Description:DESCRIPTION
Technical Field
[0001] This disclosure relates generally to workflow management, and more particularly to a method and system for automated workflow management.
Background
[0002] Traditional Business Process Management (BPM) platforms typically rely on rule-based or predefined logic to guide processes, which restrict the BPM platforms to rigid and static workflows with limited flexibility. Complex or dynamic processes (such as customer service workflows, fraud detection, and risk management in financial industry, or regulatory compliance) often require significant manual intervention due to inability of the BPM platforms to adapt to evolving contexts.
[0003] The present invention is directed to overcome one or more limitations stated above or any other limitations associated with the known arts.
SUMMARY
[0004] In one embodiment, a method for automated workflow management is disclosed. In one example, the method may include initiating a workflow corresponding to input data received from one or more sources. It should be noted that the workflow may be configured for execution through a dynamic state machine. It should also be noted that the dynamic state machine may include a plurality of states and a plurality of transitions between the plurality of states. The method may further include executing the dynamic state machine based on the input data, the plurality of states, the plurality of transitions, and a set of predefined properties associated with each of the plurality of states and the plurality of transitions, to implement the workflow. For each state of the plurality of states, the executing may include, dynamically determining, via a semiotics engine, contextual data for the state based on one or more semiotic constructs in at least one of the input data or a transition from a previous state. It should be noted that the contextual data may be based on relationships between the plurality of states, the corresponding plurality of transitions, and the set of predefined properties. For each state of the plurality of states, the executing may further include adaptively executing, via one of an Artificial Intelligence (AI) model or a rule-based model, one or more actions of the state based on the contextual data and a plurality of multi-layered property groups of historical data. It should be noted that the one or more actions may be a part of the set of predefined properties. It should also be noted that the plurality of multi-layered property groups may be a hierarchically classified arrangement of the historical data.
[0005] In another embodiment, a system for automated workflow management is disclosed. In one example, the system may include a processor and a memory communicatively coupled to the processor. The memory may store processor-executable instructions, which, on execution, may cause the processor to initiate a workflow corresponding to input data received from one or more sources. It should be noted that the workflow may be configured for execution through a dynamic state machine. It should also be noted that the dynamic state machine may include a plurality of states and a plurality of transitions between the plurality of states. The processor-executable instructions, on execution, may further cause the processor to execute the dynamic state machine based on the input data, the plurality of states, the plurality of transitions, and a set of predefined properties associated with each of the plurality of states and the plurality of transitions, to implement the workflow. For each state of the plurality of states, to execute the dynamic state machine, the processor-executable instructions, on execution, may cause the processor to dynamically determine, via a semiotics engine, contextual data for the state based on one or more semiotic constructs in at least one of the input data or a transition from a previous state. It should be noted that the contextual data may be based on relationships between the plurality of states, the corresponding plurality of transitions, and the set of predefined properties. For each state of the plurality of states, to execute the dynamic state machine, the processor-executable instructions, on execution, may further cause the processor to adaptively execute, via one of an Artificial Intelligence (AI) model or a rule-based model, one or more actions of the state based on the contextual data and a plurality of multi-layered property groups of historical data. It should be noted that the one or more actions are a part of the set of predefined properties. It should also be noted that the plurality of multi-layered property groups may be a hierarchically classified arrangement of the historical data.
[0006] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles.
[0008] FIG. 1 is a block diagram of an exemplary system for automated workflow management, in accordance with some embodiments of the present disclosure.
[0009] FIG. 2 illustrates a functional block diagram of an exemplary system for automated workflow management, in accordance with some embodiments of the present disclosure.
[0010] FIG. 3 illustrates a flow diagram of an exemplary process for automated workflow management, in accordance with some embodiments of the present disclosure.
[0011] FIG. 4 illustrates a flow diagram of an exemplary process for creating the multi-property groups, in accordance with some embodiments of the present disclosure.
[0012] FIG. 5 illustrates a flow diagram of an exemplary process for identifying a workflow change, in accordance with some embodiments of the present disclosure.
[0013] FIG. 6 illustrates a flow diagram of an exemplary process for determining one or more corrective actions, in accordance with some embodiments of the present disclosure.
[0014] FIG. 7 illustrates a block diagram of an architecture of an exemplary system for automated workflow management, in accordance with some embodiments of the present disclosure.
[0015] FIG. 8 illustrates a flow diagram of a detailed exemplary process for determining contextual signals by a semiotics engine using multi-layered property groups, in accordance with some embodiments of the present disclosure.
[0016] FIG. 9 is a schematic diagram of a hierarchical arrangement of exemplary multi-layered property groups, in accordance with some embodiments of the present disclosure.
[0017] FIG. 10 illustrates an exemplary dynamic state machine, in accordance with some embodiments of the present disclosure.
[0018] FIG. 11 is a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure.
DETAILED DESCRIPTION
[0019] Exemplary embodiments are described with reference to the accompanying drawings. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the spirit and scope of the disclosed embodiments. It is intended that the following detailed description be considered as exemplary only, with the true scope and spirit being indicated by the following claims.
[0020] Referring now to FIG. 1, an exemplary system 100 for automated workflow management is illustrated, in accordance with some embodiments of the present disclosure. The system 100 may include a computing device 102. The computing device 102 may be, for example, but may not be limited to, server, desktop, laptop, notebook, netbook, tablet, smartphone, mobile phone, or any other computing device, in accordance with some embodiments of the present disclosure. The computing device 102 may automate workflow management using a semiotics engine, an AI model, and dynamic state machines.
[0021] As will be described in greater detail in conjunction with FIGS. 2 – 11, the computing device 102 may initiate a workflow corresponding to input data received from one or more sources. It should be noted that the workflow may be configured for execution through a dynamic state machine. It should also be noted that the dynamic state machine may include a plurality of states and a plurality of transitions between the plurality of states. The computing device 102 may further execute the dynamic state machine based on the input data, the plurality of states, the plurality of transitions, and a set of predefined properties associated with each of the plurality of states and the plurality of transitions, to implement the workflow. For each state of the plurality of states, to execute the dynamic state machine, the computing device 102 may dynamically determine, via a semiotics engine, contextual data for the state based on one or more semiotic constructs in at least one of the input data or a transition from a previous state. It should be noted that the contextual data may be based on relationships between the plurality of states, the corresponding plurality of transitions, and the set of predefined properties. Further, for each state of the plurality of states, the computing device 102 may adaptively execute, via one of an AI model or a rule-based model, one or more actions of the state based on the contextual data and a plurality of multi-layered property groups of historical data. It should be noted that the one or more actions may be a part of the set of predefined properties. It should also be noted that the plurality of multi-layered property groups may be a hierarchically classified arrangement of the historical data.
[0022] In some embodiments, the computing device 102 may include one or more processors 104 and a memory 106. Further, the memory 106 may store instructions that, when executed by the one or more processors 104, may cause the one or more processors 104 to automate workflow management, in accordance with aspects of the present disclosure. The memory 106 may also store various data (for example, input data, historical data, contextual data, multi-layered property groups, workflow data, and the like) that may be captured, processed, and/or required by the system 100. The memory 106 may be a non-volatile memory (e.g., flash memory, Read Only Memory (ROM), Programmable ROM (PROM), Erasable PROM (EPROM), Electrically EPROM (EEPROM) memory, etc.) or a volatile memory (e.g., Dynamic Random Access Memory (DRAM), Static Random-Access memory (SRAM), etc.).
[0023] The system 100 may further include a display 108. The system 100 may interact with a user interface 110 accessible via the display 108. The system 100 may also include one or more external devices 112. In some embodiments, the computing device 102 may interact with the one or more external devices 112 over a communication network 114 for sending or receiving various data. The communication network 114 may include, for example, but may not be limited to, a wireless fidelity (Wi-Fi) network, a light fidelity (Li-Fi) network, a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a satellite network, the internet, a fiber optic network, a coaxial cable network, an infrared (IR) network, a radio frequency (RF) network, and a combination thereof. The one or more external devices 112 may include, but may not be limited to, a remote server, a laptop, a netbook, a notebook, a smartphone, a mobile phone, a tablet, or any other computing device.
[0024] Referring now to FIG. 2, a functional block diagram 200 of an exemplary system 200 for automated workflow management is illustrated, in accordance with some embodiments of the present disclosure. FIG. 2 is explained in conjunction with FIG. 1. The system 200 may be implemented in the computing device 102. The system 200 may include, within the memory 106, a workflow initiation module 202, a workflow execution module 204, and a property group creation module 206.
[0025] The workflow initiation module 202 may initiate a workflow corresponding to input data 208 received from one or more sources. The workflow may correspond to a business process of an organization. By way of an example, for a banking organization, the business process may be a fraud detection and risk management process, a loan approval process, a customer onboarding process, a customer service management process, a regulatory compliance process, or the like. For an ecommerce organization, the business process may be an order processing process, a supply chain management process, or the like. For an education organization, the business process may be an admission/enrollment process, a student assessment and grading process, or the like. For a healthcare organization, a patient registration process, an appointment scheduling process, a medical records management process, or the like.
[0026] The workflow may include a plurality of workflow steps. The workflow may be configured for execution through a dynamic state machine 210. The dynamic state machine 210 may include a plurality of states and a plurality of transitions between the plurality of states. The plurality of states and the plurality of transitions may correspond to the plurality of workflow steps. By way of an example, the one or more sources of the input data 208 for a workflow may include, but may not be limited to, user actions, Application Programming Interfaces (APIs) and sensors, and external data sources.
[0027] Further, the workflow execution module 204 may execute the dynamic state machine 210 based on the input data 208, the plurality of states, the plurality of transitions, and a set of pre-defined properties associated with each of the plurality of states and the plurality of transitions, to implement the workflow. By way of an example, the set of pre-defined properties may include, but may not be limited to, input events (i.e., an entry action), output events (i.e., one or more exit actions), an initial state, a number of additional states resulting from the output events, triggers, conditions, and the like.
[0028] To execute the dynamic state machine 210, for each state of the plurality of states, the workflow execution module 204 may dynamically determine, via a semiotics engine 212, contextual data for the state based on one or more semiotic constructs in at least one of the input data 208 or a transition from a previous state. The contextual data may be based on relationships between the plurality of states, the corresponding plurality of transitions, and the set of pre-defined properties.
[0029] Further, to execute the dynamic state machine 210, the workflow execution module 204 may adaptively execute, via one of an AI model 214 or a rule-based model, one or more actions of the state based on the contextual data and a plurality of multi-layered property groups of historical data. In one example, the contextual data may be determined from the received input data 208 by using the historical data. It should be noted that the one or more actions may be a part of the set of predefined properties. It should also be noted that the plurality of multi-layered property groups may be a hierarchically classified arrangement of the historical data.
[0030] The plurality of multi-layered property groups may be created by the property groups creation module 206. To create the plurality of multi-layered property groups, the property groups creation module 206 may store the input data 208 with the historical data in a historical database 216. Further, the property group creation module 206 may classify the historical data into the plurality of multilayered groups through a clustering technique to obtain the plurality of multi-layered property groups. In some embodiments, the property group creation module 206 may adaptively modify the plurality of multi-layered groups based on at least one of a user feedback or a user requirement. It may be noted that the plurality of multi-layered property groups may be arranged in the plurality of layers. Each of the plurality of layers may include one or more of the plurality of multi-layered property groups. Each of the plurality of layers may correspond to a granularity level of information. By way of an example, the plurality of multi-layered property groups may be organized in 3 hierarchical layers. A first layer (i.e., a top layer in the hierarchy) is a top-level group, the second one is a medium-level group, and the third one is a low-level group. This is explained in greater detail in conjunction with FIG. 9.
[0031] Additionally, the property group creation module 206 may allocate in real time, via the AI model 214, computational resources across the plurality of multi-layered property groups based on performance metrics corresponding to the dynamic state machine 210. The real-time allocation across the plurality of multi-layered property groups may optimize performance, minimize delays, and reduce costs.
[0032] To adaptively execute the dynamic state machine 210, upon adaptively executing the one or more actions of the state, the workflow execution module 204 may predict, via the semiotics engine 212 and the AI model 214, a next state from the plurality of states in the dynamic state machine 210 based on the execution of the one or more actions. Further, the workflow execution module 204 may modify, via the AI model 214, the set of pre-defined properties associated with the next state to obtain a new workflow path. Thus, new states of the dynamic state machine 210 may be adaptively determined and defined based on the execution of previous states of the dynamic state machine 210.
[0033] In some embodiments, during the execution of the dynamic state machine 210, the workflow execution module 204 may identify in real time, via the semiotics engine 212, a workflow change from the contextual data. Further, the workflow execution module 204 may dynamically modify, via the AI model 214, the set of pre-defined properties of each of one or more states or transitions from the plurality of states and the plurality of transitions based on the workflow change. In other words, based on the comparison of the contextual data with the historical data, the semiotics engine 212 may identify the workflow change and may dynamically modify the set of pre-defined properties of the states or transitions in the workflow in accordance with the workflow change.
[0034] Additionally, during execution of the dynamic state machine 210, the workflow execution module 204 may check in real-time, for anomalies in the workflow using the semiotics engine 212. In an embodiment, the workflow execution module 204 may identify in real-time, via the semiotics engine 212, an anomaly in the workflow using the semiotic constructs. Further, the workflow execution module 204 may determine, via the AI model 214, an error diagnosis corresponding to the anomaly by backtracking through the plurality of multi-layered groups. Further, the workflow execution module 204 may determine, via the AI model 214, one or more corrective actions for the anomaly based on the error diagnosis. The one or more corrective actions may be executed to address the error diagnosis.
[0035] Upon completion of the adaptive execution of the dynamic state machine 210, the execution of the workflow may be successfully completed. Further, the workflow execution module 204 may receive performance metrics, process outcomes, and updated objectives corresponding to the workflow (i.e., the executed workflow). The performance metrics may be based on at least one of accuracy, relevancy, false positive rate, or processing time. Further, the workflow execution module 204 may dynamically adjust, via the AI model 214, the plurality of states and the plurality of transitions of the dynamic state machine 210 based on the performance metrics, the process outcomes, and the updated objectives. In other words, the AI model 214 may be refined and the dynamic state machine 210 may be dynamically adjusted based on a feedback loop. The feedback loop is formed by using the performance metrics, the process outcomes, and the updated objectives as a feedback to train the AI model 214 using reinforcement learning.
[0036] The system 200 may be deployed to implement an AI-enabled BPM platform. By way of an example, in banking and financial services industry, the system 200 may be used to dynamically implement a fraud detection and risk management workflow. Thus, the workflow initiation module 202 may receive the input data 208 from multiple sources. The input data 208 may include transaction details (e.g., amount, location, merchant, etc.), customer behavior (e.g., login frequency, transaction history, etc.), market conditions (e.g., economic trends, emerging fraud patterns, etc.), and risk profiles (e.g., customer credit score, known fraud associations, etc.).
[0037] Further, the property group creation module 206 may organize the input data 208 into multi-layered property groups to facilitate structured analysis. For example, the multi-layered property groups may include a transaction history property group, a customer behavior property group, and a risk factors property group. The transaction history property group may include data corresponding to past transaction patterns. The customer behavior property group may include behavioral data, such as transaction frequency and geographical patterns. The risk factor property group may include risk levels, including credit history and previous fraud detection alerts.
[0038] Further, the workflow execution module 204 may interpret, via the semiotics engine 212, contextual signals within the input data 208. For instance, the workflow execution module 204 may evaluate whether a location and a time of a transaction match with a typical behavior of the customer (e.g., is the transaction consistent with usual patterns or does the transaction show an anomaly?). Further, the workflow execution module 204 may apply the contextual interpretation (or understanding) to assess risk factors in real-time. The workflow execution module 204 may dynamically flag suspicious activities based on changing conditions (e.g., a sudden spike in transactions in a high-risk region, or unusually large withdrawals).
[0039] Further, the workflow execution module 204 may manage, via the dynamic state machine 210, workflow states, transitioning between stages (i.e., workflow steps) based on real-time decision-making. For example, the stages may include, a transaction processed stage, a risk assessment stage, a fraud detection stage, an investigation requirement check stage, and a transaction approved/denied stage. At each state transition, the workflow execution module 204 may evaluate whether a fraud alert or risk escalation is warranted, dynamically adjusting the workflow based on ongoing analysis.
[0040] When the workflow execution module 204 may detect, via the semiotics engine 212, a potential risk or fraud pattern, the workflow execution module 204 may then automatically trigger predefined actions, such as blocking the transaction or flagging the transaction for review, sending an alert to the risk management team, or requesting additional customer verification (e.g., two-factor authentication). Further, the workflow execution module 204 may continually monitor all the transactions and may automatically adjust the decision-making AI models (i.e., the AI model 214) based on historical patterns, emerging threats, and customer-specific data.
[0041] It should be noted that all such aforementioned modules 202 – 206 may be represented as a single module or a combination of different modules. Further, as will be appreciated by those skilled in the art, each of the modules 202 – 206 may reside, in whole or in parts, on one device or multiple devices in communication with each other. In some embodiments, each of the modules 202 – 206 may be implemented as dedicated hardware circuit comprising custom application-specific integrated circuit (ASIC) or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. Each of the modules 202 – 206 may also be implemented in a programmable hardware device such as a field programmable gate array (FPGA), programmable array logic, programmable logic device, and so forth. Alternatively, each of the modules 202 – 206 may be implemented in software for execution by various types of processors (e.g., processor 104). An identified module of executable code may, for instance, include one or more physical or logical blocks of computer instructions, which may, for instance, be organized as an object, procedure, function, or other construct. Nevertheless, the executables of an identified module or component need not be physically located together but may include disparate instructions stored in different locations which, when joined logically together, include the module and achieve the stated purpose of the module. Indeed, a module of executable code could be a single instruction, or many instructions, and may even be distributed over several different code segments, among different applications, and across several memory devices.
[0042] As will be appreciated by one skilled in the art, a variety of processes may be employed for automated test case generation. For example, the exemplary system 100 and the associated computing device 102 may automate workflow management by the processes discussed herein. In particular, as will be appreciated by those of ordinary skill in the art, control logic and/or automated routines for performing the techniques and steps described herein may be implemented by the system 100 and the associated computing device 102 either by hardware, software, or combinations of hardware and software. For example, suitable code may be accessed and executed by the one or more processors on the system 100 to perform some or all of the techniques described herein. Similarly, application specific integrated circuits (ASICs) configured to perform some, or all of the processes described herein may be included in the one or more processors on the system 100.
[0043] Referring now to FIG. 3, an exemplary process 300 for automating workflow management is depicted via a flow chart, in accordance with some embodiments of the present disclosure. The process 300 may be implemented by the computing device 102 of the system 100. FIG. 3 is explained in conjunction with Figs. 1 and 2. In some embodiments, the process 300 may include initiating, by a workflow initiation module (such as the workflow initiation module 202), the workflow corresponding to input data (such as the input data 208) received from one or more sources, at step 302. It may be noted that the workflow may be configured for execution through a dynamic state machine (such as the dynamic state machine 210). Further, the dynamic state machine may include the plurality of states and the plurality of transitions between the plurality of states.
[0044] The process 300 may further include executing, by a workflow execution module (such as the workflow execution module 204), the dynamic state machine based on the input data, the plurality of states, the plurality of transitions, and the set of predefined properties associated with each of the plurality of states and the plurality of transitions, to implement the workflow, at step 304. Further, the execution may include for each state of the plurality of states, the process 300 may include dynamically determining, by the workflow execution module, via a semiotics engine (such as the semiotics engine 212), contextual data for the state based on one or more semiotic constructs in at least one of the input data or the transition from the previous state, at step 306. It may be noted that the contextual data may be based on relationships between the plurality of states, the corresponding plurality of transitions, and the set of predefined properties.
[0045] For each state of the plurality of states, the process 300 may further include adaptively executing, by the workflow execution module, via one of an Artificial Intelligence (AI) model or a rule-based model (such as, the AI model 214), one or more actions of the state based on the contextual data and the plurality of multi-layered property groups of historical data, at step 308. It may be noted that the one or more actions may be the part of the set of predefined properties. It may also be noted that the plurality of multi-layered property groups may be the hierarchically classified arrangement of the historical data.
[0046] Upon adaptively executing the one or more actions of the state, the process 300 may further include predicting, by the workflow execution module, via the semiotics engine and the AI model, the next state from the plurality of states in the dynamic state machine based on the execution of the one or more actions, at step 310. The process 300 may further include modifying, via the AI model, the set of predefined properties associated with the next state to obtain a new workflow path, at step 312.
[0047] Upon adaptively executing the dynamic state machine, the process 300 may further include receiving, by the workflow initiation module, performance metrics, process outcomes, and updated objectives corresponding to the workflow, at step 314. The process 300 may further include dynamically adjusting, by the workflow execution module 204, via the AI model, the plurality of states and the plurality of transitions of the dynamic state machine based on the performance metrics, the process outcomes, and the updated objectives, at step 316.
[0048] Referring now to FIG. 4, an exemplary process 400 for creating the multi-property groups is depicted via a flow chart, in accordance with some embodiments of the present disclosure. The process 400 may be implemented by the computing device 102 of the system 100. FIG. 4 is explained in conjunction with Figs. 1, 2, and 3. In some embodiments, the process 400 may include storing, by a property group creation module (for example, the property group creation module 206), input data (for example, the input data 208) with the historical data in a historical database (for example, the historical database 216), at step 402. The process 400 may further include classifying, by the property group creation module, the historical data into the plurality of multi-layered property groups through the clustering technique, at step 404.
[0049] The process 400 may further include adaptively modifying, by the property group creation module, the plurality of multi-layered property groups based on at least one of the user feedback or the user requirement, at step 406. It may be noted that the plurality of multi-layered property groups may be arranged in the plurality of layers. Further, each of the plurality of layers may include one or more of the plurality of multi-layered property groups. Further, each of the plurality of layers may correspond to the granularity level of information. The process 400 may further include allocating in real-time, by the property group creation module, via an AI model (for example, the AI model 214), computational resources across the plurality of multi-layered property groups based on performance metrics corresponding to a dynamic state machine (for example, the dynamic state machine 210).
[0050] Referring now to FIG. 5, an exemplary process 500 for identifying the workflow change is depicted via a flow chart, in accordance with some embodiments of the present disclosure. The process 500 may be implemented by the computing device 102 of the system 100. FIG. 5 is explained in conjunction with Figs. 1, 2,3, and 4. In some embodiments, the process 500 may include identifying in real-time, by a workflow execution module (such as the workflow execution module 204), via a semiotics engine (such as the semiotics engine 212), the workflow change from contextual data, at step 502. The process 500 may further include dynamically modifying, by the workflow execution module, via an AI model (such as the AI model 214) the set of predefined properties of each of one or more states or transitions from the plurality of states and the plurality of transitions based on the workflow change, at step 504.
[0051] Referring now to FIG. 6, an exemplary process 600 for determining one or more corrective actions is depicted via a flow chart, in accordance with some embodiments of the present disclosure. The process 600 may be implemented by the computing device 102 of the system 100. FIG. 6 is explained in conjunction with Figs. 1, 2,3,4, and 5. In some embodiments, the process 600 may include identifying in real-time, by a workflow execution module (for example, the workflow execution module 204), via a semiotics engine (for example, the semiotics engine 212), the anomaly in the workflow during execution of a dynamic state machine (for example, the dynamic state machine 210) using the semiotic constructs, at step 602. The process 600 may further include determining, by the workflow execution module, via an AI model (for example, the AI model 214), the error diagnosis corresponding to the anomaly by backtracking through the plurality of multi-layered property groups, at step 604. The process 600 may further include determining, by the workflow execution module, via the AI model, one or more corrective actions for the anomaly based on the error diagnosis, at step 606.
[0052] Referring now to FIG. 7, a block diagram of an exemplary architecture 700 of a system for automated workflow management (such as the system 200) is depicted, in accordance with some embodiments of the present disclosure. FIG. 7 is explained in conjunction with FIGS. 1, 2, 3, 4, 5, and 6. The architecture 700 may include an input layer 702, a processing layer 704, and an output layer 706.
[0053] In an embodiment, the input layer 702 may provide event triggers for the workflow. Additionally, the input layer 702 may be used for data ingestion. The input layer 702 may collect input data (i.e., the input data 208) from various sources. The various sources may include, but may not be limited to, user actions 708, APIs and sensors 710, and external data sources 712. Further, the input data may be transmitted to the processing layer 704.
[0054] The processing layer 704 may include a semiotics engine 714 (analogous to the semiotics engine 212), property groups 716, an AI model core 718 (analogous to the AI model 214), and dynamic state machines 720 (analogous to the dynamic state machine 210). It may be noted that the processing layer 704 may be used to implement an intelligent workflow orchestration. The semiotics engine 714 may determine the contextual data corresponding to the input data using the property groups 716. The property groups 716 may include data from the external data sources organized in a structured form. The property groups 716 may be processed by the semiotics engine 714 to determine the contextual data. Additionally, the property groups 716 may be analyzed by the AI model core 718 for decision making. Further, the dynamic state machine 720 may execute the workflow dynamically based on the decision making to provide an output (i.e., the output 218) in real-time.
[0055] The output may be obtained in the output layer 706. The output layer 706 may include dynamic workflow states 722, a feedback loop 724, action outputs 726, and performance monitoring optimization 728. The dynamic workflow states 722 (i.e., predicted next states) may be the states that are dynamically determined based on the output of previous states in the dynamic state machines 720. The feedback loop 724 may be formed between the dynamic state machines 720 and the AI model core 718. The output of each state of the dynamic state machines 720 may be used as a feedback to train and determine the next state by the AI model core 718 through the feedback loop 724. The action outputs 726 may include an output from each of the actions executed each state of the dynamic state machines 720. The performance monitoring and optimization 728 may be an iterative process used to compute the performance metrics of the execution of the dynamic state machines 720 and to optimize the computed performance metrics.
[0056] Referring now to FIG. 8, a detailed exemplary process 800 for determining contextual signals by the semiotics engine 714 using the multi-layered property groups 716 is depicted, via a flow chart, in accordance with some embodiments of the present disclosure. The process 800 may be implemented by the computing device 102 of the system 100. FIG. 8 is explained in conjunction with Figs. 1, 2, 3, 4, 5, 6, and 7. It may be noted that the multi-layered property groups 716 may encapsulate reusable, logical data structures. The property groups may include raw transaction data 802, transaction history 804, customer behavior 806, market conditions 808, and risk profile 810. The raw transaction data 802 property group may include raw input data. The transaction history 804 property group may include data such as previous transactions. The customer behavior 806 property group may include data such as login frequency, recent activity. The market conditions 808 property group may include data such as economic trends, known fraud hotspots. The risk profile 810 property group may include data such as risk scores and previous fraud alerts.
[0057] The semiotics engine 714 may be used to analyze input signals to derive actionable insights. The semiotics engine may transform the raw transaction data 802 into actionable insights based on contextual data determined from the transaction history 804, the customer behavior 806, market conditions 808, and risk profile 810. The semiotics engine 714 may interprets the data context from the raw transaction data 802. Additionally, the raw transaction data 802 may be organized hierarchically into remaining of the property groups 804-810. The determined contextual data may be used by the AI model core 718 for dynamic workflow decision-making.
[0058] Referring now to FIG. 9, a schematic diagram of a hierarchical arrangement 900 of exemplary multi-layered property groups is illustrated, in accordance with some embodiments of the present disclosure. FIG. 9 is explained in conjunction with Figs 1, 2,3,4, 5,6,7, and 8. The hierarchical arrangement 900 corresponds to categorized data. For example, a property group associated with a data category of personal data may be associated with properties groups corresponding to sub-categories for details such as name, age, gender, address, etc. Additionally, the multi-layered property groups may be organized into multiple layers in the hierarchical arrangement 900. Each of the layers may correspond to a granularity level of information. By way of an example, the multi-layered property groups may be distributed into three layers. The first layer may include a property group for fraud detection & risk management data 902. The second layer may include property groups for customer information 904, transaction detail 906, market conditions 908, and transaction history 910. The third layer may include property groups linked to the property groups of the second layer. For example, property groups for customer ID 912, account history 914, and risk profile 916 may be associated with the property group for customer information 904.
[0059] Each layer in the hierarchical arrangement 900 may organize data at different levels of granularity, enabling processing of information hierarchically and contextually appropriate decision-making. For example, the property group for fraud detection and risk management data 902 may be of a lower granularity level of information. The property group for customer information 904 may be of a higher granularity level of information than the granularity level of information of the property group for fraud detection and risk management data 902. Further, the property group for customer ID 912 may be of a higher granularity level of information than the granularity level of information of the property group for customer information 904. Thus, from the top layer (i.e., the first layer) to the bottom layer (i.e., the third layer), the multi-layered property groups are arranged in an increasing granularity level of information in the hierarchical arrangement 900.
[0060] Referring now to FIG. 10, an exemplary dynamic state machine 1000 is depicted via a flow chart, in accordance with some embodiments of the present disclosure. The dynamic state machine 1000 may be implemented by the computing device 102 of the system 100. FIG. 10 is explained in conjunction with Figs. 1,2,3,4,5,6,7,8, and 9. By way of an example, the dynamic state machine 1000 may initiate a state 1002 for a new transaction (such as an amount of $5,000). The dynamic state machine 1000 may further include a step for transaction receiving. The transaction may be received from different sources (such as location: outside the customer’s typical geographic area, and merchant: new, unverified vendor).
[0061] Further, the transaction data may be transmitted to a semiotics engine (i.e., the semiotics engine 212). The semiotics engine may process transaction data, such as the semiotics engine may trigger an analysis based on the customer behavior and transaction history property groups (i.e., the multi-layered property group). The property group may include data such as a customer’s usual transactions are small and local, and a sudden deviation to a high-value, geographically distant transaction raises a flag. The dynamic state machine 1000 may further include a state 1006 for risk assessment. Further, the risk assessment may include such as using market conditions to evaluate whether similar patterns are occurring with other customers in the same region (e.g., a rise in fraud cases in that area), cross-referencing the risk profile of the customer, including previous fraud alerts.
[0062] The dynamic state machine 1000 may further include a state 1008 for risk detection. The risk detection stage may include an AI model (i.e., the AI model 214). Further, the AI model may conclude whether there is a high probability of fraud or not in the system. If the risk is detected, the dynamic state machine 1000 may move to the state 1010 (i.e., the yes path).
[0063] If the risk is not detected, the dynamic state machine 1000 may move to the state 1012 (i.e., the no path). The dynamic state machine 1000 may further include a state 1012 for the transaction approval. If the transaction is approved, the dynamic state machine 1000 may move to the state 1020 for transaction completion. When the transaction is completed, the dynamic state machine 1000 may include a state 1022 for the process to be closed. The process closed may include an output (i.e., the output 218). If the fraud is detected, the dynamic state machine 1000 may include a state 1010 for fraud detected. When the fraud is detected, the dynamic state machine 1000 may further include a state 1014 for transaction denied, such as automatically blocking the transaction. If the transaction is denied, the dynamic state machine 1000 may move to the state 1020. In the state 1020, the transaction is completed.
[0064] After the fraud detection, for the confirmation, the dynamic state machine 1000 may further include a state 1016 for investigation required. The investigation required may include such as notifying the risk management team for further investigation. It may be noted that the process may continue to the Investigation Required state, where the team reviews the flagged transaction and the customer’s account for further patterns. If investigation is required, the dynamic state machine 1000 may move to the state 1018. In the state 1018, the system may check the fraud confirmation by human intervention, such as alerting the customer to confirm if the transaction was authorized. If the fraud is confirmed, the dynamic state machine 1000 may move to the state 1014 and then move to state 1020. If the fraud is not confirmed, the dynamic state machine 1000 may move to the state 1012, and then move to the state 1020.
[0065] As will be also appreciated, the above-described techniques may take the form of computer or controller implemented processes and apparatuses for practicing those processes. The disclosure can also be embodied in the form of computer program code containing instructions embodied in tangible media, such as floppy diskettes, solid state drives, CD-ROMs, hard drives, or any other computer-readable storage medium, wherein, when the computer program code is loaded into and executed by a computer or controller, the computer becomes an apparatus for practicing the invention. The disclosure may also be embodied in the form of computer program code or signal, for example, whether stored in a storage medium, loaded into and/or executed by a computer or controller, or transmitted over some transmission medium, such as over electrical wiring or cabling, through fiber optics, or via electromagnetic radiation, wherein, when the computer program code is loaded into and executed by a computer, the computer becomes an apparatus for practicing the invention. When implemented on a general-purpose microprocessor, the computer program code segments configure the microprocessor to create specific logic circuits.
[0066] The disclosed methods and systems may be implemented on a conventional or a general-purpose computer system, such as a personal computer (PC) or server computer. Referring now to FIG. 11, an exemplary computing system 1100 that may be employed to implement processing functionality for various embodiments (e.g., as a SIMD device, client device, server device, one or more processors, or the like) is illustrated. Those skilled in the relevant art will also recognize how to implement the invention using other computer systems or architectures. The computing system 1100 may represent, for example, a user device such as a desktop, a laptop, a mobile phone, personal entertainment device, DVR, and so on, or any other type of special or general-purpose computing device as may be desirable or appropriate for a given application or environment. The computing system 1100 may include one or more processors, such as a processor 1102 that may be implemented using a general or special purpose processing engine such as, for example, a microprocessor, microcontroller, or other control logic. In this example, the processor 1102 is connected to a bus 1104 or other communication medium. In some embodiments, the processor 1102 may be an Artificial Intelligence (AI) processor, which may be implemented as a Tensor Processing Unit (TPU), or a graphical processor unit, or a custom programmable solution Field-Programmable Gate Array (FPGA).
[0067] The computing system 1100 may also include a memory 1106 (main memory), for example, Random Access Memory (RAM) or other dynamic memory, for storing information and instructions to be executed by the processor 1102. The memory 1106 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by the processor 1102. The computing system 1100 may likewise include a read only memory (“ROM”) or other static storage device coupled to bus 1104 for storing static information and instructions for the processor 1102.
[0068] The computing system 1100 may also include a storage devices 1108, which may include, for example, a media drive 1110 and a removable storage interface. The media drive 1110 may include a drive or other mechanism to support fixed or removable storage media, such as a hard disk drive, a floppy disk drive, a magnetic tape drive, an SD card port, a USB port, a micro USB, an optical disk drive, a CD or DVD drive (R or RW), or other removable or fixed media drive. A storage media 1112 may include, for example, a hard disk, magnetic tape, flash drive, or other fixed or removable medium that is read by and written to by the media drive 1110. As these examples illustrate, the storage media 1112 may include a computer-readable storage medium having stored therein particular computer software or data.
[0069] In alternative embodiments, the storage devices 1108 may include other similar instrumentalities for allowing computer programs or other instructions or data to be loaded into the computing system 1100. Such instrumentalities may include, for example, a removable storage unit 1114 and a storage unit interface 1116, such as a program cartridge and cartridge interface, a removable memory (for example, a flash memory or other removable memory module) and memory slot, and other removable storage units and interfaces that allow software and data to be transferred from the removable storage unit 1114 to the computing system 1100.
[0070] The computing system 1100 may also include a communications interface 1118. The communications interface 1118 may be used to allow software and data to be transferred between the computing system 1100 and external devices. Examples of the communications interface 1118 may include a network interface (such as an Ethernet or other NIC card), a communications port (such as for example, a USB port, a micro USB port), Near field Communication (NFC), etc. Software and data transferred via the communications interface 1118 are in the form of signals which may be electronic, electromagnetic, optical, or other signals capable of being received by the communications interface 1118. These signals are provided to the communications interface 1118 via a channel 1120. The channel 1120 may carry signals and may be implemented using a wireless medium, wire or cable, fiber optics, or other communications medium. Some examples of the channel 1120 may include a phone line, a cellular phone link, an RF link, a Bluetooth link, a network interface, a local or wide area network, and other communications channels.
[0071] The computing system 1100 may further include Input/Output (I/O) devices 1122. Examples may include, but are not limited to a display, keypad, microphone, audio speakers, vibrating motor, LED lights, etc. The I/O devices 1122 may receive input from a user and also display an output of the computation performed by the processor 1102. In this document, the terms “computer program product” and “computer-readable medium” may be used generally to refer to media such as, for example, the memory 1106, the storage devices 1108, the removable storage unit 1114, or signal(s) on the channel 1120. These and other forms of computer-readable media may be involved in providing one or more sequences of one or more instructions to the processor 1102 for execution. Such instructions, generally referred to as “computer program code” (which may be grouped in the form of computer programs or other groupings), when executed, enable the computing system 1100 to perform features or functions of embodiments of the present invention.
[0072] In an embodiment where the elements are implemented using software, the software may be stored in a computer-readable medium and loaded into the computing system 1100 using, for example, the removable storage unit 1114, the media drive 1110 or the communications interface 1118. The control logic (in this example, software instructions or computer program code), when executed by the processor 1102, causes the processor 1102 to perform the functions of the invention as described herein.
[0073] Various embodiments provide method and system for automated workflow management. The disclosed method and system may initiate a workflow corresponding to input data received from one or more sources. The workflow may be configured for execution through a dynamic state machine. Further, the dynamic state machine may include a plurality of states and a plurality of transitions between the plurality of states. Further, the disclosed method and system may execute the dynamic state machine based on the input data, the plurality of states, the plurality of transitions, and a set of predefined properties associated with each of the plurality of states and the plurality of transitions, to implement the workflow. Further, the execution may include in the disclosed method and system may dynamically determine, for each state of the plurality of states, via a semiotics engine, contextual data for the state based on one or more semiotic constructs in at least one of the input data or a transition from a previous state. The contextual data may be based on relationships between the plurality of states, the corresponding plurality of transitions, and the set of predefined properties. Further, the disclosed method and system may adaptively execute, for each state of the plurality of states, via one of an Artificial Intelligence (AI) model or a rule-based model, one or more actions of the state based on the contextual data and a plurality of multi-layered property groups of historical data. The one or more actions may be a part of the set of predefined properties. Further, the plurality of multi-layered property groups may be a hierarchically classified arrangement of the historical data
[0074] Thus, the disclosed method and system try to overcome the technical problem of automated workflow management. The method and system may use adaptive process management. In adaptive process management, the method and system may dynamically adjust processes based on evolving data and context, allowing for flexible handling of workflows that involve complex decision points, multi-stage actions, and varied data sources. The method and system may leverage AI models to interpret the workflow context and choose the most appropriate path or action, thus enabling real-time responsiveness in highly variable environments. The method and system may use intelligent decision support. In intelligent decision support, the method and system may use intelligent decision support, enabling users to receive real-time insights and recommendations. The method and system may empower teams to make more accurate, context-aware decisions, even in scenarios with ambiguous or rapidly changing information. The AI-driven component may enhance accuracy in decision-making and reduce the risk of human error in complex workflows.
[0075] The method and system may use seamless data interpretation. In the data interpretation, the method and system may be integrated with semiotics and property groups, allowing the system to interpret diverse data types efficiently, including structured, semi-structured, and unstructured data. The functionality may be applied to streamline workflows that involve diverse data sets, enabling the platform to extract relevant insights and categorize information dynamically without extensive preprocessing or manual input.
[0076] The method and system may use dynamic workflow orchestration. In the dynamic workflow orchestration, the method and system may use state machine-based architecture, enabling workflows to transition dynamically between states based on data-driven triggers, without requiring manual adjustments. The workflow orchestration may automatically respond to triggers such as status updates, environmental signals, or user inputs, thus enabling a streamlined and automated management of transitions.
[0077] The method and system may use real-time adaptability across domains. In real-time adaptability across domains, the method and system design may make the system adaptable to a range of domains and tasks, providing industry-agnostic applications for tasks that require real-time adjustments, workflow reconfigurations, and adaptive task handling. The method and system may be tailored to suit sector-specific needs while retaining the flexibility and responsiveness necessary for broader, multi-industry deployment.
[0078] The method and system may provide enhanced flexibility and scalability. In enhanced flexibility and scalability, the method and system may dynamically respond to context and evolving inputs. The method and system may handle both small-scale and large-scale processes, adapting seamlessly as workflows scale up or become more complex. The inherent flexibility may enable organizations to address a wider range of process management needs within a single solution.
[0079] The method and system may reduce operational complexity. The method and system's intelligent, self-adjusting nature may decrease the need for constant human oversight and manual adjustments in workflow execution. The method and system may provide a simplified complex workflow, allowing businesses to reduce operational complexity and focus on high-value tasks rather than micromanaging process steps.
[0080] The method and system may increase efficiency and cost savings. The method and system may use automated decision points and context interpretation across workflows may significantly reduce the time and resources required to complete tasks, leading to faster process execution, reducing downtime, and minimizing the cost of handling complex workflows by reducing dependencies on manual interventions.
[0081] The method and system may provide seamless integration and interoperability. The method and system may use property groups and logical data models to facilitate easier data integration across systems, creating a cohesive flow of information across organizational boundaries. Further, the interoperability of the method and system may enhance collaboration across teams and departments, reducing data silos and fostering a unified approach to process management.
[0082] The method and system may provide resilience and adaptability in changing environments. In resilience and adaptability, the method and system may have the ability to adapt to real-time data inputs and environmental changes, making the resilient and highly adaptable. The method and system may rely on the system to maintain workflow continuity and responsiveness even during shifts in business requirements, market conditions, or regulatory changes.
[0083] In light of the above-mentioned advantages and the technical advancements provided by the disclosed method and system, the claimed steps as discussed above are not routine, conventional, or well understood in the art, as the claimed steps enable the following solutions to the existing problems in conventional technologies. Further, the claimed steps clearly bring an improvement in the functioning of the device itself as the claimed steps provide a technical solution to a technical problem.
[0084] The specification has described method and system for automated workflow management. The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
[0085] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
[0086] It is intended that the disclosure and examples be considered as exemplary only, with a true scope and spirit of disclosed embodiments being indicated by the following claims. , Claims:CLAIMS
I/We Claim:
1. A method (300) for automated workflow management, the method (300) comprising:
initiating (302), by a processor (104), a workflow corresponding to input data received from one or more sources, wherein the workflow is configured for execution through a dynamic state machine (210), wherein the dynamic state machine (210) comprises a plurality of states and a plurality of transitions between the plurality of states; and
executing (304), by the processor (104), the dynamic state machine (210) based on the input data, the plurality of states, the plurality of transitions, and a set of predefined properties associated with each of the plurality of states and the plurality of transitions, to implement the workflow, wherein the executing comprises:
for each state of the plurality of states,
dynamically determining (306), by the processor (104), via a semiotics engine (212), contextual data for the state based on one or more semiotic constructs in at least one of the input data or a transition from a previous state, wherein the contextual data is based on relationships between the plurality of states, the corresponding plurality of transitions, and the set of predefined properties; and
adaptively executing (308), by the processor (104) via one of an Artificial Intelligence (AI) model or a rule-based model (214), one or more actions of the state based on the contextual data and a plurality of multi-layered property groups of historical data, wherein the one or more actions are a part of the set of predefined properties, and wherein the plurality of multi-layered property groups is a hierarchically classified arrangement of the historical data.

2. The method (300) as claimed in claim 1, comprising:
storing (402) the input data with the historical data in a historical database (216);
classifying (404) the historical data into the plurality of multi-layered property groups through a clustering technique; and
adaptively modifying (406) the plurality of multi-layered property groups based on at least one of a user feedback or a user requirement.

3. The method (300) as claimed in claim 2, wherein the plurality of multi-layered property groups is arranged in a plurality of layers, wherein each of the plurality of layers comprises one or more of the plurality of multi-layered property groups, and wherein each of the plurality of layers corresponds to a granularity level of information.

4. The method (300) as claimed in claim 2, comprising allocating (408) in real-time, via the AI model (214), computational resources across the plurality of multi-layered property groups based on performance metrics corresponding to the dynamic state machine (210).

5. The method (300) as claimed in claim 1, wherein adaptively executing the dynamic state machine (210) comprises:
upon adaptively executing the one or more actions of the state,
predicting (310), via the semiotics engine (212) and the AI model (214), a next state from the plurality of states in the dynamic state machine (210) based on the execution of the one or more actions; and
modifying (312), via the AI model (214), the set of predefined properties associated with the next state to obtain a new workflow path.

6. The method (300) as claimed in claim 1, comprising:
identifying (502) in real-time, via the semiotics engine (212), a workflow change from the contextual data; and
dynamically modifying (504), via the AI model (214), the set of predefined properties of each of one or more states or transitions from the plurality of states and the plurality of transitions based on the workflow change.

7. The method (300) as claimed in claim 1, comprising:
upon adaptively executing the dynamic state machine (314), receiving performance metrics, process outcomes, and updated objectives corresponding to the workflow; and
dynamically adjusting (316), via the AI model (214), the plurality of states and the plurality of transitions of the dynamic state machine (210) based on the performance metrics, the process outcomes, and the updated objectives.

8. The method (300) as claimed in claim 1, comprising:
identifying in real-time (602), via the semiotics engine (212), an anomaly in the workflow during execution of the dynamic state machine (210) using the semiotic constructs;
determining (604), via the AI model (214), an error diagnosis corresponding to the anomaly by backtracking through the plurality of multi-layered property groups; and
determining (606), via the AI model (214), one or more corrective actions for the anomaly based on the error diagnosis.

9. A system (100) for automated workflow management, the system (100) comprising:
a processor (104); and
a memory (106) communicatively coupled to the processor (104), wherein the memory (106) stores processor instructions, which when executed by the processor (104), cause the processor (104) to:
initiate (302) a workflow corresponding to input data received from one or more sources, wherein the workflow is configured for execution through a dynamic state machine (210), wherein the dynamic state machine (210) comprises a plurality of states and a plurality of transitions between the plurality of states;
execute (304) the dynamic state machine (210) based on the input data, the plurality of states, the plurality of transitions, and a set of predefined properties associated with each of the plurality of states and the plurality of transitions, to implement the workflow, wherein the executing comprises:
for each state of the plurality of states,
dynamically determine (306), via a semiotics engine (212), contextual data for the state based on one or more semiotic constructs in at least one of the input data or a transition from a previous state, wherein the contextual data is based on relationships between the plurality of states, the corresponding plurality of transitions, and the set of predefined properties; and
adaptively execute (308), via one of an Artificial Intelligence (AI) model or a rule-based model (214), one or more actions of the state based on the contextual data and a plurality of multi-layered property groups of historical data, wherein the one or more actions are a part of the set of predefined properties, and wherein the plurality of multi-layered property groups is a hierarchically classified arrangement of the historical data.

10. The system (100) as claimed in claim 9, wherein the processor-executable instructions, on execution, cause the processor (104) to:
store (402) the input data with the historical data in a historical database (216);
classify (404) the historical data into the plurality of multi-layered property groups through a clustering technique; and
adaptively modify (406) the plurality of multi-layered property groups based on at least one of a user feedback or a user requirement.

11. The system (100) as claimed in claim 10, wherein the plurality of multi-layer property groups is arranged in a plurality of layers, wherein each of the plurality of layers, wherein each of the plurality of layers comprises one or more of the plurality of multi-layered property groups, and wherein each of the plurality of layers corresponds to a granularity level of information.

12. The system (100) as claimed in claim 10, wherein the processor-executable instructions, on execution, cause the processor (104) to:
allocate in real-time (408), via the AI model (214), computational resources across the plurality of multi-layered property groups based on performance metrics corresponding to the dynamic state machine (210).

13. The system (100) as claimed in claim 9, wherein to adaptively execute the dynamic state machine (210), the processor instructions, on execution, cause the processor (104) to:
upon adaptively execute the one or more actions of the state,
predict (310), via the semiotics engine (212) and the AI model (214), a next state from the plurality of states in the dynamic state machine (210) based on the execution of the one or more actions; and
modify (312), via the AI model (214), the set of predefined properties associated with the next state to obtain a new workflow path.

14. The system (100) as claimed in claim 9, wherein the processor-executable instructions, on execution, cause the processor (104) to:
identify in real-time(502), via the semiotics engine (212), a workflow change from the contextual data; and
dynamically modify (504), via the AI model (214), the set of predefined properties of each of one or more states or transitions from the plurality of states and the plurality of transitions based on the workflow change.

15. The system (100) as claimed in claim 9, wherein the processor-executable instructions, on execution, cause the processor (104) to:
upon adaptively execute the dynamic state machine (210), receive (314) performance metrics, process outcomes, and updated objectives corresponding to the workflow; and
dynamically adjust (316), via the AI model (214), the plurality of states and the plurality of transitions of the dynamic state machine (210) based on the performance metrics, the process outcomes, and the updated objectives.

16. The system (100) as claimed in claim 9, wherein the processor-executable instructions, on execution, cause the processor (104) to:
identify in real-time (602), via the semiotics engine (212), an anomaly in the workflow during execution of the dynamic state machine (210) using the semiotic constructs;
determine (604), via the AI model (214), an error diagnosis corresponding to the anomaly by backtracking through the plurality of multi-layered property groups; and
determine (606), via the AI model (214), one or more corrective actions for the anomaly based on the error diagnosis.