 1, 8, and 15, and Coolidge teaches monitoring and tracking workflow, application programming on computing devices, updating tasks, and what if analyses, and Amin teaches a data set into and subsets of sets of records, application programming interface, and computing workflows and Amin is similar to Chowdhary where Amin and Chowdhary transform data and Coolidge and Chowdhary provides executable instructions for agricultural tasks and Coolidge, Amin, Chowdhary and Bursey are similar where all provide databases for storing and retrieving data or instructions and Bursey further subsets of workflows and clustering and Bursey teaches the following:
and the workflow datastore as a target; Applicant does not provide a definition for “a target”. Examiner defines “a target” as a place where data is collected. Bursey teaches in ¶ 0375, repository is a shared, secured data store that contains both events and event component definitions, where repository may me be likened to workflow datastore as a target;
executing the query; Bursey teaches in ¶ 0276, Workflows are contextually scheduled for execution and executed within a cloud computing environment; Bursey further teaches in ¶ 0293, the database to allow spatial operations to be executed within the database itself, and supported spatial data types include features, imagery, metadata, and multimedia;
and receiving identifications of the subset of workflows as a result of executing the query; Bursey teaches in ¶ 0063, the partitioning of a data set into subsets (clusters); Bursey teaches in ¶ 0331, the enterprise geospatial intelligence service oriented architecture (EGI-SOA) executes the workflows.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a computer-implemented cloud-based agricultural enterprise management system and methods of Coolidge and methods, systems, and apparatus, including computer programs encoded on computer-storage media, for efficiently processing of rule-based computing workflows of Amin and an image processing algorithm for crop stem width estimation designed to extract the foreground in the presence of significant leaf and stem clutter, view of other rows, and varying lighting of Chowdhary with an enterprise geospatial intelligence service oriented architecture (EGI-SOA) provides a consumer with one or more tailored products in response to either a dynamic request or a standing request by the consumer of Bursey to assist businesses with using a data clustering technique when classifying the data set into k clusters (Bursey, Spec. ¶ 0063).
Claims 7 and 14:
Coolidge, Amin, and Chowdhary teach claims 1, 8, and 15. Coolidge further teaches the following:
presenting a workflow creation user interface element on a client device including: a model selection user interface element configured to receive a selection of a model from a set of data models an event type selection user interface element configured to receive a selection of an event type from a set of event types for the selected data model; Coolidge teaches in pg. 4, line 10, an agricultural enterprise management system that is likened to a workflow creation user interface element on a client device; Coolidge teaches in pg. 5, lines 4 – 5, generating a work order and is likened to a workflow; Coolidge teaches in pg. 25, lines 1 – 12, an agricultural enterprise management system comprising data input modules pertaining to multiple cropping cycle records pertaining to variable zone-based agronomic prescriptions for optimized production of selected crops in selected agricultural fields, based on correlations of selected satellite imagery with soil sample analyses, agronomic prescriptions, historical crop production records, and historical weather data and is likened to receive a selection of a model from a set of data models an event type selection user interface element configured to receive a selection of an event type from a set of event types for the selected data model;
a conditional input element configured to receive an evaluation expression associated with a parameter of the selected event type; Coolidge teaches in pg. 27, lines 5 – 10, a plurality of algorithms for assimilating outputs from the above components into dashboard summaries of “key performance indicators” (KPIs) that provide high-level snapshots of real-time crop production performance with “SWOT” (strengthweakness-opportunity-threat) analyses during a crop production cycle, to enable management decisions to modify crop management activities in order to optimize production outputs and revenues captured;
receiving a data package including an identification of the model, an identification of the event type, the evaluation expression, and the action; Coolidge teaches in pg. 1, lines 9 – 10, identifying, evaluating and optimizing options for crop selection, crop rotations, and selection of crop production inputs where evaluating and optimizing options for crop selection may be likened to identification of the model.
in response to receiving the data package, generating a workflow data structure based on the identification of the model, the identification of the event type, the evaluation expression, and the action; Coolidge teaches in pg. 1, an agricultural producer to input their annual field-by-field production-related agronomic data for assimilation and correlation with the historical data for the fields, and then for assimilation of the correlated production data with related inputs and/or services data records pertaining to fertility and pest management to provide accurate historical data regarding annual and multi-year revenues and returns-on-investment generated by the different crops that were grown on those fields where an agricultural producer to input their annual field-by-field production-related agronomic data is likened to receive the data package, generating a workflow data structure based on the identification of the model, and for assimilation is likened to the identification of the event type, and correlation with the historical data for the fields is likened to based on the identification of the model, and correlated production data with related inputs and/or services data records pertaining to fertility and pest management is likened to the evaluation expression, and to provide accurate historical data regarding annual and multi-year revenues and returns-on-investment generated by the different crops that were grown on those fields is likened to the action.
storing the workflow data structure in the workflow datastore; Examiner interprets workflow data structure in the workflow datastore as application data stored in a database; Coolidge teaches in pg. 9, lines 16 – 17, recording the application data of the received one or more first application events in the one or more cloud-based databases.
While Coolidge, Amin, and Chowdhary teach claims 1, 8, and 15, and Coolidge teaches monitoring and tracking workflow, application programming on computing devices, updating tasks, and what if analyses, and Amin teaches a data set into and subsets of sets of records, application programming interface, and computing workflows and Amin is similar to Chowdhary where Amin and Chowdhary transform data and Coolidge and Chowdhary provides executable instructions for agricultural tasks and Coolidge, Amin, Chowdhary and Bursey are similar where all provide databases for storing and retrieving data or instructions and Bursey further subsets of workflows and clustering and Bursey teaches the following:
and an action input element configured to receive an action to perform when the evaluation expression is true; Bursey teaches in ¶ 0376, an event pattern that triggers the event handler when the pattern resolves to "TRUE", evaluation sets that provide the business logic that determines if the action or actions associated with the event are triggered for execution;
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a computer-implemented cloud-based agricultural enterprise management system and methods of Coolidge and methods, systems, and apparatus, including computer programs encoded on computer-storage media, for efficiently processing of rule-based computing workflows of Amin and an image processing algorithm for crop stem width estimation designed to extract the foreground in the presence of significant leaf and stem clutter, view of other rows, and varying lighting of Chowdhary with an enterprise geospatial intelligence service oriented architecture (EGI-SOA) provides a consumer with one or more tailored products in response to either a dynamic request or a standing request by the consumer of Bursey to assist businesses with using a data clustering technique when classifying the data set into k clusters (Bursey, Spec. ¶ 0063).
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied:
Note: these are additional references found but not used.
- Reference Johnson, Jerome Dale (U.S. Publication No. 2013/0174040) discloses methods, apparatus, and systems for generating, updating, and executing a crop-planting plan.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the 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 Frank Alston whose telephone number is 703-756-4510. The Examiner can normally be reached 9:00 AM – 5:00 PM Monday - Friday. Examiner can be reached via Fax at 571-483-7338.
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 Beth Boswell can be reached at (571) 272-6737.
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.
/FRANK MAURICE ALSTON/
Examiner, Art Unit 3625
03/24/2026
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625