DETAILED ACTION
The following is a non-final office action upon examination of application number 18/343560.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/8/2026 has been entered.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 3/26/20206, 5/19/2026, and 6/8/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the Examiner.
Response to Amendment
Claims 1, 8, and 15 have been amended.
Claims 1-20 are pending in the application and have been examined on the merits discussed below.
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.
(Step 2A) 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, wherein the subset of workflows includes workflows that have the event type;
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, and a storage device and an application programming interface. These additional elements do not add a meaningful limitation to the abstract idea since these elements are only broadly applied to the abstract ideas at a high level of generality; thus, none of recited hardware offers a meaningful limitation beyond generally linking the abstract idea to a particular technological environment, in this case, implementation via a processor/computer. Similarly, reciting that certain steps are performed electronically… only adds computer implementation of an abstract step.
Additional elements such as receiving, over 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; and issuing a command using the API… do not yield an improvement in the functioning of the computer itself, nor do they yield improvements to a technical field or technology; further, these additional elements only generally link the abstract idea to a technological environment. Similarly, additional elements in claims 7 and 14, add additional elements that do not yield an improvement and only generally link the abstract idea to a technological environment. Accordingly, these additional element do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
(Step 2B) The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration of the abstract idea into a practical application, the hardware additional elements amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Additional elements such as receiving, over 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; and issuing a command using the API… do not yield an improvement in the functioning of the computer itself, nor do they yield improvements to a technical field or technology; further, these additional elements only generally link the abstract idea to a technological environment. In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
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 Parmar et al. (U.S. Publication No. 2020/0065152) hereinafter “Parmar”.
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).
Parmar teaches the following:
receiving, over an API, a command identifying a change in a value of data… ([0034] … establishment of APIs to facilitate communication between the applications and system, and/or other integration capabilities.[0048] …the establishment of an API and/or data structures to record certain event attributes associated with the interaction events at the content management system [0057] … user devices send instances of interaction event messages 322 over network communication links 301 that are received by message processor 312 at content management server 310 [0059] … in response to user 102.sub.1 uploading a particular content object to content management system 104 using third-party application 110.sub.11 at user device 302.sub.1, an interaction event message is issued from third-party application 110.sub.11 and forwarded to selection service 314)
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; ([0078] …When an interaction event message is received (step 504), the interaction event message is parsed to retrieve one or more interaction attributes (step 506). … the interaction attributes associated with interaction event message 322.sub.1 includes an event identifier (e.g., stored in an “event ID” field), and application identifier (e.g., stored in an “appID” field), an enterprise identifier (e.g., stored in an “enterprise ID” field), an endpoint URL (e.g., stored in an “endpoint” field), a content object identifier (e.g., stored in an “objID” field), a set of event parameters specific to the event (e.g., stored in an “eParams [ ]” object), and/or other attributes. [0070] …Such interaction event messages may also identify one or more interaction events as defined by a set of select event definition attributes 466. As can be observed, an interaction event associated with an application identified in an “appID” field is defined by an event identifier (e.g., stored in an “eventID” field), an event type description (e.g., stored in a “type” field), a set of event parameters specific to the event (e.g., stored in an “eParams [ ]” object), a set of response parameters specific to the event (e.g., stored in an “rParams [ ]” object), and/or other attributes. [0080] All retrieved attributes are then recorded as event attributes associated with the interaction event message (step 512)…)
based on the event type of the event, retrieving a subset of workflows from a workflow datastore, wherein the subset of workflows includes workflows that have the event type of the event; ([0059] … accesses the event attributes 344 stored in event records 334 to select one or more instances of selected workflows 132 from a plurality of workflows [0060] … event-to-workflow mapping 370, which mapping facilitates the selection of certain workflows in response to the detection of certain interaction events. [0072] The interaction events and workflows defined in the application-specific information 348 can be associated in various selection rules described by a set of select selection rule attributes 468. The select selection rule attributes 468 indicates that a particular selection rule is described by a rule identifier (e.g., stored in a “rule ID” field), an event identifier (e.g., stored in an “event ID” field), one or more workflows associated with the interaction event identifier (e.g., stored in a “workflows [ ] ” object), and/or other attributes. Each workflow associated with the event identifier is described by a workflow identifier (e.g., stored in a “workflowID” field), one or more conditions for initiating the workflow (e.g., stored in a “conditions [ ] ” object), and/or other attributes. [0084] …, the event attributes are applied to the selection rules to automatically select at least one selected workflow).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Coolidge with the aforementioned teachings of Parmar with the motivation of facilitating selection of workflows in response to events (Parmar [0060]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Parmar to the system of Coolidge would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the execution of workflows based on events.
Claims 2, 9, and 16:
Coolidge, Amin, and Parmar 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 Parmar 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 Parmar 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 Parmar 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 Parmar et al. (U.S. Publication No. 2020/0065152) hereinafter “Parmar” in view of Bursey, Brent (U.S. Publication No. 2009/0089078) hereinafter “Bursey”.
Claims 3, 10, and 17:
Coolidge, Amin, and Parmar 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 Parmar teach claims 1, 8, and 15, 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 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 Parmar 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 Parmar teach claims 1, 8, and 15, 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 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).
Response to Arguments
Applicant's arguments filed 5/19/2026 have been fully considered but they are not persuasive.
With respect to the rejection under 35 USC 101, Applicant argues that the claims are directed to an improvement.
Examiner respectfully disagrees. Additional elements such as receiving, over 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; and issuing a command using the API… do not yield an improvement in the functioning of the computer itself, nor do they yield improvements to a technical field or technology; further, these additional elements only generally link the abstract idea to a technological environment.
Automating execution of a workflow based on events does not amount to a technical improvement. In FairWarning, the court found that accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer did not show an improvement, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016).
With respect to the rejection under 35 USC 103, Applicant argues that the art of record does not disclose the claimed limitations.
Examiner respectfully disagrees. The Applicant’s arguments are directed to newly amended features; additional search has been conducted and the rejection has been updated to address said amendments. See updated Claim Rejections - 35 USC § 103 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2020/0076913 (Trocki) – discloses a system that extracts and maps API call parameters.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALAN TORRICO-LOPEZ whose telephone number is (571)272-3247. The examiner can normally be reached M-F 10AM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached at (571)272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALAN TORRICO-LOPEZ/Primary Examiner, Art Unit 3625
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