Systems and methods for data-driven workflow platforms

Abstract

The present disclosure provides systems and methods for a data-driven workflow platform. The methods of the present disclosure can include mapping a selected data object to a data storage model of the data-driven workflow platform, wherein the selected data object is stored in a data cloud configuration operatively coupled with the data-driven workflow platform; and displaying, on a graphical user interface (GUI), a flow for building a cloud application with or managing the selected data object. The interactive flow includes at least one graphical element corresponding to a rule for automating an action triggered by a trigger event of the selected data object.

Description

[0001] This application is a divisional application of Chinese patent application No. 202380085277.6, entitled "System and Method for Data-Driven Workflow Platform" (the corresponding PCT application was filed on October 20, 2023, with application number 202380085277.6 and invention title PCT / US2023 / 077464). Cross-references

[0002] This application claims priority and benefit to U.S. Provisional Application No. 63 / 418,397, filed October 21, 2022; U.S. Provisional Application No. 63 / 454,917, filed March 27, 2023; and U.S. Application No. 18 / 340,510, filed June 23, 2023, each of which is incorporated herein by reference in its entirety. Background Technology

[0003] Computing systems are ubiquitous in modern business and are typically used as a critical operational resource. For example, many companies utilize so-called "Enterprise Resource Planning" (or "ERP") systems to assist in various aspects such as financial management, human resources, and inventory management. Other commonly used distributed computing business systems include systems known as "Transportation Management Systems" (or "TMS") (which can be used to plan, monitor, and optimize logistics and transportation), and systems known as "Risk Management Systems" (or "RMS") (which can be used to help compliance officers and others understand the company's risk profile and the extent to which it complies with applicable rules and regulations). The global ERP software market alone is estimated to be worth $45 billion annually, with providers such as SAP (RTM), Oracle (RTM), and Workday (RTM) offering a wide range of solutions. Summary of the Invention

[0004] Current data-intensive applications (e.g., ERP software, ERP applications, RMS applications, etc.) may need to integrate with cloud lakes or data warehouses to copy or download data from the cloud for business intelligence analysis, computation, and workflow execution on local data. For example, ETL (Extract, Transform, Load) or ELT (Load and Transform in a Data Warehouse) processes are required to move data from one database, multiple databases, or other sources to a unified repository.

[0005] There is a need for a service that can natively connect to the cloud to manage the cloud, allowing cloud applications to be created and executed on real-time data in existing cloud-based repositories without integration, transformation, or downloading. This disclosure provides systems and methods that allow users to create, customize, and manage applications that leverage distributed computing systems to manage data flows and processes. In particular, the systems and methods described herein can be used for business process optimization, where operations and processes can be managed and used without traditional relocation and / or replication of enterprise data. This disclosure provides users, organizations, or cloud service providers with a unified platform (e.g., a cloud-native SaaS platform for no-code business applications with data-driven workflows) to access their cloud data, process cloud data from business applications that initiate and manage workflows via native cloud connectivity, and improve efficiency by eliminating the need for data integration, transformation, or downloading. The platform described herein allows users to create, customize, and / or configure cloud applications through a no-code user interface with built-in features such as data mining, configurable and automated workflows, and dynamic relationship discovery and creation.

[0006] In one aspect, this paper describes a method for providing a data-driven workflow platform. The method includes: mapping selected data objects to a data storage model of the data-driven workflow platform, wherein the selected data objects are stored in a data cloud configuration operatively coupled to the data-driven workflow platform; and displaying an interactive flow on a graphical user interface (GUI) for utilizing or managing the selected data objects to build cloud applications, wherein the interactive flow includes at least one graphical element corresponding to a rule for automating actions triggered by triggering events of the selected data objects.

[0007] In some embodiments, a data cloud configuration includes one or more data clouds storing data objects, and a data-driven workflow platform is granted permissions to access, process, and edit the data objects stored on the one or more data clouds. In some embodiments, mapping selected data objects to a data storage model includes defining relationships between the selected data objects and elements of the data storage model. In some cases, this relationship is defined by the user through a GUI. In some cases, the GUI allows the user to link one or more data fields of the selected data object to one or more data fields or elements of the data storage model. In some cases, this relationship is automatically generated by the data-driven workflow platform and displayed as a recommended relationship on the GUI.

[0008] In some embodiments, mapping selected data objects to a data storage model includes identifying missing elements from the data storage model and prompting the user to identify another set of data objects for the missing elements. In some embodiments, the data storage model includes multiple data types, including at least one of task type, application type, and element data type. In some cases, mapping selected data objects to a data storage model includes mapping the selected data objects to element data types.

[0009] In some embodiments, the interactive flow allows users to add, remove, or modify one or more components of a cloud application by dragging and dropping one or more graphical elements onto the interactive flow. In some cases, the interactive flow includes a pre-built template flow that prompts the user to add, remove, or modify one or more components. In some cases, the pre-built template flow is determined automatically, at least in part, based on selected data objects and the cloud application.

[0010] In some embodiments, rules are automatically generated, at least in part, based on one or more data fields added to the interactive stream. In some cases, models are used to automatically generate rules, and the models are developed using rules extracted from past actions and previously processed data. In some cases, rules are recommended to the user on a GUI, and at least one graphical element allows the user to accept, reject, or modify the rules.

[0011] In some embodiments, rules are manually defined by the user via a GUI. In some embodiments, rules include definitions of triggering events, wherein the triggering events are based on time or associated with changes in the values ​​or states of at least a subset of selected data objects. In some cases, rules further include definitions of conditions for performing actions. In some cases, rules further include definitions of actions. In some examples, actions are selected from adding an observer, updating a field, sending a notification, posting a comment, assigning to a user or group, and creating a record.

[0012] In some embodiments, the method further includes displaying selected data objects conforming to the data storage model within a portal of the GUI. In some cases, the method further includes modifying the value of at least one of the selected data objects via the GUI and automatically updating the value of the corresponding selected data object in the data cloud configuration via an API connection. In some cases, the method further includes receiving instructions via the GUI for performing an operation on at least one of the selected data objects, and performing the operation on at least one of the selected data objects in the data cloud configuration without using an Extract, Transform, and Load (ETL) data integration process. For example, the selected data objects include transactional data or streaming data, and the operation further includes caching intermediate results via a data-driven workflow platform. In some embodiments, triggering events for the selected data objects include changes to the selected data objects stored in the data cloud configuration.

[0013] In another aspect, this paper describes a system for providing a data-driven workflow platform. The system includes: a first module configured to operatively couple the data-driven workflow platform to one or more data clouds; a second module configured to map selected data objects to a data storage model of the data-driven workflow platform, wherein the selected data objects are stored on one or more data clouds; and a visualization module configured to display an interactive flow on a graphical user interface (GUI) for building cloud applications using or managing the selected data objects, wherein the interactive flow includes at least one graphical element corresponding to a rule for automating actions triggered by triggering events from the selected data objects.

[0014] In some embodiments, the first module manages permissions granted to the data-driven workflow platform for accessing, processing, and editing one or more data objects stored on one or more data clouds. In some embodiments, selected data objects are mapped to the data storage model by defining connections between selected data objects and elements of the data storage model. In some embodiments, the visualization module is further configured to display a second GUI that allows the user to define relationships between elements of the data storage model. In some cases, the second GUI allows the user to link one or more data fields of a first selected element of the data storage model to one or more data fields of a second selected element of the data storage model. In some cases, this relationship is automatically generated by the data-driven workflow platform and displayed as a recommended relationship on the second GUI.

[0015] In some embodiments, the second module is configured to further identify missing elements from the data storage model and prompt the user to identify another set of data objects for the missing elements. In some embodiments, the data storage model includes multiple data types, including at least one of task type, application type, transaction data type, and element data type. In some cases, the second module is configured to further map selected data objects to transaction data type or element data type.

[0016] In some embodiments, the interactive flow allows users to add, remove, or modify one or more components of a cloud application by dragging and dropping one or more graphical elements onto the interactive flow. In some cases, the interactive flow includes a pre-built template flow that prompts the user to add, remove, or modify one or more components. For example, the pre-built template flow is automatically determined at least in part based on selected data objects and the cloud application. In some cases, rules are automatically generated at least in part based on one or more data fields added to the interactive flow. In some cases, models are used to automatically generate rules, and the models are developed using rules extracted from past actions and previously processed data. For example, rules are recommended to the user on a GUI, and at least one graphical element allows the user to accept, reject, or modify the rules.

[0017] In some embodiments, rules are manually defined by the user via a GUI. In some embodiments, rules include definitions of triggering events, wherein the triggering events are based on time or associated with changes in the values ​​or states of at least a subset of selected data objects. In some cases, rules further include definitions of conditions for performing actions. In some cases, rules further include definitions of actions. In some cases, actions are selected from adding observers, updating fields, sending notifications, posting comments, assigning to users or groups, and creating records.

[0018] In some embodiments, the visualization module is further configured to display selected data objects conforming to the data storage model within a portal of the GUI. In some cases, the value of at least one of the selected data objects is modified via the GUI, and the value of the corresponding selected data object in the data cloud configuration is automatically updated by the first module. In some embodiments, the first module is configured to translate instructions for performing operations on at least one of the selected data objects received via the GUI into database operations executable in the data cloud configuration. In some cases, database operations are performed on the selected data objects in the data cloud configuration without using an Extract, Transform, and Load (ETL) data integration process. In some cases, the selected data objects include transactional data or streaming data, and a data-driven workflow platform is configured to cache intermediate results for performing operations. In some embodiments, triggering events for the selected data objects include changes to the selected data objects stored in the data cloud configuration.

[0019] In some embodiments, interaction flows are identified from multiple predefined workflows using a large language model (LLM). In some cases, interaction flows are identified at least in part based on data patterns of selected data objects stored in a data cloud configuration. In some cases, the output of the LLM includes a list of instructions for creating interaction flows.

[0020] Additional aspects and advantages of this disclosure will become apparent to those skilled in the art from the following detailed description, in which only illustrative embodiments of the disclosure are shown and described. As will be appreciated, this disclosure is capable of having other embodiments and different embodiments, and several details thereof can be modified in various obvious ways, all without departing from the disclosure. Therefore, the drawings and descriptions should be considered illustrative in nature and not restrictive. Incorporation

[0021] All publications, patents, and patent applications mentioned in this specification are incorporated herein by reference to the extent that each individual publication, patent, or patent application is specifically and individually indicated as incorporated by reference. If any publication, patent, or patent application incorporated by reference contradicts the disclosure contained in this specification, the specification is intended to supersede and / or take precedence over any such contradictory material. Attached Figure Description

[0022] The novel features of this disclosure are particularly set forth in the appended claims. A better understanding of the features and advantages of this disclosure will be obtained by referring to the following detailed description of illustrative embodiments and the accompanying drawings (also referred to herein as “Figures”), in which: Figure 1This illustrates an example of storing enterprise data in a traditional database system.

[0023] Figure 2 An example of a cloud-based repository provider is shown.

[0024] Figure 3 Examples of cloud services and SaaS are illustrated schematically.

[0025] Figures 4-7 The illustration shows various configurations in which the service management cloud system can be configured to have direct interconnectivity between user and data cloud configurations.

[0026] Figure 8 The diagram illustrates a platform that provides an interface for viewing, accessing, and managing all process data protected within the data cloud.

[0027] Figure 9 An example of a service management cloud system is illustrated schematically.

[0028] Figure 10 The diagram illustrates the service management cloud configuration.

[0029] Figure 11 The diagram illustrates the service management cloud session configuration.

[0030] Figure 12 The diagram illustrates the configuration of a service management cloud session with write-back capability.

[0031] Figures 13-15 The diagram illustrates access management, control, and collaboration within the platform.

[0032] Figure 16 The diagram illustrates a platform with secure and efficient access management.

[0033] Figure 17 The illustration shows an example of establishing a connection to a data source in the data cloud and mapping the source data to data elements in the platform.

[0034] Figures 18-20 Examples of use cases for configuring and using data-driven workflow platform variants in complex business processes with varying levels of automation are shown.

[0035] Figures 21-23 An example of a GUI for creating and / or editing automation is shown.

[0036] Figure 24 and Figure 25 An example of a GUI for creating or adding relationships is shown.

[0037] Figures 26-30 An example of a GUI for creating workflows is shown.

[0038] Figure 31 and Figure 32 An example is shown that displays a created workflow with tracking progress and analysis.

[0039] Figures 33-38 An example of a logistics application suite is shown.

[0040] Figures 39-43 An example of a GUI for configuring or creating data mining is shown.

[0041] Figure 44 The architecture of a data-driven workflow platform is illustrated schematically.

[0042] Figure 45 Examples of AI-based application discovery features according to some embodiments of this disclosure are illustrated.

[0043] Figures 46-48 An example of a GUI for AI-based application discovery features is shown.

[0044] Figure 49 Examples of AI-generated workflow features according to some embodiments of this disclosure are illustrated schematically.

[0045] Figures 50-53 An example GUI of AI-generated workflow features is shown.

[0046] Figure 54 and Figure 55 An example of a GUI for automated workflows is shown.

[0047] Figure 56 and Figure 57 An example of the GUI for the app marketplace is shown.

[0048] Figure 58 and Figure 59 An example of a GUI (e.g., CloudLink Explorer) is shown, which allows users to search for data in a data cloud (e.g., Snowflake).

[0049] Figure 60 An example of a GUI is shown for users to set logical rules (e.g., filter parameters and logical operators in records) to find data.

[0050] Figure 61 An example of a GUI is shown for users to set up machine learning-based anomaly detection and reporting rules.

[0051] Figure 62 An example of a GUI is shown for users to select the main column to be used as a unique identifier for data.

[0052] Figure 63 A non-limiting example of a computing device is shown; in this case, the computing device is a device having one or more processors, memory, storage devices, and network interfaces.

[0053] Figure 64 A non-limiting example of a web / mobile application delivery system is shown; in this case, the web / mobile application delivery system is a system that provides browser-based and / or native mobile user interfaces.

[0054] Figure 65 A non-limiting example of a cloud-based web / mobile application delivery system is shown; in this case, the cloud-based web / mobile application delivery system is a system that includes elastically load-balanced, automatically scaling web server and application server resources, as well as synchronously replicated databases.

[0055] This application provides, but is not limited to, the following implementation methods: 1. A method for providing a data-driven workflow platform, the method comprising: Mapping selected data objects to the data storage model of the data-driven workflow platform, wherein the selected data objects are stored in a data cloud configuration operatively coupled to the data-driven workflow platform; and A flow for building cloud applications using or managing the selected data objects is displayed on a graphical user interface (GUI), wherein the flow includes at least one graphical element that corresponds to a rule for automating actions triggered by triggering events of the selected data objects.

[0056] 2. The method according to embodiment 1, wherein the data cloud configuration includes one or more data clouds for storing data objects, and wherein the data-driven workflow platform is granted permissions to access, process, and edit the data objects stored on the one or more data clouds.

[0057] 3. The method according to embodiment 1, wherein mapping the selected data object to the data storage model includes defining the relationship between the selected data object and the elements of the data storage model.

[0058] 4. The method according to embodiment 3, wherein the relationship is defined by the user through the GUI.

[0059] 5. The method according to embodiment 4, wherein the GUI allows the user to link one or more data fields of a selected data object to one or more data fields or elements of the data storage model.

[0060] 6. According to the method of embodiment 3, the relationship is automatically generated by the data-driven workflow platform and displayed as a recommended relationship on the GUI.

[0061] 7. The method according to embodiment 1, wherein mapping the selected data object to the data storage model includes identifying missing elements from the data storage model and prompting the user to identify another set of data objects for the missing elements.

[0062] 8. According to the method of embodiment 1, the data storage model includes multiple types of data, including at least one of task type, application type and element data type.

[0063] 9. The method according to embodiment 8, wherein mapping the selected data object to the data storage model includes mapping the selected data object to an element data type.

[0064] 10. The method according to embodiment 1, wherein the stream allows a user to add, remove or modify one or more components of the cloud application by dragging and dropping one or more graphical elements onto the stream.

[0065] 11. The method according to embodiment 10, wherein the stream includes a pre-built template stream that prompts the user to add, remove or modify one or more components.

[0066] 12. The method according to embodiment 11, wherein the pre-built template stream is automatically determined based at least in part on the selected data object and the cloud application.

[0067] 13. The method according to embodiment 1, wherein the rules are automatically generated based at least in part on one or more data fields added to the stream.

[0068] 14. The method according to embodiment 13, wherein a model is used to automatically generate the rules, and wherein the model is developed using rules extracted from past actions and previously processed data.

[0069] 15. The method according to embodiment 14, wherein a machine learning algorithm is used to train the model.

[0070] 16. The method according to embodiment 14, wherein the rules are recommended to the user on the GUI, and wherein the at least one graphical element allows the user to accept, reject or modify the rules.

[0071] 17. The method according to embodiment 1, wherein the rules are manually defined by the user through the GUI.

[0072] 18. The method according to embodiment 1, wherein the rule includes the definition of the triggering event, and wherein the triggering event is based on time or associated with a change in the value or state of at least a subset of the selected data objects.

[0073] 19. The method according to embodiment 18, wherein the rule further includes the definition of conditions for performing the action.

[0074] 20. The method according to embodiment 18, wherein the rule further includes the definition of the action.

[0075] 21. The method according to embodiment 20, wherein the action is selected from adding an observer, updating a field, sending a notification, posting a comment, assigning to a user or group, and creating a record.

[0076] 22. The method according to embodiment 1 further includes displaying the selected data object conforming to the data storage model within the portal of the GUI.

[0077] 23. The method according to embodiment 22 further includes modifying the value of at least one of the selected data objects through the GUI, and automatically updating the value of the corresponding selected data object in the data cloud configuration through an API connection.

[0078] 24. The method according to embodiment 22 further includes receiving instructions via the GUI for performing an operation on at least one of the selected data objects, and performing the operation on at least one of the selected data objects in the data cloud configuration without using an extract, transform, and load (ETL) data integration process.

[0079] 25. The method according to embodiment 24, wherein the selected data object includes transactional data or streaming data, and wherein performing the operation further includes caching intermediate results via the data-driven workflow platform.

[0080] 26. According to the method of embodiment 1, the triggering event of the selected data object includes changes to the selected data object stored in the data cloud configuration.

[0081] 27. The method according to embodiment 1, wherein the flow is identified from a plurality of predefined workflows by means of a large language model (LLM).

[0082] 28. The method according to embodiment 27, wherein the stream is identified at least in part based on the data patterns of the selected data objects stored in the data cloud configuration.

[0083] 29. The method according to embodiment 27, wherein the output of the LLM includes a list of instructions for creating the stream.

[0084] 30. According to the method of embodiment 1, the data pattern of the selected data object stored in the data cloud configuration is converted into a graphical representation and displayed on the GUI.

[0085] 31. The method according to embodiment 1, wherein the GUI allows a user to modify the flow by using graphical elements corresponding to high-level logic including loop logic or if statement logic.

[0086] 32. The method according to embodiment 1, wherein the selected data object is found by setting logical rules or machine learning-based rules on the data objects stored in the data cloud configuration.

[0087] 33. The method according to embodiment 32, wherein the logical rules or the machine learning-based rules are set via a GUI.

[0088] 34. A system for providing a data-driven workflow platform, the system comprising: The first module is configured to operatively couple the data-driven workflow platform to one or more data clouds; The second module is configured to map selected data objects to the data storage model of the data-driven workflow platform, wherein the selected data objects are stored on the one or more data clouds; and A visualization module is configured to display a flow on a graphical user interface (GUI) for building cloud applications using or managing the selected data objects, wherein the flow includes at least one graphical element that corresponds to a rule for automating actions triggered by triggering events of the selected data objects.

[0089] 35. The system according to embodiment 34, wherein the first module manages granting the data-driven workflow platform one or more permissions for accessing, processing, and editing data objects stored on the one or more data clouds.

[0090] 36. The system according to embodiment 34, wherein the selected data object is mapped to the data storage model by defining connections between the selected data object and elements of the data storage model.

[0091] 37. The system according to embodiment 34, wherein the visualization module is further configured to display a second GUI that allows a user to define relationships between elements of the data storage model.

[0092] 38. The system according to embodiment 35, wherein the second GUI allows the user to link one or more data fields of a first selected element of the data storage model to one or more data fields of a second selected element of the data storage model.

[0093] 39. The system according to embodiment 37, wherein the relationship is automatically generated by the data-driven workflow platform and displayed as a recommended relationship on the second GUI.

[0094] 40. The system according to embodiment 34, wherein the second module is configured to further identify missing elements from the data storage model and prompt the user to identify another set of data objects for the missing elements.

[0095] 41. The system according to embodiment 34, wherein the data storage model includes multiple types of data, and the multiple types of data include at least one of task type, application type, transaction data type and element data type.

[0096] 42. The system according to embodiment 41, wherein the second module is configured to further map the selected data object to a transaction data type or an element data type.

[0097] 43. The system according to embodiment 34, wherein the interactive flow allows a user to add, remove or modify one or more components of the cloud application by dragging and dropping one or more graphical elements onto the interactive flow.

[0098] 44. The system according to embodiment 43, wherein the interaction flow includes a pre-built template flow that prompts the user to add, remove or modify one or more components.

[0099] 45. The system according to embodiment 44, wherein the pre-built template stream is automatically determined based at least in part on the selected data object and the cloud application.

[0100] 46. ​​The system according to embodiment 34, wherein the rules are automatically generated at least in part based on one or more data fields added to the interaction stream.

[0101] 47. The system according to embodiment 46, wherein a model is used to automatically generate the rules, and wherein the model is developed using rules extracted from past actions and previously processed data.

[0102] 48. The system according to embodiment 47, wherein the rules are recommended to a user on the GUI, and wherein the at least one graphical element allows the user to accept, reject or modify the rules.

[0103] 49. The system according to embodiment 34, wherein the rules are manually defined by the user through the GUI.

[0104] 50. The system according to embodiment 34, wherein the rule includes the definition of the triggering event, and wherein the triggering event is based on time or associated with a change in the value or state of at least a subset of the selected data objects.

[0105] 51. The system according to embodiment 50, wherein the rule further includes the definition of conditions for performing the action.

[0106] 52. The system according to embodiment 50, wherein the rule further includes the definition of the action.

[0107] 53. The system according to embodiment 52, wherein the action is selected from adding an observer, updating a field, sending a notification, posting a comment, assigning to a user or group, and creating a record.

[0108] 54. The system according to embodiment 34, wherein the visualization module is further configured to display the selected data object conforming to the data storage model within the portal of the GUI.

[0109] 55. The system according to embodiment 54, wherein the value of at least one of the selected data objects is modified by the GUI, and the value of the corresponding selected data object in the data cloud configuration is automatically updated by the first module.

[0110] 56. The system according to embodiment 34, wherein the first module is configured to translate instructions for performing an operation on at least one of the selected data objects received through the GUI into database operations executable in the data cloud configuration.

[0111] 57. The system according to embodiment 56, wherein the database operation is performed on the selected data object in the data cloud configuration without using an extract, transform, and load (ETL) data integration process.

[0112] 58. The system according to embodiment 57, wherein the selected data object includes transaction data or streaming data, and wherein the data-driven workflow platform is configured to cache intermediate results for performing the operation.

[0113] 59. The system according to embodiment 34, wherein the triggering event of the selected data object includes changes to the selected data object stored in the data cloud configuration.

[0114] 60. The system according to embodiment 34, wherein the data pattern of the selected data object stored in the data cloud configuration is converted into a graphical representation and displayed on the GUI.

[0115] 61. The system according to embodiment 34, wherein the GUI allows a user to modify the flow by using graphical elements corresponding to high-level logic including loop logic or if statement logic.

[0116] 62. The system according to embodiment 34, wherein the selected data object is found by setting logical rules or machine learning-based rules for data objects stored in the one or more data clouds.

[0117] 63. The system according to embodiment 62, wherein the logical rules or the machine learning-based rules are set via a GUI. Detailed Implementation

[0118] While various embodiments of the invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many variations, modifications, and substitutions will occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed.

[0119] Some definitions Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0120] Throughout this specification, references to "some embodiments" or "embodiments" mean that a particular feature, structure, or characteristic associated with that embodiment is included in at least one embodiment. Therefore, the phrases "in some embodiments" or "in an embodiment" appearing in various places throughout this specification do not necessarily refer to the same embodiment. Furthermore, in one or more embodiments, specific features, structures, or characteristics may be combined in any suitable manner.

[0121] As used herein, the terms “component,” “system,” “interface,” “unit,” etc., are intended to refer to computer-related entities, hardware, software (e.g., in execution), and / or firmware. For example, a component can be a processor, a process running on a processor, an object, an executable file, a program, a storage device, and / or a computer. For instance, an application running on a server and the server itself can be components. One or more components may reside within a process, and components may be centralized on a single computer and / or distributed across two or more computers.

[0122] Furthermore, these components can be executed from various computer-readable media on which various data structures are stored. Components can communicate via local and / or remote processes, such as based on signals having one or more data packets (e.g., data from a component that interacts with another component in a local system, a distributed system, and / or with other systems across networks (e.g., the Internet, a local area network, a wide area network, etc.) via signals).

[0123] As another example, a component can be a device having specific functions provided by mechanical parts operated by electrical or electronic circuitry; the electrical or electronic circuitry can be operated by a software application or firmware application executed by one or more processors; the one or more processors can be internal or external to the device and can execute at least a portion of the software or firmware application. As yet another example, a component can be a device providing specific functions through electronic components without mechanical parts; the electronic components may include one or more processors to execute software and / or firmware that at least partially endow the electronic components with functionality. In some cases, the component can be simulated by a virtual machine (e.g., within a cloud computing system).

[0124] Whenever the term "at least," "greater than," or "greater than or equal to" precedes the first value in a series of two or more values, the term "at least," "greater than," or "greater than or equal to" applies to each value in that series. For example, greater than or equal to 1, 2, or 3 is equivalent to greater than or equal to 1, greater than or equal to 2, or greater than or equal to 3.

[0125] Whenever the term "not exceeding," "less than," or "less than or equal to" precedes the first value in a series of two or more values, the term "not exceeding," "less than," or "less than or equal to" applies to each value in that series. For example, less than or equal to 3, 2, or 1 is equivalent to less than or equal to 3, less than or equal to 2, or less than or equal to 1.

[0126] For example, as used herein, a processor includes one or more processors, such as a single processor, or multiple processors in a distributed processing system. The controller or processor described herein typically includes tangible media to store instructions for implementing steps of a process, and, for example, the processor may include one or more of a central processing unit, programmable array logic, gate array logic, or field-programmable gate array. In some cases, one or more processors may be programmable processors (e.g., a central processing unit (CPU) or microcontroller), digital signal processors (DSPs), field-programmable gate arrays (FPGAs), and / or one or more advanced RISC machine (ARM) processors. In some cases, one or more processors may be operatively coupled to a non-transitory computer-readable medium. The non-transitory computer-readable medium may store logic, code, and / or program instructions executable by one or more processor units to perform one or more steps. The non-transitory computer-readable medium may include one or more storage units (e.g., removable media or external storage devices, such as SD cards or random access memory (RAM)). One or more methods or operations disclosed herein may be implemented in hardware components or combinations of hardware and software, such as, for example, ASICs, special-purpose computers, or general-purpose computers.

[0127] Furthermore, the word “exemplary” is used herein to mean used as an example, instance, or illustration. Any aspect or design described herein as “exemplary” is not necessarily to be construed as preferred or superior to other aspects or designs. Rather, the use of the word “exemplary” is intended to present concepts in a concrete manner. As used in this application, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless otherwise stated or clear from the context, “X adopts A or B” is intended to mean any natural inclusive arrangement. That is, if X adopts A; X adopts B; or X adopts both A and B, then in any of the foregoing, “X adopts A or B” holds true. Furthermore, the articles “a” and “an” used in this application and the appended claims should generally be interpreted as meaning “one or more” unless otherwise stated or clearly indicated from the context as singular.

[0128] Cloud Service Overview Figure 1An example of storing enterprise data in a traditional database system is illustrated. As this example shows, typically, one or more users within the enterprise (2, 4, 6; these users may be the same user operating through a separate system, or may represent three different users operating on separate systems) will establish separate user sessions (10, 12, 14) with each connected (66, 68, 70; 72, 74, 76) system (e.g., 16-ERP system; e.g., 18-TMS system; e.g., 20-RMS system), and the one or more users (2, 4, 6) have the credentials and permissions for access and use. Typically, each system (16, 18, 20) can be operatively coupled (78, 80, 82) to one or more database systems (22, 24, 26), which are configured to store relevant data and utilize such data for sorting, report generation, and / or calculations, such as dynamically processing requests issued through interconnected systems (16, 18, 20). Enterprises using this configuration typically possess specific IT resources available for maintaining, updating, and resolving various aspects of the database / computing system (22, 24, 26). Such enterprises also face inherent operational risks and inefficiencies, which are related to the proprietary and secretive nature of many ERP / database / computing configurations, as will be discussed in further detail below.

[0129] Next-generation configurations have emerged, in which enterprise data and computing resources are becoming increasingly separated. For example... Figure 2 As shown, cloud-based repository providers (e.g., Snowflake (RTM)) continue to leverage traditional ERP / database / computing configurations (such as...) by providing systems... Figure 1 (as shown) to gain market share, in which data cloud systems configured for specific enterprises (34) are built to essentially separate the enterprise’s data from core computing resources that can reside in mutually coupled (96, such as through high-throughput connections) scalable computing configurations (36) (such as scalable computing configurations available from Amazon (RTM), Google (RTM) and Microsoft (RTM) under the trade names Amazon Web Services (RTM), Google Cloud (RTM) and Azure (RTM).

[0130] like Figure 2As shown, one or more users within an enterprise (2, 4, 6; such users may be the same user operating through a single system, or may represent three different users operating a single system) can utilize one or more computing sessions (10, 12, 14) to operate one or more connected systems (16, 18, 20), which can be coupled (84, 86, 88; 90, 92, 94) to a data cloud configuration (34). Many such systems, such as Figure 2 The systems shown (16, 18, 20) will typically still require separate databases (28, 30, 32) to maintain a considerable level or amount of enterprise data (such as through traditional system integration, such as application programming interfaces (or “APIs”), batch tables, XML scheduling, etc.) in order to be operational, and therefore even if some of the enterprise’s data (such as reporting and / or audit data) can be stored in a data cloud (34), then copied from the data cloud (34) and operational computing provided by a scalable computing configuration (36) coupled to each other (96), the data and data processing will typically still be distributed across other different systems (18, 20, 22), which again brings various drawbacks to such enterprises in terms of efficiency, complexity, cost and risk management.

[0131] Recently, cloud services and SaaS (Software as a Service) have offered enterprise computing resources that are more scalable, functional, efficient, scalable, and less isolated while remaining secure. This is particularly relevant in typical modern enterprise scenarios where companies address a wide range of challenges, such as supply chain difficulties, where the number of disparate information fragments from different systems can be substantial. These fragments are often manually integrated and processed to make timely and informed business decisions. For example, ... Figure 3 As shown, it may not be uncommon for a typical enterprise that manufactures complex technology products to attempt to extract information from multiple traditional integrated systems (16, 18, 20) and / or SaaS (38) systems (e.g., software for checking approved purchase orders for key components of goods to be manufactured, and software for checking shipment / transportation status, operational risks, payment status and relevant weather data) to understand whether a particular shipment will indeed arrive at the appropriate manufacturing facility on time to help the manufactured goods be shipped in a timely manner during a particular holiday.

[0132] Perhaps more importantly, even in scenarios where a sufficient number of users / operators can participate in real-time discussions to address such complex and multifaceted problems, they may bring data from disparate systems that are unlinked, uncoordinated, possibly not updated in real-time or near real-time, and not yet aided in decision-making based on numerous inputs through business process analytics. In other words, such a discussion might involve 30 operators, each with their own perspective and data from different systems (some of which may not be within the enterprise firewall), each wanting to join a real-time discussion about the current problem and potential solutions. This paper describes systems and methodologies for the operation, management, and automation of business processes, configured to address these and other operational challenges in modern enterprises.

[0133] refer to Figure 3 It shows the relationship with Figure 2 The enterprise configuration shown is similar to an enterprise configuration in which one or more so-called "Software as a Service" (or "SaaS") systems (38) are added, which are configured to allow users (8) to participate in the SaaS configuration (38), such as customer relationship management (CRM), enterprise resource planning (ERP), content management system (CMS), project management software, sales, marketing, or e-commerce software (e.g., Salesforce (RTM), Adobe Creative Cloud (RTM), ServiceNow (RTM), etc.). Such systems typically utilize intercoupled (100) SaaS data systems (40) (such as databases) specifically configured to facilitate the operation of the SaaS configuration (38) by extracting certain data from the intercoupled data cloud configuration (34) (such as through traditional system integration as discussed above in reference to interconnected systems (16, 18, 20)). Figure 2 As shown in the system configuration, even if some of the enterprise’s data will be located on the data cloud (34) and the operational computing will be provided by the scalable computing configuration (36), some data will still be distributed on other different systems (28, 30, 32, 40), which again brings various drawbacks to such enterprises in terms of efficiency, complexity, cost and risk management.

[0134] Service Management Cloud System (Data-Driven Workflow Platform) This disclosure provides an improved service management cloud system or cloud-native SaaS platform for no-code applications with data-driven workflows that leverage cloud data. The service management cloud system described herein can provide configurable and automated data-driven workflows through no-code applications. The service management cloud system can be natively integrated into any data cloud and can allow configurable applications for workflows or processes without requiring ETL (Extract, Transform, Load) or ELT (Load and Transform in a Data Warehouse). The terms "service management cloud" or "cloud-native SaaS platform" may also be used as "data-driven workflow platform," and they are used interchangeably throughout this specification.

[0135] Figures 4-7 The diagram illustrates various configurations in which the service management cloud system (44) can be configured to have direct interconnectivity (104, 106) between the user (8) and the data cloud configuration (34). The service management cloud system (44) can be specifically configured to operate without requiring the migration of large amounts of data from the data cloud configuration (34) to other systems, while also providing visibility and usability to the user (8) through the service management cloud (44) to manage business activities and processes in an efficient and scalable manner, as described in further detail below. See below for reference. Figure 9 To describe in further detail, for example, the service management cloud system (44) can be specifically configured not only to provide efficient and globally controlled access to a variety of interconnected systems and data through the use of appropriately granted privileges, but also to connect these data and systems so that data is available to the service management cloud system (44) with similar efficiency and latency as it might be local to the service management cloud system (44). In other words, during a given session, the service management cloud system (44) and its coupled resources (34, 36) can be configured to make the target data “functionally native” to the session of the subject service management cloud system (44) – and this provides the enterprise with significant additional opportunities to leverage the data while ensuring that the data continues to be updated (e.g., in real-time or near real-time) and continues to reside fully or at least primarily on the data cloud (34).

[0136] refer to Figure 4 The diagram illustrates the enterprise data configuration, including traditional connection business systems (16, 18, 20), such as... Figure 2The system shown is kept in place (i.e., within the data cloud) to assist one or more given users (2, 4, 6) in traditional operations through sessions (10, 12, 14) with such systems (16, 18, 20) and their connected data (28, 30, 32; 34), and wherein a separate service management cloud system (44) is configured to provide direct access to the mutually coupled (106) data cloud configuration (34), so that users (8) of the service management cloud system (44) can not only examine the information contained on the data cloud configuration (34) in the form of views, reports, etc. returned by queries without migrating data from the data cloud configuration (34) to users (8), but also wherein users (8) can create, operate, and manage business processes by utilizing the combined interconnect resources of the service management cloud system (44), the data cloud configuration (34), and the associated scalable computing configuration (36) without migrating data from the data cloud configuration (34) to users (8), as described below, such as references. Figure 9 Further discussion is needed.

[0137] For the sake of simplicity, Figure 5 The illustration shows a traditional enterprise system that does not have integration (e.g., Figure 4 Variations of 16, 18, 20). Such configurations may appear in paradigms where the legacy configuration has been migrated to the service management (44) and data cloud (34) configurations, or where the legacy functionality has been replaced by the available functionality of the service management (44) and data cloud (34) configurations.

[0138] Figure 6 An embodiment is illustrated in which three separate data cloud configurations (34, 52, 54) are shown interconnected (106, 110, 112) between a service management cloud system (44) and three separate interconnected (96, 114, 116) scalable computing configurations (36, 48, 50), which may be maintained by different and / or differentiated providers (e.g., Amazon Web Services (RTM), Google Cloud (RTM), and / or Azure (RTM)). This configuration illustrates how a single user (8) can utilize a single instance of the service management cloud (44) to inspect and control data from various different interconnected systems and perform computational operations on that data, as referenced below. Figure 9Further described, it also does not heavily rely on pulling data from such a system to the user (8), but depends to some extent on the data cloud configuration (34, 52, 54). For example, in embodiments where a particular data cloud configuration has remote compute management features (such as those provided by Snowflake (RTM) under the trade name "Streams" (RTM)) or where a suitable adapter has been built in its location, data manipulation language ("DML") changes made to tables, catalog tables, external tables, or underlying tables in one or more views (including security views) can be recorded for a given source object, thereby allowing traceable remote operation or manipulation of Snowflake data cloud configuration instances. Such streaming configurations can be used to provide the service management cloud (44) with access to data within one or more data cloud configurations (34, 52, 54), and access to compute manipulation of these data via one or more associated interconnects (96, 114, 116) of scalable compute configurations (36, 48, 50).

[0139] refer to Figure 7 One or more interconnected adapter (58, 60, 62) modules (120, 122, 124; 126, 128, 130) modules can be configured to assist the service management cloud (44) in specific uses of the subject data cloud configuration (34, 52, 54), such as assisting with functions related to leveraging scalable computing configurations (36, 48, 50) for as many associated computations as possible. The adapter can make data hosted in the data cloud (e.g., Snowflake) appear as native data within the service management cloud platform. For example, during configuration or integration of target data between the data cloud (e.g., Snowflake) and the service management cloud, the adapter can set up mappings and data type matching without changing the data or imposing any type of modification on the data within the data cloud. Details regarding adapters, data type matching, allocation, and data mapping (setting up connections to data sources) are described later in this document.

[0140] As mentioned above, many multi-factor modern business challenges require not only personnel, information, and expertise from various people and sources within a specific organization, but also from various people and sources from other (i.e., external) organizations. For example, a typical enterprise may contract for various aspects of its logistics operations. To understand and address specific urgent business challenges that may involve logistics, enterprises may need to involve personnel and information from external logistics service providers. Traditionally, this involvement may require email, conference calls, telephone, and many people. A key benefit of the Subject Service Management Cloud (44) configuration is the enhanced ability to bring people into collaborative processes with specific and controlled access levels, regardless of whether they are within a specific organization, department, or security department in a broader sense.

[0141] Service management cloud platforms enable process sharing. Beyond securely sharing data within the data cloud, participants or different entities involved in a workflow can also share processes. For example, a supply chain team can use the platform described in this article to collaborate with partners on the same data within the same workflow (directly processing the same data with partners). The platform provides an interface for viewing, accessing, and managing process data such as tasks, assignments, and reminders, all protected within the data cloud. References Figure 8 The service management cloud (44) can be configured (132) to allow pre-established or application-defined login permissions (134) that can provide specific access roles or levels (e.g., full global access, organization only, application only, or even limited to a single record) (136). The service management cloud (44) can also be configured to allow access to the data cloud (34) and associated computing resources (such as... Figure 4 36) Connect and access information appropriately.

[0142] Furthermore, by leveraging precise access and tracking through records, applications, organizations, roles, etc., access to every specific aspect of enterprise data and systems can be tracked and audited (140). For example, reports, user interface dashboards, or notifications can be set up to allow administrators configured with the service management cloud (44) to easily understand who accesses what across the system through real-time or near real-time updates. Reference Figures 13-15 This illustrates another aspect of access management, control, and collaboration. (Reference) Figure 13 The diagram illustrates a hierarchical configuration (186), which, as described above, can be used to help administrators of the service management cloud (44) configuration provide very specific access to various aspects of the system, such as based on a single record (194), application (192), organizational base (190), or global (188), but subject to appropriate limitations. Therefore, for example, refer to... Figure 14 This configuration (202) facilitates collaboration between one or more parties both within and outside a given organization. The illustration shows a user (“John Smith” 204) with an internal role (212) within a given company organization and appropriate access (214) to the company's service management cloud (44). (See reference...) Figure 8 Regarding the access configurability discussed, John Smith (204) may also be granted separate and different access permissions to external resources of the partner organization’s service management cloud (44), such as based on his role in that external partner organization (206) or based on the basis of a specific application (208). Figure 14The illustration also shows that John Smith (204) can have limited access to a single record (210) within a third organization's service management cloud (44). Thus, John Smith (204) can use the service management cloud's theme configuration to easily and efficiently collaborate with people, processes, and data from three or more organizations securely and in real-time or near real-time through the cloud without having to log in and out of multiple systems.

[0143] Figure 15 The diagram illustrates how this service manages the cloud (44) configuration (216), and users (such as John Smith) Figure 14 Element 204) can be easily switched between organizations for collaboration. In other words, "bringing someone from another organization to help solve this urgent / specific problem" becomes highly efficient, secure, and controllable, and can be automated in many ways, as further described below. Furthermore, the service management cloud (44) can be configured to be platform-independent, making it accessible and usable from any web interface, thus allowing appropriate users to manage any platform from anywhere, typically from a secure data center (such as...). Figure 4 The embodiment supports the powerful computing capabilities of the scalable computing configuration (36) that is operably coupled (96) to the data cloud (34).

[0144] refer to Figure 9 Leveraging a powerful, precise, and convenient paradigm for managing access, operators can not only visualize real-time or near real-time updated data, but also utilize that data in new ways across a wide variety of business processes with varying degrees of automation. For example... Figure 9 As shown, under appropriate access restrictions, data becomes feature-native data for further use. As stated above, the concept of feature-native refers to the fact that the service management cloud (44) can be configured to present a given user with access to continuously updated data in real-time or near real-time, with latency and access levels as if the data resided in its local computing operations, even though the data typically actually resides on the data cloud (34) and is powered by significant scalable computing configurations (such as...). Figure 4 Element 36) supports this. With appropriate permissions, and with the efficient availability of updated data, it can be used for various in-session operations (146), such as creating reports or notifications, various types of calculations, auditing, searching, analysis, sequential and / or logical exploitation, process automation, etc. (150). Furthermore, with appropriate permissions, data can be written back (150), so that changed or new data is stored on the data cloud and can be used to update other interconnected systems and their databases.

[0145] refer to Figure 10An extended illustrative view of the Service Management Cloud (44) configuration (152) is shown, in which many operations can be efficiently performed using the Service Management Cloud (44) on a platform-independent basis, given native access to the data. For example, cloud applications (“Apps”) can be created to perform various operations repeatedly or one-off, such as the following functionally possible operations: “Display all current suppliers in Japan” (154); “Determine the quantity of assemblies in the finished goods inventory of factory #522” (156); “Prepare a report that introduces the SKU superset to be received in December” (158); “Return the total monthly sales cost from production line #12” (160); and “Show all delayed purchase orders since January” (162).

[0146] Regarding the utilization of data that is functionally native during a given session on the service management cloud (44), the system can be configured to deliver data to a given user's session based on factors such as: the platform the user uses to access the service management cloud (44) (e.g., a smartphone-based platform may not have the throughput or data reception capabilities of a powerful desktop workstation); the quality of the connection between the user's client device and the service management cloud (44); the bandwidth or latency of the connection between the user's client device and the service management cloud (44); and / or the user's client device relative to the data cloud (e.g., such as...). Figure 4 Element 34 may allow users to configure a specific session in the service management cloud (44) to prioritize local data closest to the user and scalable computing configurations (such as...). Figure 4 The position of element 36). In other words, the service management cloud (44) can be configured to automatically adjust the delivery of data to user sessions based on various factors to enhance usability and generally support users in collaboration and other business operations.

[0147] refer to Figure 11The diagram illustrates a session configuration (164) for the Service Management Cloud (44), where feature-native data (144) can be used to automate complex business processes. For example, the Service Management Cloud (44) can be configured to automatically run processes that utilize available data, such as: “If any SKU contains metadata ‘dangerous,’ mark it in a report and send the report to the regulatory authorities” (166); “If any goods appear to be delayed by more than 20 days during December, execute remediation / replacement logic, notify the controller and legal department, and send the remediation / replacement terms to the legal department via email” (168); “If the purchase is made in China, and if the SKU is hardware, contact Chinese customs and provide a goods list” (170); “If the valuation figure has not yet been signed by an authorized person in the accounting department, send the goods list to the accounting department” (172); “On the first day of each month, search for all available information related to the reputation of all suppliers and send it to the ESG department” (174).

[0148] refer to Figure 12 The diagram illustrates a service management cloud (44) session configuration (176), where real-time or near real-time access (138) to feature-native data (144) can be used for write-back purposes (150). For example, the service management cloud (44) can be configured to write back to the feature-native data cloud (such as... Figure 4Element 34), which can be used to update other intercoupled systems as described above in the following example scenarios: "Include new metadata comments associated with this table: 'Data may be corrupted; several columns look the same; audit required'" (178); "Update the ETA (Estimated Time of Arrival) for goods from January 1 to January 5" (180); "Fix data in a specific row / column of this specific table: replace '2oo,100.55' with '200,100.55'" (182); "Increase the purchase quantity from 1,500 to 2,500" (184). This write-back can represent significant changes in operations and allows for efficient and secure navigation through an interface, immediately populating the data for other users, representing another key paradigm shift. Because the service management cloud is directly connected to data stored on the data cloud provider, and workloads or queries run in the data cloud, source data can be updated and modified in the data cloud, and / or new data can be added to the data cloud (e.g., when an action requesting data updates is performed in an automated setup). Service management clouds can provide alternative capabilities for making direct API calls to cloud services (such as Salesforce) to perform actions (such as adding new data records to a new column or table in the data cloud) or update source data. The platform can be able to write back to cloud services (such as Salesforce), write back directly to source systems (such as ERP, CRM, CMS, etc.), or a combination of both. In some cases, the platform can allow users to configure write-back preferences or permissions. For example, a user can configure write-back to be enabled for both the cloud service and the connected source system. Alternatively, a user can configure write-back to be enabled only for the cloud service.

[0149] refer to Figure 16 As described above, on a platform-independent basis, leveraging secure and efficiently managed access (138), a service management cloud (44) can be used to make additional data available on a native basis, as shown in (220): 1. Log in using credentials to connect to the data cloud; 2. Select the relevant table to connect to; 3. Add details such as name, handle, and / or description to the new element; 4. Map the fields by matching the table fields in the data cloud with the record fields in the element. Reference Figure 17 This process is illustrated in the view of the Service Management Cloud (44) session user interface (Set Credentials 222; Connect to Table 224; Associate Element Details 226; Configure Field Mapping 228).

[0150] refer to Figures 18-20 The illustration shows several use cases for configuring and using the Service Management Cloud (44) variant in complex business processes with varying levels of automation.

[0151] refer to Figure 18Environmental, social, and governance (“ESG”) scores and their monitoring have become a key priority for many business organizations. Data can be used in many forms from many sources, with varying levels of latency, certainty, and other critical factors, leading to a range of complexities within these organizations. Figure 18 The illustration depicts a scenario where an organization requires all its partners to provide ESG-related data in a specified format and in specified tables at specified locations, so that the data can be made available via the Service Management Cloud (44) with appropriate permissions. Thus, the ESG data is already placed in tables in a specified format, which can be accessed via the Service Management Cloud (44) with appropriate permissions (230). To facilitate efficient and automated use of the relatively standardized and predictable data from various partners, pre-existing applications can be created and configured to automatically (236) generate specified records or reports (232) based on connectivity (234) to the ESG data tables. Furthermore, the Service Management Cloud (44) can be configured to automatically flag suppliers or partners whose ESG scores may fall below specific predetermined or customizable thresholds and automatically deliver this information, such as written reports sent via email or electronic notifications sent to the Service Management Cloud (44) dashboard interface, smartphones, etc. (238).

[0152] refer to Figure 19 The illustration depicts an embodiment related to ESG analysis where the available data may not be homogeneous or standardized, but rather provided in a non-homogeneous form via a service management cloud (44) (240). In this case, operators of the service management cloud (44) can create custom applications to operate within the service management cloud (44) using both complex and simplified (the service management cloud's "no-code" and / or "drag-and-drop" configuration interface), instead of using or modifying one of several pre-created applications available on the service management cloud (44). Details regarding the user interface and system for workflow creation are described later in this document. See again Figure 19Users can leverage the application to create user interfaces (such as drag-and-drop features) to add chapters (such as phases, summaries, key details, solution code), add fields within each chapter and appropriately identify “required” fields (such as date, value (such as quantity or cost), name (such as owner-related fields), and add interactions (such as dialogues (i.e., multi-party chat); approvals; tasks; attachments; updated components) (244); by creating applications for capturing and processing data, workflow or process automation configurations can be created to automate ESG analysis and audit processes (such as: performing quality assurance analysis on updated data; calculating average E, S, and G scores for each vendor (if data is available); sending notifications to the ESG department (such as via connected devices, data-driven workflow platforms {such as via in-app notification centers or dashboards}, SMS); creating a second notification related to any vendor whose E, S, or G scores are below a specified threshold, and sending a second notification to the ESG and risk management department (246).

[0153] refer to Figure 20 The configuration of the Theme Services Management Cloud (44) can be used to automate business process challenges related to the supply chain. Specific buyers (such as large Fortune 500 entities) may require all suppliers / partners to accurately meet their delivery requirements (i.e., on time, not exceeding, not falling short of, not damaged, etc.) or they will be subject to a payable and undisputed penalty unless disputed within a relatively short time window after the penalty is issued. Due to the involvement of many operators inside and outside the specific supplier / partner organization (e.g., partner manufacturing, transportation, logistics personnel; supplier logistics personnel; potential information provided in the data cloud by external suppliers (such as Project 44, which can geographically track shipping containers, etc.), properly flagging and supporting potential penalty disputes can be very challenging (and in fact, therefore, many disputes may simply not be raised in a timely manner, resulting in significant operational costs for the various parties involved). In some embodiments, a custom application can be created to incorporate any proposed buyer deductions or penalties (262), original order information (264), relevant shipping information (266), information from partners (268), final goods / arrivals and other milestone information (270), and automatically (272) and efficiently create an information package to support penalty disputes with the buyer (274), and the information package can be automatically submitted to the buyer's dispute resolution portal through a workflow automatically generated by the service management cloud.

[0154] Leveraging additional data and experience gained from automatically resolving various business challenges, and utilizing the vast amounts of data continuously updated and aggregated using various instances of the Service Management Cloud (44), neural network configurations can be created to help users and organizations address various business challenges based on relevance, labeled data, heuristics and algorithms, and reinforcement learning models based on business objectives. Furthermore, the Theme Service Management Cloud (44) system can be configured to automatically identify gaps in various datasets, tables, and / or documents and seek to automatically bridge these gaps. For example, in one embodiment, where an application or process is configured to utilize certain information from a “Purchase Order” document (such as in a business process automation configuration) and where a given purchase order has all the necessary information but lacks the supplier’s actual mailing address, the system can be configured to identify the supplier based on a unique SKU or other field in the data and provide the supplier’s actual mailing address from other data linked to the supplier.

[0155] Data-driven automation As described above, the data-driven workflow platform presented in this paper allows for no-code automation of processes at various levels. In some embodiments, the platform can provide a graphical user interface (GUI) that allows users to configure, create, and manage automations to initiate workflows when data changes. In some cases, automations can be created by defining rules that automate actions triggered by triggering events of selected data objects. In some cases, rules may include definitions of triggering events, conditions for performing actions, and the actions themselves.

[0156] Figures 21-23 An example of a GUI for creating and / or editing automation is shown. For example... Figure 21 As shown, GUI 2100 allows users to create, modify, or edit automations with multiple configurable fields. For example, the automation GUI 2100 can provide at least three fields, including trigger 2101, condition 2103, and action 2105, thereby allowing for convenient configuration of trigger-condition-action type automations.

[0157] In some embodiments, automation can be data-driven. For example, each field (e.g., trigger 2101, condition 2103, and action 2105) can be configured with auto-populated values ​​or data fields. The auto-populated values ​​or data fields can be dynamically determined based on the connected data object 2017. For example, when setting up automation object 2107, a dropdown menu 2201 with dynamic population options (e.g., add attachments, based on time, update approvals) can be provided. Figure 22As shown. Users can be allowed to assign triggers based on record creation, status updates, data changes, quantity changes, value changes, or various other types of trigger events. Users can select from option list 2201 to set trigger events. In some cases, the options provided in drop-down menu 2201 can be dynamically changed based on the connected data object. For example, trigger options can indicate a data field (e.g., a column) on which a change can trigger an action. In another example, a trigger can include an action / operation performed in the connected cloud database (e.g., creating a new record).

[0158] In some cases, users may be allowed to define trigger conditions. Conditions can define specific values ​​or states for the trigger. For example, conditions could be a new phase, the number of days before or after the due date, or the number of days exceeding or falling below a threshold. Figure 22 As shown, the GUI can also allow users to set or define conditions via the Conditions panel 2203. The Conditions panel 2203 can provide data fields with auto-fill options, such as filter conditions 2205. Users can select the column to which filtering is applied from the list of options provided in the drop-down menu 2205. In some cases, the option list can be automatically populated based on the connected data objects. Users can be allowed to further define the filtering conditions (e.g., no value, greater than, equal to, less than, between, greater than or equal to, less than or equal to, etc.) via the Conditions panel 2203. For example, users can define thresholds 2207 and relationships (e.g., equal to) to apply filtering. In some cases, users can create compound conditions (e.g., condition groups) to combine multiple conditions (e.g., filter conditions) 2209 using operators (e.g., AND, OR) 2208. The GUI 2203 can also allow users to create complex conditions, such as by adding conditions or condition groups 2211. Condition groups can be added using any suitable operation (e.g., AND). The triggering events and conditions can then be translated into a query language (e.g., Structured Query Language (SQL)) compatible with database technologies supported by the connected data cloud. In some cases, triggers and triggering conditions can be implemented using the platform's data mining features. For example, data mining capabilities can automatically detect changes in data defined by triggering events and conditions. Details regarding data mining features are described later in this article.

[0159] Figure 23An example GUI for user-created actions is shown. Actions can be associated with assigning owners, escalating alerts, updating selected data fields, placing orders, and a variety of other actions. As shown in the example, users can select actions from a dropdown menu 2302 that presents a list of action options. Action options can be dynamically determined based on the connected object. As shown in the example, actions can include, but are not limited to, adding observers, creating output APIs, creating records, posting comments, sending notifications, updating fields, assigning to users, assigning to groups, etc. In some cases, actions can involve adding or modifying data directly in the connected data cloud. For example, the execution of an action can directly call an API to a cloud service (e.g., Salesforce) to perform an action (e.g., adding a new data record to a new column or table in the data cloud) or update a source data object (e.g., updating field 2303). This automatic write-back capability, as described elsewhere in this document, can beneficially allow for reduced latency and increased efficiency without the transformations or data cleansing required by traditional ETL.

[0160] In some cases, the list of options used to define triggers and / or actions can be fixed across different connected data objects. For example, trigger options and / or actions can be pre-built based on industry knowledge and expertise. For example, trigger options and / or actions can be built based on connected data cloud monitoring services (e.g., available API calls). Alternatively, an auto-populated list of options for defining actions and / or triggers can be dynamically provided based on selected data objects. The auto-populated list of options can be determined based on predetermined rules, industry knowledge and expertise, and / or data patterns extracted from past data. For example, different action options can be mapped to different types of data objects. In some cases, the list of action options can be dynamically provided based on past behavior associated with users, organizations, industries, etc. For example, the action menu for the first user / industry can differ from the action menu presented to the second user / industry based on past data associated with that user / industry. In some cases, action options can be dynamically provided based on time. For example, different menus or options can be provided based on different times of the year (e.g., different months, different seasons, etc.).

[0161] Figure 54 and Figure 55An example of a GUI for an automated flow is shown. As mentioned above, an automated flow is a no-code automation platform that allows access to system functions and control flow on data cloud data without requiring programming expertise. The system described in this paper can provide available variables for each action in the automated flow. Users can use functions presented through the GUI, such as 'link' buttons and / or operators (e.g., the $ operator), to identify variables outside the automation (e.g., variables from a previous step in the automated process) and use these variables in one or more actions. Variables can come from raw data and / or intermediate data generated by any step of the automated process.

[0162] Figure 54 An example of a GUI that allows the creation of custom payloads in API integration using operators (e.g., the $ operator) 5401 is shown. The API can allow sending custom messages to external APIs (such as the Slack API) based on dynamic variables from automations. The GUI can also allow selection of linked values ​​from automations. For example, a dropdown menu 5403 can display filtered options (only valid options (data fields)) to help ensure that automations can run successfully. As shown in the example, a user can select a reference value from the dropdown menu to trigger the logging variable.

[0163] A GUI can also allow users to control automated flows through high-level logic without requiring coding or programming skills. For example... Figure 55 As shown, the GUI allows users to access data in the data cloud to set or change trigger 5501 to initiate automation. The system can provide one or more high-level logics in a visual manner. The high-level logic provided by the system is intuitive and requires no coding skills. For example, logic such as loops and if statement logic can be provided as loops and branches on the GUI so that users can control the flow of automation. Users can use high-level logic on automation variables to control the flow of automation by selecting dynamic variables, logical operators (such as equal to or less than / greater than), and another dynamic variable to compare. Figure 55 An example of a GUI for controlling an automated flow using visual features such as loop 5503 and branch 5505 is shown. As shown in the exemplary flow, the loop action 5503 in the automation can control the flow to search for all valid records and run sub-workflows 5507 one by one on each record found in the record search. The automation flow can then use the branch action 5505 to perform if statement logic checks to perform other actions based on the checks found in the path 5509 of the branch.

[0164] The system's GUI can hide complex programming concepts (such as types and variable ranges) from the user, making it easier for them to utilize data in automation. It provides intuitive functionality, and the system can automatically determine the associated complex programming concepts. For example, the user can provide input, such as referencing a list of data and / or running a subroutine for each item in the list, while the types and variable ranges are automatically determined by the system based on the user input.

[0165] In some embodiments, in addition to a GUI for creating or defining automation, the data-driven workflow platform described herein can also leverage artificial intelligence (AI) technologies to provide intelligent automation or automation recommendations. For example, an AI model can be trained using past actions, conditions, and trigger data, as well as connected data objects. Once trained, the AI ​​model can automatically determine trigger conditions (e.g., condition values) and / or actions to provide recommendations to the user.

[0166] Dynamic relationships and data models The data-driven workflow platform described in this article allows users to create dynamic relationships within data. This ability to create dynamic relationships beneficially allows for flexible rules to connect data elements together. For example, users can understand that certain elements (such as master data and transaction data) are related: goods are related to ports of entry, SKUs are related to POs, and computers are related to suppliers. When something happens upstream (e.g., upstream triggers automation), this upstream data can be used to identify downstream impacts, thereby avoiding delays in identifying impacts (e.g., days or weeks).

[0167] In some embodiments, relationships can be created by connecting data models or elements of data models within the platform. As described above, a data-driven workflow platform may include adapters configured to connect to data objects in a cloud repository. Adapters can allow users to map fields between the platform's stored data model and table fields in the data cloud. The stored data model within the platform may include different types of datasets. In some cases, for example, different types of data may include things like "elements," "tasks," "applications," etc. Adapters can make data hosted in a data cloud (e.g., Snowflake) appear as native data within the platform. For example, such as... Figure 17As shown, the adapter can provide a GUI that allows users to set mappings and data type matching. For example, users can assign data types (e.g., transaction, element, application, etc.) to data fields or tables of data hosted in the data cloud. For instance, to create an ESG application, the adapter can connect to a table stored in the data cloud, and the platform can automatically identify elements relevant to the ESG application, such as vendors, products, economy, environment, labor, society, etc., and display a GUI with auto-populated fields, allowing users to assign data types to the extracted elements. For example, users can assign data type element types to vendors and products (e.g., master data / static data), or assign element types to economy, environment, labor, and / or society (e.g., transaction data / streaming data). This operation can be performed without applying any modifications or changes to the data in the data cloud.

[0168] Relationships can be created between storage data models within the platform. The storage data models provided by the platform can dynamically map relationships within cloud data. For example, a relationship can be created between "Element" type data named "Product" (which contains a complete list of all products manufactured or sold by the customer) and "Transaction" type data named "Inventory Location" (indicating the quantity of each product on hand for the customer).

[0169] In some cases, users can manually create relationships through the GUI provided by the platform. Figure 24 and Figure 25 An example of a GUI for creating or adding relationships is shown. Figure 24 As shown, the GUI 2400 can provide users with fields to create relationships (e.g., equality) by selecting one or more data fields 2403 of a first data model or element 2401 of the first data model and selecting one or more data fields 2405 of a second data model or element 2407 of the second data model. Field name options can be automatically populated in a dropdown menu 2409 of the selected object. Figure 25 As shown, relationships can be created between two objects of various data types defined within the platform. For example, object 2501 can be an application type, an element type, or a transaction type. After selecting object 2501, the associated data field 2503 can be provided for selection in the drop-down menu.

[0170] In some cases, relationships can be created automatically without user intervention. For example, a platform can analyze its stored data models and suggest automatically created relationships. For instance, a platform can automatically identify that a data model "Inventory" with a column named "sku" should be related to a data model "Product" with a column named "sku". The platform can generate suggestions to users to set recommended relationships. Users can choose to accept, reject, or modify the recommended relationships. In some cases, the platform can develop AI models for automatically identifying relationships. Alternatively, relationships can be identified based on predefined rules (e.g., based on public identifiers, expert knowledge, or other criteria). The platform can also allow users to manage and share all relationships created for one or more applications. Users can view relationships in real time, dynamically modify relationships at any point in time, and make decisions based on multi-level organization.

[0171] No-code application creation As described above, the data-driven workflow platform provides a no-code configuration interface for creating cloud applications. The platform allows users to create, customize, and / or configure cloud applications through a no-code user interface with built-in features such as configurable and automated workflows and dynamic relationship discovery and creation. In some cases, the platform can provide pre-built applications that users can further customize via a drag-and-drop GUI. For example, the platform can provide initial pre-built automation for an "application suite" (e.g., inventory management or merchandise sales). In some cases, these initial automations can be generated based on industry knowledge and expertise.

[0172] The platform can automatically provide initial workflows based on connected data objects. In some cases, the platform can automatically initiate workflows based on connected data objects and allows for secure collaboration with third parties. Workflows can be highly configurable through computation, approval, tasks, analytics, automation, and more.

[0173] The platform can automatically select from a library of application suites based on connected data objects, including those for logistics, sales, inventory management, risk management, procurement, finance, HR, and business development. For example, based on insights extracted from cloud data (e.g., data mining), the platform can select an initial application / workflow from the application suite library.

[0174] In some cases, the platform can provide users with a GUI to configure or edit pre-built workflows. This beneficially allows for the creation of cloud applications without code using pre-built automation. Figure 26An example of a GUI 3200 for creating workflows is shown. An initial workflow can be created using pre-built automations 3203 in one or more locations. Users can modify the initial workflow using drag-and-drop functionality. For example, a user can add objects 3201 (such as tasks, records, elements, transactions, approvals, fields) to the workflow at any desired location by dragging a component from the Objects panel 3205 and placing it into the workflow. Users can be allowed to further add actions to selected objects by dragging elements (e.g., data mining, automation, calculations, relationships, assigning users, APIs, etc.) from the Actions panel 3207 to objects in the workflow. In some cases, users can add objects and / or actions by clicking on graphical elements 3203 in the workflow (e.g., a plus sign icon or object icon for adding objects) to activate a menu for selecting the objects and / or actions to add. In some cases, users can choose to delete or modify actions (e.g., automations) or objects provided in the initial workflow by interacting with graphical elements corresponding to actions or objects.

[0175] Figures 27-30 Another example of a GUI for creating workflows is shown. Figure 27 As shown, the GUI can display workflows with one or more stages 2710, 2720, 2730, 2740, and 2750. The GUI can display general information associated with each stage, such as the number of actions contained in each stage and the percentage of automation 2751. Different stages can have different percentages of automation. (See diagram 2751 for details.) Figure 27 As shown, the startup phase 2710 can be 100% automated. The startup phase can include multiple actions 2719-1, 2719-2, 2719-3, and 2719-4. In some cases, actions can include logic 2711, 2713, 2715, 2717 and objects 2712, 2714, 2716, and 2718. Logic and objects can define "who" (logic) does "what" (object). Logic can be, for example, automation, request approval, user input, calculation, relations, data mining, etc. Objects can be, for example, records, fields, tables, summaries, and various other objects / elements provided by the system. In some cases, users can modify the workflow by dragging elements from the panel (left panel) and placing them into the workflow. This panel can provide, for example, shapes 2761 (e.g., a square shape can be used to represent an action, and a diamond shape can be used to represent a decision), logic options 2763, and an object list 2765. The GUI can also display information related to the entire process, such as the percentage of automation in the entire process / workflow and the total number of actions (3201).

[0176] Figure 28An example of a GUI displaying the workflow of the second stage 2720 is shown. Similarly, the second-stage workflow may include one or more actions 2721, 2722, and each action may include logic 2723 and an object 2724. In some cases, the system may recommend an initial workflow for the stage and display it on the GUI, where the user can then choose to accept, modify, or reject any component of the workflow. In some cases, the user may be allowed to zoom in / out from any stage to view the complete process 2801. The GUI may also display a preview 2803 of the next stage. Figure 29 An example GUI showing the workflow of Phase 3 2730 is shown. In this example, the workflow can be 50% automated because one action involves a human analyst and another involves automation. Figure 30 An example of a GUI showing the workflow of the fourth stage 2740 is shown.

[0177] Figure 31 An example is shown that displays a GUI showing a created workflow with tracking progress. For example... Figure 31 and Figure 32 As shown, once the workflow is deployed and executed, details about data analysis, calculations, actions, progress, etc., can be displayed to the user on the GUI.

[0178] Examples of use cases As described above, the platform can automatically select an initial workflow from a library of application suites based on connected data objects. This library includes applications such as logistics, sales, inventory management, risk management, procurement, finance, HR, and business development. For example, based on insights extracted from cloud data (e.g., data mining), the platform can select an initial application / workflow from the application suite library. An application suite can include multiple workflows. Figures 33-38 An example of a logistics application suite is shown. As the example illustrates, a logistics application suite can include multiple workflows. Workflows can include data mining to identify dynamic relationships between objects, as well as automation (e.g., triggering conditions and actions). For example, as... Figure 34 As shown, leadtime optimization workflows can be provided to proactively address channel variance gaps and reduce excess inventory. Data connected to the workflow (e.g., channels, goods, partners, sites) can be mined to identify when actual leadtime falls within defined tolerance levels and automate actions to adjust leadtime accordingly. Figure 35 As shown, a temperature alert workflow can be provided to reduce the amount of expired products by proactively managing temperature conditions during transportation. Data can be mined to identify temperature issues with goods in transit. Actions between the logistics team and carriers can be automated to resolve alert issues. It is possible to create... Figure 36The customer issue workflow shown aims to reduce customs delays through proactive issue management. Data mining is used to alert for potential problems based on port congestion, strikes, and other impacts on the port. Actions between logistics and brokers are automated. It is possible to create... Figure 37 The workflow shown is for delayed shipments, designed to improve OTIF by proactively identifying delayed shipments. Data is mined to identify when the estimated arrival time of shipments is later than the promised delivery date. Actions for identifying alternative sources, expediting, and mitigating delays are automated. It is possible to create workflows such as... Figure 38 The expedited request workflow is shown to provide complete transparency, and accountability for the costs of authorizing expedited requests is managed and centralized within a single platform. Actions are automated to notify carriers, logistics companies, and others of approvals.

[0179] AI-based suggestions In some embodiments, AI-based recommendations can be provided to the initial workflow. For example, the platform can develop AI models to generate predictions about when to initiate an action (e.g., its location in the workflow), what action to take, or other characteristics of the initial workflow. Training datasets collected within the platform can be used to train and develop the AI ​​models. For example, past action patterns can be extracted from action or process data defined within the platform, and this data can be used as training data to develop the AI ​​models. In some cases, the AI ​​models can also predict data fields involved in the workflow.

[0180] The provided system can employ any suitable artificial intelligence techniques to generate workflows, automate identification, dynamically correlate identification, transform data models (e.g., normalize raw data in the cloud to conform to the stored data model in the platform), and / or perform other functions described elsewhere in this document. Artificial intelligence (including machine learning algorithms) can be used to train predictive models for predictive recommendations (e.g., automation, workflows, etc.), extract data relationships, normalize data, perform impact analysis as described above, and perform various other functions described elsewhere in this document. For example, a machine learning algorithm can be a neural network. Examples of neural networks include deep neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Machine learning algorithms can include one or more of the following: support vector machines (SVMs), Naive Bayes classification, linear regression models, quantile regression models, logistic regression models, random forests, isolated forest (iForest) models, neural networks, CNNs, RNNs, gradient boosting classifiers or suppressors, or another supervised or unsupervised machine learning algorithm (e.g., generative adversarial networks (GANs), Cycle-GANs, etc.). In some cases, models trained by machine learning algorithms can be pre-trained and implemented on the provided system, and the pre-trained models can be continuously trained or refined using custom data. This may involve continuously adjusting the predictive model or its components (e.g., classifiers) to adapt to changes in the implementation environment or application over time (e.g., changes in user data, insight data, model performance, third-party data, etc.).

[0181] Data mining Data-driven workflows can provide data mining capabilities, allowing insights to be extracted from data in the data cloud. In some cases, the platform's data mining features can be used to trigger insight generation directly from data within the data cloud, and / or automatically identify data events (e.g., data changes, data additions / deletions, data anomalies, or other data analytics provided by the cloud provider). The platform can provide a GUI for users to easily configure or set up data mining for selected data. Data mining features can be seamlessly integrated with other functionalities such as automation. For example, data mining configurations or data mining results can be used as triggers and conditions for automation to initiate or trigger actions.

[0182] Data mining features allow users to create data mining operations to automate workflows. In some cases, GUI features allow users to find data in a data cloud (e.g., users are not always aware of their data in the data cloud). GUI features can also allow users to configure their data streams to connect, clean, filter, and enhance their data. Figure 58 and Figure 59An example GUI (e.g., CloudLink Explorer) is shown, allowing users to search for data in a data cloud (e.g., Snowflake). As described above, the system in this paper can access the data schema of all customers' data in a connected data lake / cloud. The shape of the data schema (logical data structure) or table (e.g., multidimensional data model, dimension table, etc.) can be configured based on the data cloud. As shown in the example, CloudLink Explorer makes API calls to data providers (e.g., Snowflake, Azure, etc.) to obtain metadata about databases, schemas, and data tables in the data lake. In some embodiments, the platform can automatically provide an initial workflow based on the fetched metadata or cloud data objects. In some cases, the platform can automatically initiate a workflow based on connected data objects and can allow secure collaboration with third parties. The platform can automatically select from an application suite library (such as the marketplace described later in this paper) based on the fetched cloud data objects and / or metadata. For example, based on insights extracted from cloud data (e.g., data mining), the platform can select an initial application / workflow from the application suite library. The initial workflow can be presented to the user as a suggestion, and the user can further configure or edit the initial workflow as described elsewhere in this paper.

[0183] After receiving metadata, the system can convert it into a graphical view that can be searched and browsed via a GUI. For example... Figure 58 As shown, graphical views 5803 and 5805 of the data schema / tables associated with users (e.g., a data lake associated with a user account) can be displayed on the GUI. Users can visualize their data schemas / tables in the underlying data lake in various formats 5803 and 5805 (e.g., tabular format, graphical representation, etc.). The GUI also allows users to search and / or sort data 5801. Figure 59 As shown, a preview of data mode 5901 can be displayed on the GUI.

[0184] In some embodiments, the system-provided GUI allows users to set logical rules or run models trained using machine learning algorithms on data to find data. For example, users can set logical rules to find data to start a workflow. Figure 60 An example of a GUI is shown for users to set logical rules (e.g., filter parameters and logical operators in records) to find data. Figure 61 An example of a GUI for users to set up machine learning-based anomaly detection and reporting rules is shown. For example, users can use the GUI to set rules for preparing data, selecting numeric columns, selecting data / time columns, selecting windowing intervals, checking results, and scheduling and data mining processes.

[0185] In some embodiments, the system can store the state of found data so that users are not repeatedly notified when data is found. For example, the system can use a data identifier (e.g., a unique identifier) ​​to track each time a row of data begins a workflow, making subsequent data queries idempotent. For instance, if the system does not track that a row of data has triggered a workflow, the same workflow might be triggered multiple times per hour. This feature beneficially reduces unnecessary notifications to users, allowing them to avoid multiple notifications when data is found until the data no longer passes logical checks. The system described herein can use the primary key of a data table as the data identifier. In some cases, the system may allow users to configure or control the data identifier via a GUI. Figure 62 This example shows a GUI for users to select the primary column to use as a unique identifier for their data. In some cases, the system may also allow users to perform idempotent data queries on transactional data that does not have an identifier by default.

[0186] After setting up the data mining process, users can configure automation to create new workflows using the found data. As shown in the GUI above, users can schedule the execution of the data mining process (e.g., scheduling the frequency of the data mining process or the conditions used to run the data mining process).

[0187] Figures 39-43 An example GUI for configuring or creating data mining is shown. In some cases, users can configure or set the data to be mined (e.g., a table) and one or more parameters to run data mining on the data. Figures 39-42 An example of the GUI 3900 for creating objects (e.g., tables) for data mining is shown. Figure 39 As shown, users can drag element 3903 from the object pane and drop the selected element (e.g., channel 3901). For example, users can click the element icon in the left pane and select an object (e.g., channel) from the drop-down menu. The table 3905 of the selected elements can be automatically populated on the GUI. Users can filter the selected object 3901, such as by clicking the "Filter Conditions" icon 3907, and then the filter pane 3909 will pop up with multiple configurable fields for users to set filter conditions. For example, users can set values, combine filter conditions, set filter states, etc., to set the filter conditions to be applied to object 3901. Once the filter conditions are applied, table 3905 can be automatically updated, and information about the filter condition 4001 can also be displayed along with the object, such as... Figure 40 As shown.

[0188] The user can be prompted to drag and drop another object, such as goods 4003. Similarly, the user can be prompted (4005) to set filters to apply to the second object 4003. Once the second object and the second filter are set, the user can be prompted to set a relationship or view the relationship between the two objects. For example, clicking the relationship icon (4007) can pop up a relationship pane and allow the user to define the relationship between the two objects, as described elsewhere in this document. This table can be automatically updated as relationships and / or filters are configured.

[0189] like Figure 41 As shown, the GUI provides users with options for column 4101 in the aggregated output table. For example, users can select the columns to be aggregated and / or define filter conditions, such as... Figure 42 As shown. GUI 4201 allows users to select the columns to be included in the output table and define how the selected columns are aggregated. It also allows users to create new columns 4203 in the output table via the GUI.

[0190] The GUI also allows users to set actions to be performed on the tables they create. For example, ... Figure 41 As shown, the GUI can display message 4103, prompting the user to select the action to apply to the table. The user can click the automation icon 4105 and select from the action options (such as create record, send notification) in the drop-down menu to set the automation action.

[0191] Once the table is created and saved (for example, data can be written directly back to the data cloud), users can set one or more parameters to run data mining. For example, Figure 43 The GUI shown can prompt the user (4301) to set one or more parameters to schedule the frequency and / or time of data mining operations and / or one or more parameters for filtering tables. As shown in the example, after clicking the schedule button (4303), options (4307) for setting the frequency and / or time of data mining operations can be displayed. The user can select from frequency options (such as hourly, daily, weekly) and / or set the start time via the GUI. The user can also be allowed to set filter conditions via the GUI (4309), which can be applied to the data mining operations by clicking the filter conditions button (4305).

[0192] cloud-native architecture In one aspect, this disclosure provides a system for providing a data-driven workflow platform. The system includes: a first module configured to operatively couple the data-driven workflow platform to one or more data clouds; a second module configured to map selected data objects to a data storage model of the data-driven workflow platform, wherein the selected data objects are stored on the one or more data clouds; and a visualization module configured to display an interactive flow on a graphical user interface (GUI) for utilizing or managing the selected data objects to build cloud applications. In some cases, the interactive flow includes at least one graphical element corresponding to a rule for automating actions triggered by triggering events of the selected data objects.

[0193] In some embodiments, the first module is configured to translate instructions for performing operations on at least one of selected data objects received via a GUI into database operations executable in a data cloud configuration. Database operations are performed on the selected data objects in the data cloud configuration without using an Extract, Transform, and Load (ETL) data integration process. In some cases, the selected data objects include transactional data or streaming data. In some cases, the data-driven workflow platform is configured to cache intermediate results used to perform the actions.

[0194] Figure 44 The diagram illustrates the architecture of a data-driven workflow platform. This architecture can be layered, comprising a data structure and storage layer, a service / platform logic layer, and a visualization / API layer. This layered architecture allows users to access and use data stored in any data cloud without moving the data to a central location. The data-driven workflow platform can execute workloads (e.g., queries) within a data cloud provider (e.g., AWS S3, Snowflake, Databricks, external data providers, etc.) and then stream the data and / or insights back to the user through a user experience interface (UI) provided by the visualization module. In some cases, the platform can employ buffering techniques to allow real-time streaming or updates from the data cloud to the enterprise SaaS solution. For example, the platform can cache intermediate results generated during workflow actions for predetermined time periods (e.g., 15 seconds, 20 seconds, 30 seconds, etc.).

[0195] The platform can configure monitoring processes via cloud links that connect to notification services in the platform's logic layer and monitoring services in the data structure and storage layers, so that notifications are received when data in the source dataset in the data cloud changes. Monitoring features can be used to trigger actions in automated functions within the platform. Query execution takes place in the service layer. For example, queries can be processed using "virtual repositories," where each virtual repository is a massively parallel computing cluster consisting of multiple compute nodes from a cloud provider.

[0196] AI-based application discovery In some embodiments, the platform can analyze available datasets and leverage artificial intelligence (AI) technology to recommend one or more predefined applications. This beneficially allows users to utilize the recommended applications based on their data.

[0197] Figure 45 Examples of AI-based application discovery features according to some embodiments of this disclosure are illustrated schematically. System 4503 can access the data schema of all customers' data in a connected data lake 4505. The shape of the data schema (logical data structure) or table (e.g., multidimensional data model, dimension table, etc.) can be configured based on the data cloud. System 4503 can be the same as the data-driven workflow platform or service management cloud system described elsewhere herein. For example, an adapter for system 4503 can make data hosted in a data cloud (e.g., Snowflake) appear as native data within the system. For example, during configuration or integration of target data between the data cloud (e.g., Snowflake) and the system, the adapter can set up mappings and data type matching without changing the data or imposing any type of modification on the data within the data cloud. For example, system 4505 can send a request for access to the user data table schema in data lake 4505. This request can be generated based on input received through GUI 4501, such as business process name, description, or other input information.

[0198] System 4503 may include multiple predefined applications organized or managed in an application library (e.g., an application marketplace), which platform users or customers may install into their organizations. For example, system 4503 may include a predefined application suite library that includes logistics, merchandise sales, inventory management, risk management, procurement, finance, HR, business development, etc., as described elsewhere in this document.

[0199] Figure 56 and Figure 57An example of an application marketplace GUI is shown. In some cases, system 4503 can publish complex workflows to the marketplace, which allows users (e.g., customers) to deploy workflows selected from the marketplace in their environments. Figure 56 As shown, workflows can be built and maintained (e.g., updated) by the system administrator. In some cases, system administrators (e.g., process experts) can build and update workflows and deploy the updated workflows to clients, who can then begin using the workflows within their companies without needing to build associated data tables, automation, approval processes, or surveys.

[0200] Workflows can be organized by category (e.g., enterprise technology, supply chain, enterprise service management, etc.) for clients to select and deploy to their own company environments. In some cases, clients / users can search for workflows by category and / or application (e.g., IT operations, leave, HR, basic events, delivery, etc.). Figure 57 As shown, clients / users can view detailed information about selected workflows or applications (e.g., IT operations) through the GUI. For example, the GUI can display examples of applications used by IT operations, typical variables that the application can track, and the potential industry sectors of the application.

[0201] The system can train a large language model (LLM) 4507 based on the predefined availability of the application and the shape of the data tables required to implement the application (i.e., the data lake schema). The system trains the LLM based on the shape of the data tables in the client's data lake. The LLM can be personalized or customized using user data. For example, a user can provide a list of data tables. The system can identify a list of available predefined workflows or business workflows and provide them with the necessary data. For example, the LLM can be trained to identify one or more workflows from a predefined workflow library, at least in part based on the shape of the data tables associated with the user. The system can be instructed to find data tables with similar shapes and functionalities to predefined workflows. For example, the system can return a JSON array of business process objects with the following keys:

[0202] After training the LLM, the system requests the LLM to find predefined applications that can be driven by data from the data lake. For example, during the inference / prediction phase, the LLM can be deployed to take data patterns (e.g., the shape of data tables) obtained from the data lake associated with user accounts as input and output data table mappings. The LLM returns the data table mappings to the system for creating predefined applications.

[0203] If a match is found with a predefined application, the LLM returns the data table mapping to the system, and the system creates the application on behalf of the client. If no match is found, the system switches to a generation approach and instructs the LLM to generate possible business workflows outside of the predefined application.

[0204] As mentioned above, the input to a trained LLM can include the shape of a data table or schema (e.g., table fields, views, etc.). Below is an example of the input to a trained LLM: User data table: <[{"name": "USERS", "databaseName": "financials", "schemaName": "internal"}, {"name": "SHIPMENTS", "databaseName": "financials", "schemaName": "internal"}, {"name": "PRODUCTS", "databaseName": "financials", "schemaName": "internal Here is an example of the model's output:

[0205] Figures 46-48 An example of a GUI for AI-based application discovery features is shown. Figure 46 As shown, users can provide input through the application discovery function within the GUI, such as by selecting a cloud link. The system can then automatically collect data patterns from the selected data lake and identify a list of available predefined workflows or business workflows with the necessary data to drive the application. Figure 47 The illustration shows an example of a predefined application that the system identifies as the output of a model.

[0206] like Figure 48 As shown, users can choose from several predefined applications to create an application. For example, users can be prompted to provide input in data fields such as name, namespace, handle, description, and category to create an application.

[0207] AI-generated workflow In some embodiments, workflows can be generated by an AI model. For example, in AI-based application discovery features, if an LLM cannot map customer data to a predefined application, the system can use AI to generate a business workflow. The AI ​​workflow module of this document may include a trained model that takes a description of a business process (e.g., provided by the customer via a GUI used to create the business process) as input and outputs a workflow. This model can be trained using machine learning algorithms described elsewhere in this document.

[0208] Figure 49 Examples of AI-generated workflow features according to some embodiments of this disclosure are illustrated schematically. System 4903 may receive input from GUI 4901, such as business workflow name, description, or other input information. System 4903 may be the same as the data-driven workflow platform or service management cloud system described elsewhere herein.

[0209] System 4903 can train an LLM to create workflows. In some cases, an LLM can be trained by: i) instructing the LLM to undertake its purpose of creating business workflows, ii) instructing the LLM to break down a business process into one or more stages, iii) instructing the LLM to create one or more steps for each stage of the process, and iv) requesting the LLM to identify and track data related to each step of the business process.

[0210] After the LLM is trained 4905, the system 4903 can provide the LLM with the name of the business process, the description of the process, and any additional context from the user about how they wish to define their business process (received via GUI 4901).

[0211] An LLM can be trained to output business workflow data. In some cases, the output of an LLM may include a list of instructions used by system 4903 to create business workflows on behalf of a client.

[0212] AI-generated workflow features can automatically generate business processes for users / customers. For example, the system can i) receive instructions to create a new business workflow, ii) break down the business process into named stages, iii) create named steps for reaching stages, and iv) create data fields for each step to track the business process, returning only JSON objects. Here is an example of the format: - Phase: - Name: Stage Name - Steps: - Description: Step-by-step description - Data fields: - Name: Field Name - Field Type: Select only one of the following field types: BOOLEAN | TEXT | NUMBER | DECIMAL | DATE | DATETIME The input to the trained LLM can be based on user input. For example, the user input can include business process name: <HR Onboarding>, business process description: <Run the employee onboarding process>, additional business process context: <Make sure to include tracking of social security number, birthday, and T-shirt size so that we can send them a giveaway when they join the company>.

[0213] As mentioned above, the output of the model can include instructions for the system to create a business workflow. The following are examples of workflows:

[0214] Figures 50-53 An example of a GUI showing AI-generated workflow features is shown. Figure 50 An example of the input provided through the GUI is shown. As shown in the example, the user can provide a description of the business process to start business process generation. As Figure 51 shown, the system can automatically collect data associated with the user and the business process, such as through the above-described AI-based application discovery features. As Figure 52 shown, the LLM can output one or more stages of the business process, such as start, pre-onboarding, and onboarding. As Figure 53 shown, the LLM can output one or more steps or actions for each stage. After executing the list of instructions output by the LLM, the GUI can display graphical elements representing one or more stages and one or more steps for each stage.

[0215] In some embodiments, various functionalities and visual features may be provided virtually without requiring the installation, configuration, or management of any software. The data-driven workflow platform system may be implemented on a cloud platform system (e.g., including server-based or serverless systems) that communicates with one or more user systems / devices over a network. The cloud platform system may be configured to provide the aforementioned functionalities to users through one or more user interfaces or graphical user interfaces (GUIs), which may include, but are not limited to, web-based GUIs, client-side GUIs, or any other GUIs as described above. For example, users may access coding challenges through a web-based GUI or within a web browser. In some cases, the graphical user interface (GUI) or user interface may be provided on a display. The display may be a touchscreen or may not be a touchscreen. The display may be a light-emitting diode (LED) screen, an organic light-emitting diode (OLED) screen, a liquid crystal display (LCD) screen, a plasma screen, or any other type of screen. The display may be configured to display a user interface (UI) or graphical user interface (GUI) presented through an application programming interface (API) executed on a user device or user system or in the cloud.

[0216] Without using such exclusive terminology, the term "comprising" in claims associated with this disclosure should allow for the inclusion of any additional elements—regardless of whether a given number of elements are enumerated in such claim, or whether adding features can be considered to alter the nature of an element set forth in such claim. Unless otherwise expressly defined herein, all technical and scientific terms used herein should be given the meaning as broadly as commonly understood, while preserving the validity of the claims.

[0217] Computing System refer to Figure 63 The diagram illustrates an exemplary machine including a computer system 6300 (e.g., a processing or computing system) within which a set of instructions can be executed to cause the device to perform any one or more aspects and / or methods of static code scheduling of this disclosure. Figure 63 The components described are merely examples and do not limit the scope or functionality of any hardware, software, embedded logic components, or combinations of two or more such components used to implement a particular embodiment.

[0218] Computer system 6300 may include one or more processors 6301, memory 6303, and storage devices 6308, which communicate with each other and with other components via bus 6340. Bus 6340 may also link a display 6332, one or more input devices 6333 (e.g., which may include a keypad, keyboard, mouse, stylus, etc.), one or more output devices 6334, one or more storage devices 6335, and various tangible storage media 6336. All these components may be directly connected to bus 6340 or connected to bus 6340 via one or more interfaces or adapters. For example, various tangible storage media 6336 may be connected to bus 6340 via storage media interface 6326. Computer system 6300 may have any suitable physical form, including but not limited to one or more integrated circuits (ICs), printed circuit boards (PCBs), mobile handheld devices (such as mobile phones or PDAs), laptops or notebook computers, distributed computer systems, computing grids, or servers.

[0219] Computer system 6300 includes one or more processors 6301 (e.g., a central processing unit (CPU), a general-purpose graphics processing unit (GPGPU), or a quantum processing unit (QPU)) that perform functions. Processor 6301 optionally includes cache memory 6302 for temporary local storage of instructions, data, or computer addresses. Processor 6301 is configured to assist in the execution of computer-readable instructions. Because processor 6301 executes non-transitory, processor-executable instructions embodied in one or more tangible computer-readable storage media (such as memory 6303, storage device 6308, storage device 6335, and / or storage medium 6336), computer system 6300 can be Figure 63 The components depicted herein provide functionality. A computer-readable medium may store software implementing a particular embodiment, and the processor 6301 may execute that software. The memory 6303 may read the software from one or more other computer-readable media (such as mass storage devices 6335, 6336) or from one or more other sources via a suitable interface (such as network interface 120). The software may cause the processor 6301 to perform one or more processes or one or more steps of processes described or illustrated herein. Performing such processes or steps may include defining data structures stored in the memory 6303 and modifying the data structures according to the instructions of the software.

[0220] Memory 6303 may include various components (e.g., machine-readable media), including but not limited to random access memory components (e.g., RAM 6304) (e.g., static RAM (SRAM), dynamic RAM (DRAM), ferroelectric random access memory (FRAM), phase-change random access memory (PRAM), etc.), read-only memory components (e.g., ROM 105), and any combination thereof. ROM 6305 may be used to unidirectionally transfer data and instructions to processor 6301, while RAM 6304 may be used to bidirectionally transfer data and instructions with processor 6301. ROM 6305 and RAM 6304 may include any suitable tangible computer-readable media described below. In one example, a basic input / output system 6306 (BIOS) may be stored in memory 6303, which includes basic routines that facilitate the transfer of information (such as during startup) between elements within computer system 6300.

[0221] Fixed storage device 6308 is optionally bidirectionally connected to processor 6301 via storage control unit 6307. Fixed storage device 6308 provides additional data storage capacity and may also include any suitable tangible computer-readable medium described herein. Storage device 6308 may be used to store operating system 6309, executable file 6310, data 6311, application 6312, etc. Storage device 6308 may also include optical disc drive, solid-state storage device (e.g., flash-based system), or any combination thereof. Where appropriate, information in storage device 6308 may be incorporated into memory 6303 as virtual memory.

[0222] In one example, storage device 6335 can be detachably connected to computer system 6300 via storage device interface 6325 (e.g., via an external port connector (not shown)). Specifically, storage device 6335 and associated machine-readable medium can provide non-volatile and / or volatile storage for machine-readable instructions, data structures, program modules, and / or other data for computer system 6300. In one example, software may reside wholly or partially within the machine-readable medium on storage device 6335. In another example, software may reside wholly or partially within processor 6301.

[0223] Bus 6340 connects various subsystems. In this document, references to the bus may include, where appropriate, one or more digital signal lines serving common functions. Bus 6340 can be any of several types of bus structures using any of a variety of bus architectures, including but not limited to memory buses, memory controllers, peripheral buses, local buses, and any combination thereof. By way of example and not limitation, such architectures include Industry Standard Architecture (ISA) buses, Enhanced ISA (EISA) buses, Micro Channel Architecture (MCA) buses, Video Electronics Standards Association Local Bus (VLB), Peripheral Component Interconnect (PCI) buses, PCI-Express (PCI-X) buses, Accelerated Graphics Port (AGP) buses, HyperTransport (HTX) buses, Serial Advanced Technology Attachment (SATA) buses, and any combination thereof.

[0224] Computer system 6300 may also include input device 6333. In one example, a user of computer system 6300 may input commands and / or other information into computer system 6300 via input device 6333. Examples of input device 6333 include, but are not limited to, alphanumeric input devices (e.g., keyboard), pointing devices (e.g., mouse or touchpad), touchpad, touchscreen, multi-touch screen, joystick, stylus, gamepad, audio input devices (e.g., microphone, voice response system, etc.), optical scanner, video or still image capture devices (e.g., camera), and any combination thereof. In some embodiments, the input device is Kinect, Leap Motion, etc. Input device 6333 may be connected to bus 6340 via any of a variety of input interfaces 6323 (e.g., input interface 6323), including but not limited to serial, parallel, game port, USB, firewire, thunderbolt, or any combination thereof.

[0225] In a particular embodiment, when computer system 6300 is connected to network 6330, computer system 6300 can communicate with other devices connected to network 6330 (specifically, mobile devices and enterprise systems, distributed computing systems, cloud storage systems, cloud computing systems, etc.). Communication to and from computer system 6300 can be sent via network interface 6320. For example, network interface 6320 can receive one or more incoming communications (such as requests or responses from other devices) in the form of packets (such as Internet Protocol (IP) packets) from network 6330, and computer system 6300 can store the incoming communications in memory 6303 for processing. Similarly, computer system 6300 can store one or more outgoing communications (such as requests or responses to other devices) in the form of packets in memory 6303 and transmit them from network interface 6320 to network 6330. Processor 6301 can access these communication packets stored in memory 6303 for processing.

[0226] Examples of network interface 6320 include, but are not limited to, network interface cards, modems, and any combination thereof. Examples of network 6330 or network segment 6330 include, but are not limited to, distributed computing systems, cloud computing systems, wide area networks (WANs) (e.g., the Internet, corporate networks), local area networks (LANs) (e.g., networks associated with offices, buildings, campuses, or other relatively small geographical areas), telephone networks, direct connections between two computing devices, peer-to-peer networks, and any combination thereof. Networks (such as network 6330) can employ wired and / or wireless communication modes. Typically, any network topology can be used.

[0227] Information and data can be displayed via display 6332. Examples of display 6332 include, but are not limited to, liquid crystal displays (LCDs), thin-film transistor liquid crystal displays (TFT-LCDs), organic liquid crystal displays (OLEDs) (such as passive matrix OLEDs (PMOLEDs) or active matrix OLEDs (AMOLEDs) displays), plasma displays, and any combination thereof. Display 6332 can be connected to processor 6301, memory 6303, and fixed storage device 6308, as well as other devices (such as input devices 6333), via bus 6340. Display 6332 is linked to bus 6340 via video interface 6322, and data transmission between display 6332 and bus 6340 can be controlled via graphical control 6321. In some embodiments, the display is a video projector. In some embodiments, the display is a head-mounted display (HMD), such as a VR headset. In further embodiments, as non-limiting examples, suitable VR headsets include HTC Vive, Oculus Rift, Samsung Gear VR, Microsoft HoloLens, Razer OSVR, FOVE VR, Zeiss VR One, Avegant Glyph, Freefly VR headsets, etc. In even further embodiments, the display is a combination of devices such as those disclosed herein.

[0228] In addition to the display 6332, the computer system 6300 may also include one or more other peripheral output devices 6334, including but not limited to audio speakers, printers, storage devices, and any combination thereof. Such peripheral output devices can be connected to the bus 6340 via output interface 6324. Examples of output interface 6324 include, but are not limited to, serial ports, parallel connections, USB ports, firewire ports, thunderbolt ports, and any combination thereof.

[0229] In addition, or alternatively, computer system 6300 may provide functionality as a result of logic hard-wired in a circuit or otherwise embodied, which may operate in place of or with software to perform one or more processes or steps of one or more processes described or illustrated herein. References to software in this disclosure may cover logic, and references to logic may cover software. Furthermore, where appropriate, references to computer-readable media may cover circuitry (such as an IC) storing software for execution, circuitry embodying logic for execution, or both. This disclosure covers any suitable combination of hardware, software, or both.

[0230] Those skilled in the art will understand that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, various exemplary components, blocks, modules, circuits, and steps have been generally described above in terms of their functionality.

[0231] The various illustrative logic blocks, modules, and circuits described in conjunction with the embodiments disclosed herein can be implemented or executed using a general-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The general-purpose processor may be a microprocessor, but alternatively, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors combined with a DSP core, or any other such configuration.

[0232] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be embodied directly in hardware, directly in a software module executed by one or more processors, or directly in a combination of both. The software module can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor such that the processor can read information from and write information to the storage medium. Alternatively, the storage medium can be integrated with the processor. The processor and storage medium can reside in an ASIC. The ASIC can reside in a user terminal. Alternatively, the processor and storage medium can reside as discrete components in the user terminal.

[0233] Based on the description herein, suitable computing devices, by way of non-limiting example, include cloud computing platforms, distributed computing platforms, server clusters, server computers, desktop computers, laptop computers, notebook computers, mini-notebook computers, netbook computers, internet tablet computers, set-top box computers, media streaming devices, handheld computers, internet devices, mobile smartphones, tablet computers, personal digital assistants, video game consoles, and vehicles. Those skilled in the art will also recognize that selected televisions, video players, and digital music players with optional computer network connectivity are suitable for the systems described herein. In various embodiments, suitable tablet computers include those with brochures, tablets, and convertible configurations known to those skilled in the art.

[0234] In some embodiments, the computing device includes an operating system configured to execute executable instructions. For example, an operating system is software that includes programs and data, manages the device's hardware, and provides services for executing applications. Those skilled in the art will recognize that, as non-limiting examples, suitable server operating systems include FreeBSD, OpenBSD, and NetBSD. ® Linux, Apple ® Mac OS X Server ® Oracle ® Solaris ® Windows Server ® and Novell ® NetWare ® Those skilled in the art will recognize that, by way of non-limiting example, suitable personal computer operating systems include Microsoft... ® Windows ® Apple ® Mac OS X ® UNIX ® and UNIX-like operating systems (such as GNU / Linux) ® In some embodiments, the operating system is provided by cloud computing. Those skilled in the art will also recognize that, as a non-limiting example, suitable mobile smartphone operating systems include Nokia… ® Symbian ® OS, Apple ® iOS ® Research in Motion ® BlackBerry OS ® Google ® Android ® Microsoft ® Windows Phone ® OS, Microsoft ® Windows Mobile ® OS, Linux ® and Palm ® WebOS ® Those skilled in the art will also recognize that, by way of non-limiting example, suitable media streaming device operating systems include Apple TV. ® Roku ® Boxee ® Google TV ®Google Chromecast ® Amazon Fire ® and Samsung ® HomeSync ® Those skilled in the art will also recognize that, as a non-limiting example, suitable video game console operating systems include Sony... ® PS3 ® Sony ® PS4 ® Microsoft ® Xbox 360 ® , Microsoft Xbox One, Nintendo ® Wii ® Nintendo ® Wii U ® and Ouya ® .

[0235] Non-transitory computer-readable storage media In some embodiments, the platforms, systems, media, and methods disclosed herein include one or more non-transitory computer-readable storage media encoded with a program including instructions executable by an operating system of an optionally networked computing device. In further embodiments, the computer-readable storage medium is a tangible component of a computing device. In still further embodiments, the computer-readable storage medium may optionally be removable from the computing device. In some embodiments, as a non-limiting example, the computer-readable storage medium includes CD-ROMs, DVDs, flash memory devices, solid-state storage, disk drives, tape drives, optical disc drives, distributed computing systems (including cloud computing systems and services), etc. In some cases, the program and instructions are permanently, substantially permanently, semi-permanently, or non-transitory encoded on the medium.

[0236] Computer program In some embodiments, the platforms, systems, media, and methods disclosed herein include at least one computer program or its use. A computer program includes a sequence of instructions executable by one or more processors of a computing device's CPU, the sequence of instructions being written to perform a specified task. Computer-readable instructions can be implemented as program modules that perform a particular task or implement a particular abstract data type, such as functions, objects, application programming interfaces (APIs), computational data structures, etc. Based on the disclosure provided herein, those skilled in the art will recognize that computer programs can be written in various versions of various languages.

[0237] The functionality of computer-readable instructions can be combined or distributed as needed in various environments. In some embodiments, a computer program includes a sequence of instructions. In some embodiments, a computer program includes multiple sequences of instructions. In some embodiments, a computer program is provided from one location. In other embodiments, a computer program is provided from multiple locations. In various embodiments, a computer program includes one or more software modules. In various embodiments, a computer program includes, in part or in whole, one or more web applications, one or more mobile applications, one or more standalone applications, one or more web browser plugins, extensions, add-ons, or attachments, or combinations thereof.

[0238] web applications In some embodiments, the computer program includes a web application. As will be appreciated by those skilled in the art based on the disclosure provided herein, in various embodiments, the web application utilizes one or more software frameworks and one or more database systems. In some embodiments, the web application is based on a platform such as Microsoft... ® The web application is built on a software framework such as .NET or Ruby on Rails (RoR). In some embodiments, the web application utilizes one or more database systems, which, as a non-limiting example, include relational database systems, non-relational database systems, object-oriented database systems, relational database systems, XML database systems, and document-oriented database systems. In further embodiments, as a non-limiting example, suitable relational database systems include Microsoft... ® SQL Server, mySQL™ and Oracle ® Those skilled in the art will also recognize that, in various embodiments, the web application is written in one or more versions of one or more languages. The web application may be written in one or more markup languages, presentation definition languages, client-side scripting languages, server-side coding languages, database query languages, or combinations thereof. In some embodiments, the web application is written to some extent in a markup language such as Hypertext Markup Language (HTML), Extensible Hypertext Markup Language (XHTML), or Extensible Markup Language (XML). In some embodiments, the web application is written to some extent in a presentation definition language such as Cascading Style Sheets (CSS). In some embodiments, the web application is written to some extent in a client-side scripting language such as Asynchronous JavaScript and XML (AJAX), Flash... ® ActionScript, JavaScript or Silverlight ®The web application is written in some form of server-side coding language (such as Active Server Pages (ASP), ColdFusion). ® Perl, Java™, Java Server Pages (JSP), Hypertext Preprocessor (PHP), Python™, Ruby, Tcl, Smalltalk, WebDNA ® The web application is written in a database query language such as Groovy. In some embodiments, the web application is written to some extent in a database query language such as Structured Query Language (SQL). In some embodiments, the web application integrates with enterprise server products such as IBM. ® Lotus Domino ® In some embodiments, the web application includes a media player element. In various further embodiments, the media player element utilizes one or more of a number of suitable multimedia technologies, including, as a non-limiting example, Adobe... ® Flash ® HTML5, Apple ® QuickTime ® Microsoft ® Silverlight ® Java™ and Unity ® .

[0239] refer to Figure 64 In a particular embodiment, the application providing system includes one or more databases 6400 accessed by a relational database management system (RDBMS) 6410. Suitable RDBMSs include Firebird, MySQL, PostgreSQL, SQLite, Oracle Database, Microsoft SQL Server, IBM DB2, IBM Informix, SAP Sybase, Teradata, etc. In this embodiment, the application providing system also includes one or more application servers 6420 (such as Java servers, .NET servers, PHP servers, etc.) and one or more web servers 6430 (such as Apache, IIS, GWS, etc.). The web servers optionally expose one or more web services through an application programming interface (API) 6440. The system provides a browser-based and / or mobile-native user interface via a network (such as the Internet).

[0240] refer to Figure 65In a particular embodiment, the application providing system may alternatively have a distributed, cloud-based architecture 6500, and include elastically load-balanced, auto-scaling web server resources 6510 and application server resources 6520, as well as a synchronously replicated database 6530.

[0241] Mobile application In some embodiments, the computer program includes a mobile application provided to a mobile computing device. In some embodiments, the mobile application is provided to the mobile computing device at the time of manufacture. In other embodiments, the mobile application is provided to the mobile computing device via the computer network described herein.

[0242] Given the disclosure provided herein, mobile applications are created using techniques known to those skilled in the art, employing hardware, languages, and development environments known in the art. Those skilled in the art will recognize that mobile applications are written in several languages. As non-limiting examples, suitable programming languages ​​include C, C++, C#, Objective-C, Java™, JavaScript, Pascal, Object Pascal, Python™, Ruby, VB.NET, WML, and XHTML / HTML, or combinations thereof, with or without CSS.

[0243] Suitable mobile application development environments are available from multiple sources. As non-limiting examples, commercial development environments include AirplaySDK, alcheMo, and Appcelerator. ® Celsius, Bedrock, Flash Lite, .NET CompactFramework, Rhomobile, and WorkLight Mobile Platform are among the software development environments (SDKs) available free of charge. Other free SSDs include Lazarus, MobiFlex, MoSync, and PhoneGap, as examples of non-restrictive SSDs. Additionally, mobile device manufacturers distribute software development kits (SDKs), including the iPhone and iPad (iOS) SDK, Android™ SDK, and BlackBerry SDK, as examples of non-restrictive SSDs. ® SDK, BREW SDK, Palm ® OS SDK, Symbian SDK, webOSSDK and Windows ® Mobile SDK.

[0244] Those skilled in the art will recognize that several business forums can be used for distributing mobile applications; as non-limiting examples, these business forums include Apple. ®App Store, Google ® Play, Chrome WebStore, BlackBerry ® App World, App Store for Palm devices, App Catalog for webOS, Windows for mobile phones ® Marketplace, for Nokia ® Ovi Store for devices, Samsung ® Apps and Nintendo ® DSiShop.

[0245] standalone application In some embodiments, a computer program includes a standalone application, which is a program that runs as an independent computer process, rather than an appendage to an existing process, such as a plugin. Those skilled in the art will recognize that standalone applications are typically compiled. A compiler is a computer program that translates source code written in a programming language into binary object code, such as assembly language or machine code. Suitable compiled programming languages, as non-limiting examples, include C, C++, Objective-C, COBOL, Delphi, Eiffel, Java™, Lisp, Python™, Visual Basic, and VB .NET, or combinations thereof. Compilation is typically performed at least partially to create an executable program. In some embodiments, a computer program includes one or more executable compiled applications.

[0246] Web browser plugin In some embodiments, a computer program includes web browser plugins (e.g., extensions, etc.). In computing, a plugin is one or more software components that add specific functionality to a larger software application. Software application manufacturers support plugins to enable third-party developers to create extended applications, allowing for easy addition of new features and reduction of application size. When supported, plugins can customize the functionality of a software application. For example, plugins are commonly used in web browsers to play videos, generate interactive elements, scan for viruses, and display specific file types. Those skilled in the art will be familiar with several web browser plugins, including those from Adobe... ® Flash ® Player, Microsoft ® Silverlight ® and Apple ® QuickTime ®In some embodiments, the toolbar includes one or more web browser extensions, add-ons, or accessories. In some embodiments, the toolbar includes one or more Explorer bars, toolbars, or desktop toolbars.

[0247] In view of the disclosure provided herein, those skilled in the art will recognize that several plugin frameworks are available that enable the development of plugins in a variety of programming languages, including, by way of non-limiting example, C++, Delphi, Java™, PHP, Python™, and VB .NET or combinations thereof.

[0248] A web browser (also known as an internet browser) is a software application designed for use with computing devices connected to a network to retrieve, display, and traverse information resources on the World Wide Web. As a non-limiting example, suitable web browsers include Microsoft... ® Internet Explorer ® Mozilla ® Firefox ® Google ® Chrome, Apple ® Safari ® Opera Software ® Opera ® And KDE Konqueror. In some embodiments, the web browser is a mobile web browser. Mobile web browsers (also known as microbrowsers, mini-browsers, and wireless browsers) are designed for use on mobile computing devices, which, as a non-limiting example, include handheld computers, tablet computers, netbook computers, small notebook computers, smartphones, music players, personal digital assistants (PDAs), and handheld video game systems. As a non-limiting example, suitable mobile web browsers include: Google... ® Android ® browser, RIM BlackBerry ® Browser, Apple ® Safari ® Palm ® Blazer, Palm ® WebOS ® Browser, Mozilla for mobile phones ® Firefox ® Microsoft ® Internet Explorer ®Mobile, Amazon ® Kindle ® BasicWeb, Nokia ® Browser, Opera Software ® Opera ® Mobile and Sony ® PSP™ browser.

[0249] Software Module In some embodiments, the platforms, systems, media, and methods disclosed herein include software, server, and / or database modules, or their use. Given the disclosure provided herein, the software modules are created using techniques known to those skilled in the art, employing machines, software, and languages ​​known in the art. The software modules disclosed herein are implemented in a variety of ways. In various embodiments, the software module includes files, code segments, programming objects, programming constructs, distributed computing resources, cloud computing resources, or combinations thereof. In further various embodiments, the software module includes multiple files, multiple code segments, multiple programming objects, multiple programming constructs, multiple distributed computing resources, multiple cloud computing resources, or combinations thereof. In various embodiments, as a non-limiting example, one or more software modules include web applications, mobile applications, standalone applications, and distributed or cloud computing applications. In some embodiments, the software module resides in a single computer program or application. In other embodiments, the software module resides in more than one computer program or application. In some embodiments, the software module is hosted on a single machine. In other embodiments, the software module is hosted on more than one machine. In further embodiments, the software module is hosted on a distributed computing platform, such as a cloud computing platform. In some embodiments, the software module is hosted on one or more machines in one location. In other embodiments, the software module is hosted on one or more machines in more than one location.

[0250] database In some embodiments, the platforms, systems, media, and methods disclosed herein include one or more databases, or their use. In view of the disclosure provided herein, those skilled in the art will recognize that many databases are suitable for storing and retrieving data or information native to the system described herein. The databases described herein may be accessed, maintained, or controlled by a data-driven workflow platform. In some cases, the database may differ from a cloud-based repository associated with a platform customer. The database may be local or may be remotely accessed by a data-driven workflow platform.

[0251] In various embodiments, as non-limiting examples, suitable databases include relational databases, non-relational databases, object-oriented databases, object databases, entity-relational model databases, association databases, XML databases, document-oriented databases, and graph databases. Further non-limiting examples include SQL, PostgreSQL, MySQL, Oracle, DB2, Sybase, and MongoDB. In some embodiments, the database is internet-based. In further embodiments, the database is web-based. In still further embodiments, the database is cloud-based. In a particular embodiment, the database is a distributed database. In other embodiments, the database is based on one or more local computer storage devices.

[0252] This document describes various embodiments of the present disclosure. These examples are mentioned in a non-limiting sense. They are provided to illustrate aspects of the broader applicability of this disclosure. Various changes may be made to the disclosure and equivalents may be substituted without departing from the true spirit and scope of the disclosure. Furthermore, many modifications may be made to adapt particular circumstances, materials, composition, processes, process actions, or steps to the purpose, spirit, or scope of the disclosure. Moreover, those skilled in the art will understand that each individual variation described and illustrated herein has discrete components and features that can be readily separated from or combined with features of any of the other several embodiments without departing from the scope or spirit of the disclosure. All such modifications are intended to fall within the scope of the claims associated with this disclosure.

[0253] While preferred embodiments of the invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. The invention is not intended to be limited to the specific examples provided in the specification. Although the invention has been described with reference to the foregoing specification, the description and illustration of the embodiments herein are not intended to be construed as limiting. Many variations, alterations, and substitutions will now be conceived by those skilled in the art without departing from the invention. Furthermore, it should be understood that all aspects of the invention are not limited to the specific descriptions, configurations, or relative proportions set forth herein, which depend on various conditions and variables. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in carrying out the invention. Therefore, the invention is also intended to cover any such substitutions, modifications, variations, or equivalents. The appended claims are intended to define the scope of the invention and are thereby intended to cover the methods and structures within the scope of these claims and their equivalents.

Claims

1. A method for a no-code automation platform, the method comprising: Access data objects stored in a data cloud configuration, wherein the data cloud configuration is different from and operatively coupled to the no-code automation platform; The no-code automation platform displays a workflow for building cloud applications that utilize or manage the data objects on its graphical user interface (GUI), wherein the workflow includes graphical elements corresponding to automated actions, and wherein the automated actions are defined by a rule that defines actions triggered by triggering events of the selected data objects, or the automated actions include anomaly detection of the selected data objects based on machine learning. as well as The cloud application, built using the workflow displayed on the GUI of the no-code automation platform, automatically detects the triggering events of selected data objects in the data cloud configuration and automatically performs the action on at least one selected data object, or performs the machine learning-based anomaly detection on selected data objects, without extracting or loading the data objects from the data cloud configuration or moving the data objects to the no-code automation platform.

2. The method of claim 1, wherein the automated action can be modified by defining variables using operators on the GUI.

3. The method of claim 2, wherein the variable includes a data object invoked using the operator or intermediate data generated by a previous action in the workflow.

4. The method of claim 2, wherein the variable is selected from one or more available variables provided by the no-code automation platform.

5. The method of claim 3, wherein the intermediate data is cached by the no-code automation platform.

6. The method of claim 1, wherein the workflow is created by identifying workflows from a plurality of predefined workflows using a large language model (LLM).

7. The method of claim 1, wherein the workflow is identified at least in part based on the data patterns of the data objects stored in the data cloud configuration.

8. The method of claim 6, wherein the LLM is trained using data patterns of the data objects stored in the data cloud configuration.

9. The method of claim 1, wherein the workflow is created by an LLM, and the output of the LLM includes a list of instructions for creating the workflow.

10. The method of claim 9, wherein the input of the LLM includes a description of the cloud application.

11. The method of claim 1, wherein the machine learning-based anomaly detection can be configured via the GUI.

12. The method of claim 11, wherein at least one time window interval for detecting anomalous data objects is configurable.

13. A system for providing a no-code automation platform, the system comprising at least one processor and instructions executable to cause the at least one processor to perform the following operations: Access data objects stored in a data cloud configuration, wherein the data cloud configuration is different from and operatively coupled to the no-code automation platform; The no-code automation platform displays a workflow for building cloud applications that utilize or manage the data objects on its graphical user interface (GUI), wherein the workflow includes graphical elements corresponding to automated actions, and wherein the automated actions are defined by a rule that defines actions triggered by triggering events of the selected data objects, or the automated actions include anomaly detection of the selected data objects based on machine learning. as well as The cloud application, built using the workflow displayed on the GUI of the no-code automation platform, automatically detects the triggering events of selected data objects in the data cloud configuration and automatically performs the action on at least one selected data object, or performs the machine learning-based anomaly detection on selected data objects, without extracting or loading the data objects from the data cloud configuration or moving the data objects to the no-code automation platform.

14. The system of claim 13, wherein the automated action can be modified by defining variables using operators on the GUI.

15. The system of claim 14, wherein the variable includes a data object invoked using the operator or intermediate data generated by a previous action in the workflow.

16. The system of claim 14, wherein the variable is selected from one or more available variables provided by the no-code automation platform.

17. The system of claim 15, wherein the intermediate data is cached by the no-code automation platform.

18. The system of claim 13, wherein the workflow is created by identifying workflows from a plurality of predefined workflows using a large language model (LLM).

19. The system of claim 13, wherein the workflow is identified at least in part based on the data patterns of the data objects stored in the data cloud configuration.

20. The system of claim 18, wherein the LLM is trained using data patterns of the data objects stored in the data cloud configuration.

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