ABSTRACT
Abstract
The present invention provides a large language model-based system and method for data processing in application developed by codeless platform. The invention includes identification of intent of a user to process procurement, supply chain, application integration, application restructuring or development scenarios.
Description
BACKGROUND
1. Technical Field
The present invention relates generally to data processing. More particularly, the invention relates to large language models-based data processing in one or more enterprise applications including procurement and supply chain applications developed by codeless platform.
2. Description of the Prior Art
Traditional Enterprise applications including procurement and supply Chain applications cater to support collaboration between limited parties at any node in the enterprise application. In modern enterprise applications the number of parties involved has increased significantly with each of the parties being a specialist in the niche function they perform. Further, the existing enterprise applications dealing with multiple parties make the process cumbersome, time-consuming, and require manual intervention while dealing with complex scenarios. They frequently entail several channels, intricate workflows and human intervention leading to inefficiency which results in errors, delays, escalated expenditure, maverick spend and in turn increased cost of procurement. The rise of artificial intelligence (AI) has offered promising solutions to streamline enterprise application functions, with natural language processing (NLP) playing a key role in enabling human-like communication. However, existing solutions often lack the context awareness, adaptability, and conversational fluency needed for a truly autonomous experience.
Large language models perform various natural language processing (NLP) tasks with vast amounts of data. For any enterprise, data is extremely critical but more importantly meaningful data is of extreme value as it helps in decision making related to vital functions. To meet any operational requirement in an enterprise application, the ease in enabling the system to process information plays a critical role. The interaction of a user with the computing system in trying to execute the most complex tasks seamlessly is the growing need of the hour. While identification of intent of the user for determining the task to be executed is critical, the complexity of the nature of the task makes it extremely challenging and technically cumbersome to implement.
Enterprise application developed based on codeless platform present additional challenges while dealing with real time data processing. The complexity in structuring an application is technically challenging and impractical for a non-technical individual not familiar with programming concepts and paradigms to even understand the requirement. While certain aspects of application development may be addressed through user friendly interface, the complexity of tasks in enterprise applications related to procurement and supply chain makes it cumbersome to meet the requirement. The architecture of the codeless platform remains unsupportive in multiple aspects including working with different data abstraction. Moreover, for the computer to understand varied requirements of the user accurately is a big challenge.
None of the prior arts address the processing complexity and technical limitations in executing tasks associated with an enterprise application that are developed by codeless platform. Moreover, implementation of large language models for such enterprise applications developed by codeless platform are non-existent due the unknows and the existing complexity in data processing for deriving meaningful insights to enable execution of the required enterprise application function. Further, while scalability of the processing capability of existing computing resources while dealing with large language model is extremely challenging, such scaling in case of multiple large language models interacting to execute an enterprise function is even more cumbersome. Furthermore, existing data processing techniques for identification of intent of a user to understand the requirement and accordingly restructure the workflow, data processing steps and deal with the unknows is extremely limited and non-existent due to the varied requirements.
In view of the above problems, there is a need for a data processing system and method that can overcome the problems associated with the prior arts.
SUMMARY
According to an embodiment, the present invention provides a system and method for large language model-based data processing in one or more applications developed by codeless development platform. The data processing comprises generating by a processing device, a graphical user interface (GUI) having a conversational assistant configured for receiving at least one input from a user. The method includes determining an intent of the user based on one or more data objects identified from the received input where the conversation assistant is configured to generate one or more query in response to the received input until the intent of the user is identified by an intent analyzer, triggering one or more LLM (large language model) agent for executing at least one task associated with the identified intent of the user wherein a process orchestrator invokes one or more tools identified by the LLM agent for executing the at least one task. The data processing method includes generating on the GUI, one or more graphical elements depicting one or more actionable data points associated with the executed task.
In an embodiment, the intent analyzer is a bot configured to parse the intent of the user based on the identified data objects and mapping the intent with the LLM agent, wherein one or more one data scripts are identified based on the parsed intent to trigger the at least one task.
In an embodiment, the data processing system and method includes a bot builder configured to process one or more historical data for generating and storing, training artifacts and flow artifacts in an intent database. The intent analyzer processes the received input based on one or more intent data models to identify the intent.
In a related embodiment, the intent analyzer is configured to analyze the intent from the at least one received input by converting the received input into numerical representation through embeddings, creating embeddings, one or more clusters during training and receiving sample prompts from users where each cluster represents a different intent. The intent analyzer identifies cluster nearest to an embedding representation of the received input to determine the intent, where a generative AI based reasoning model enables mapping of the received input to the intent in case the embedding representation is equally close to different clusters.
In an embodiment, parsing intent of the user includes predicting one or more procurement scenarios, supply chain scenarios, application integration scenarios, application restructuring or application development scenarios intended to be executed by the user as the at least one task, the bot identifies one or more nodes of a data network linked to the at least one task for parsing the intent.
In an embodiment, the system and method of the invention includes processing by an AI engine coupled to a processor, a plurality of historical procurement and user activity data from a data lake based on one or more procurement data models to generate code for a recommended strategy to execute the at least one task through prediction analysis.
In an embodiment, the data processing method of the invention includes injecting by an intelligent bot, aggregated user activity data and procurement data patterns related to one or more procurement categories into the recommended strategy. The method includes identifying one or more suppliers for executing the recommended strategy and encapsulating one or more recommended supplier awarding scenario on the GUI for selection.
In an embodiment, for the one or more application integration scenarios, the method includes identifying one or more entities, one or more application integration parameters, and the one or more integration data models from the data object for executing the at least one task of integration the one or more applications.
In a related embodiment, the data processing method for one or more application integration scenarios includes identifying source and target for executing the at least one task by automapping. The includes loading source and target files of syntax based structured data and extracting source path from a historical structured data database, and tokenizing source path and fetching matching target paths from Inverted Index supported historical database for automapping source and target.
In a related embodiment, matching of target paths includes converting each object of source to vector by word embedding, computing dot products and magnitude of the vectors to determine similarity, and determining similarity score for each object of target.
In another related embodiment, in response to determination of the application for integrations, generating one or more integration workflows by an intelligent bot, identifying by the bot, one or more configuration parameters for integration, and injecting by the bot, the configuration parameters into the one or more integration workflows for creating and deploying integration of the applications.
In an embodiment, the data processing method for application restructuring scenarios includes determining, by a processor, a requirement to restructure the one or more applications developed by a codeless platform as the at least one task, identifying by the one or more tools, one or more logical flow blocks to be invoked by the processor for creating one or more SCM application operation logical fragments configured to restructure the one or more applications, triggering a syntax data library by the processor, to enable the one or more tools to load one or more data library components on an extension tool interface for structuring the one or more logical flow blocks to create the one or more SCM application operation logical fragments, and restructuring the one or more applications by the one or more SCM application operation logical fragments to enable execution of at least one SCM application operation.
In a related embodiment, the data processing method for application development includes determining by a processor, a requirement to create one or more applications as the at least one task, wherein the one or more application is developed by a codeless platform. The method includes identifying by one or more tools, a plurality of configurable components invoked by the processor to be structured on a user interface for creating the one or more application, wherein the plurality of configurable components interacts through an application process orchestrator for executing the at least one task.
In an exemplary embodiment, the conversation assistant of the invention is configured to recommend one or more templates or components for customization of the one or more application, define data structures, relationships and rules, data models and validation rules, in response to a request for creating a user interface, recommend one or more interface components and generate a corresponding code for execution, and recommend data patterns and explain behavior and effects of different orchestrations.
The codeless platform includes a plurality of configurable components, a customization layer, an application layer, a shared framework layer, a foundation layer, a data layer and a application orchestrator, wherein the at least one processor is configured to cause the plurality of configurable components to interact with each other in a layered architecture to customize the one or more application based on at least one operation to be executed using the customization layer, organize at least one application service of the one or more application by causing the application layer to interact with the customization layer through one or more configurable components of the plurality of configurable components, wherein the application layer is configured to organize the at least one application service of the one or more application, fetch shared data objects to enable execution of the at least one application service by causing the shared framework layer to communicate with the application layer through one or more configurable components of the plurality of configurable components, wherein the shared framework layer is configured to fetch the shared data objects to enable execution of the at least one application service, wherein fetching of the shared data objects is enabled via the foundation layer communicating with the shared framework layer, wherein the foundation layer is configured for infrastructure development through the one or more configurable components of the plurality of configurable components, manage database native queries mapped to that at least one operation using a data layer to communicate with the foundation layer through one or more configurable components of the plurality of configurable components, wherein the data layer is configured to manage database native queries mapped to the at least one operation; and execute the at least one operation and develop the one or more application using the application orchestrator to enable interaction of the plurality of configurable components in the layered architecture.
In an advantageous aspect, the codeless development platform architecture is a layered architecture structured to execute a plurality of complex enterprise application operations in an organized and less time-consuming manner due to faster processing as the underlining architecture is appropriately defined to execute the operations through shortest path. Further, the platform architecture enables secured data flow through applications and resolution of code break issues without affecting neighboring functions or application. Moreover, the large language model's (LLM) accuracy of processing any input to execute a task is dependent on the efficiency of processing real time datasets generated due to the codeless platform architecture. The LLM agent is configured to process inputs by considering the real time datasets generated in one or more application developed by codeless platform. The technical problem in accurately identifying varied intents for executing a task related to procurement, supply chain, application integration, application restructuring or application development is addressed through learning and processing by large language models.
In another advantageous aspect, the present invention utilizes Machine Learning algorithms, large language models, artificial intelligence-based process orchestration for data processing to identify user intent in varied scenarios for executing the required task.
BRIEF DESCRIPTION OF THE DRAWINGS
The disclosure will be better understood when consideration is given to the drawings and the detailed description which follows. Such description makes reference to the annexed drawings wherein:
FIG. 1 is an architecture diagram of a large language model-based data processing system configured for one or more applications developed by a codeless platform in accordance with an embodiment of the invention.
FIG. 2 a flow diagram of a large language model (LLM) based data processing method is provided in accordance with an embodiment of the invention.
FIG. 3 is a block diagram depicting a bot builder flow for the data processing system in accordance with an embodiment of the invention.
FIG. 4 is a block diagram depicting a bot builder training flow for the data processing system in accordance with an embodiment of the invention.
<div id="p-0030" num="0029" class="description-
BACKGROUND
1. Technical Field
The present invention relates generally to data processing. More particularly, the invention relates to large language models-based data processing in one or more enterprise applications including procurement and supply chain applications developed by codeless platform.
2. Description of the Prior Art
Traditional Enterprise applications including procurement and supply Chain applications cater to support collaboration between limited parties at any node in the enterprise application. In modern enterprise applications the number of parties involved has increased significantly with each of the parties being a specialist in the niche function they perform. Further, the existing enterprise applications dealing with multiple parties make the process cumbersome, time-consuming, and require manual intervention while dealing with complex scenarios. They frequently entail several channels, intricate workflows and human intervention leading to inefficiency which results in errors, delays, escalated expenditure, maverick spend and in turn increased cost of procurement. The rise of artificial intelligence (AI) has offered promising solutions to streamline enterprise application functions, with natural language processing (NLP) playing a key role in enabling human-like communication. However, existing solutions often lack the context awareness, adaptability, and conversational fluency needed for a truly autonomous experience.
Large language models perform various natural language processing (NLP) tasks with vast amounts of data. For any enterprise, data is extremely critical but more importantly meaningful data is of extreme value as it helps in decision making related to vital functions. To meet any operational requirement in an enterprise application, the ease in enabling the system to process information plays a critical role. The interaction of a user with the computing system in trying to execute the most complex tasks seamlessly is the growing need of the hour. While identification of intent of the user for determining the task to be executed is critical, the complexity of the nature of the task makes it extremely challenging and technically cumbersome to implement.
Enterprise application developed based on codeless platform present additional challenges while dealing with real time data processing. The complexity in structuring an application is technically challenging and impractical for a non-technical individual not familiar with programming concepts and paradigms to even understand the requirement. While certain aspects of application development may be addressed through user friendly interface, the complexity of tasks in enterprise applications related to procurement and supply chain makes it cumbersome to meet the requirement. The architecture of the codeless platform remains unsupportive in multiple aspects including working with different data abstraction. Moreover, for the computer to understand varied requirements of the user accurately is a big challenge.
None of the prior arts address the processing complexity and technical limitations in executing tasks associated with an enterprise application that are developed by codeless platform. Moreover, implementation of large language models for such enterprise applications developed by codeless platform are non-existent due the unknows and the existing complexity in data processing for deriving meaningful insights to enable execution of the required enterprise application function. Further, while scalability of the processing capability of existing computing resources while dealing with large language model is extremely challenging, such scaling in case of multiple large language models interacting to execute an enterprise function is even more cumbersome. Furthermore, existing data processing techniques for identification of intent of a user to understand the requirement and accordingly restructure the workflow, data processing steps and deal with the unknows is extremely limited and non-existent due to the varied requirements.
In view of the above problems, there is a need for a data processing system and method that can overcome the problems associated with the prior arts.
SUMMARY
According to an embodiment, the present invention provides a system and method for large language model-based data processing in one or more applications developed by codeless development platform. The data processing comprises generating by a processing device, a graphical user interface (GUI) having a conversational assistant configured for receiving at least one input from a user. The method includes determining an intent of the user based on one or more data objects identified from the received input where the conversation assistant is configured to generate one or more query in response to the received input until the intent of the user is identified by an intent analyzer, triggering one or more LLM (large language model) agent for executing at least one task associated with the identified intent of the user wherein a process orchestrator invokes one or more tools identified by the LLM agent for executing the at least one task. The data processing method includes generating on the GUI, one or more graphical elements depicting one or more actionable data points associated with the executed task.
In an embodiment, the intent analyzer is a bot configured to parse the intent of the user based on the identified data objects and mapping the intent with the LLM agent, wherein one or more one data scripts are identified based on the parsed intent to trigger the at least one task.
In an embodiment, the data processing system and method includes a bot builder configured to process one or more historical data for generating and storing, training artifacts and flow artifacts in an intent database. The intent analyzer processes the received input based on one or more intent data models to identify the intent.
In a related embodiment, the intent analyzer is configured to analyze the intent from the at least one received input by converting the received input into numerical representation through embeddings, creating embeddings, one or more clusters during training and receiving sample prompts from users where each cluster represents a different intent. The intent analyzer identifies cluster nearest to an embedding representation of the received input to determine the intent, where a generative AI based reasoning model enables mapping of the received input to the intent in case the embedding representation is equally close to different clusters.
In an embodiment, parsing intent of the user includes predicting one or more procurement scenarios, supply chain scenarios, application integration scenarios, application restructuring or application development scenarios intended to be executed by the user as the at least one task, the bot identifies one or more nodes of a data network linked to the at least one task for parsing the intent.
In an embodiment, the system and method of the invention includes processing by an AI engine coupled to a processor, a plurality of historical procurement and user activity data from a data lake based on one or more procurement data models to generate code for a recommended strategy to execute the at least one task through prediction analysis.
In an embodiment, the data processing method of the invention includes injecting by an intelligent bot, aggregated user activity data and procurement data patterns related to one or more procurement categories into the recommended strategy. The method includes identifying one or more suppliers for executing the recommended strategy and encapsulating one or more recommended supplier awarding scenario on the GUI for selection.
In an embodiment, for the one or more application integration scenarios, the method includes identifying one or more entities, one or more application integration parameters, and the one or more integration data models from the data object for executing the at least one task of integration the one or more applications.
In a related embodiment, the data processing method for one or more application integration scenarios includes identifying source and target for executing the at least one task by automapping. The includes loading source and target files of syntax based structured data and extracting source path from a historical structured data database, and tokenizing source path and fetching matching target paths from Inverted Index supported historical database for automapping source and target.
In a related embodiment, matching of target paths includes converting each object of source to vector by word embedding, computing dot products and magnitude of the vectors to determine similarity, and determining similarity score for each object of target.
In another related embodiment, in response to determination of the application for integrations, generating one or more integration workflows by an intelligent bot, identifying by the bot, one or more configuration parameters for integration, and injecting by the bot, the configuration parameters into the one or more integration workflows for creating and deploying integration of the applications.
In an embodiment, the data processing method for application restructuring scenarios includes determining, by a processor, a requirement to restructure the one or more applications developed by a codeless platform as the at least one task, identifying by the one or more tools, one or more logical flow blocks to be invoked by the processor for creating one or more SCM application operation logical fragments configured to restructure the one or more applications, triggering a syntax data library by the processor, to enable the one or more tools to load one or more data library components on an extension tool interface for structuring the one or more logical flow blocks to create the one or more SCM application operation logical fragments, and restructuring the one or more applications by the one or more SCM application operation logical fragments to enable execution of at least one SCM application operation.
In a related embodiment, the data processing method for application development includes determining by a processor, a requirement to create one or more applications as the at least one task, wherein the one or more application is developed by a codeless platform. The method includes identifying by one or more tools, a plurality of configurable components invoked by the processor to be structured on a user interface for creating the one or more application, wherein the plurality of configurable components interacts through an application process orchestrator for executing the at least one task.
In an exemplary embodiment, the conversation assistant of the invention is configured to recommend one or more templates or components for customization of the one or more application, define data structures, relationships and rules, data models and validation rules, in response to a request for creating a user interface, recommend one or more interface components and generate a corresponding code for execution, and recommend data patterns and explain behavior and effects of different orchestrations.
The codeless platform includes a plurality of configurable components, a customization layer, an application layer, a shared framework layer, a foundation layer, a data layer and a application orchestrator, wherein the at least one processor is configured to cause the plurality of configurable components to interact with each other in a layered architecture to customize the one or more application based on at least one operation to be executed using the customization layer, organize at least one application service of the one or more application by causing the application layer to interact with the customization layer through one or more configurable components of the plurality of configurable components, wherein the application layer is configured to organize the at least one application service of the one or more application, fetch shared data objects to enable execution of the at least one application service by causing the shared framework layer to communicate with the application layer through one or more configurable components of the plurality of configurable components, wherein the shared framework layer is configured to fetch the shared data objects to enable execution of the at least one application service, wherein fetching of the shared data objects is enabled via the foundation layer communicating with the shared framework layer, wherein the foundation layer is configured for infrastructure development through the one or more configurable components of the plurality of configurable components, manage database native queries mapped to that at least one operation using a data layer to communicate with the foundation layer through one or more configurable components of the plurality of configurable components, wherein the data layer is configured to manage database native queries mapped to the at least one operation; and execute the at least one operation and develop the one or more application using the application orchestrator to enable interaction of the plurality of configurable components in the layered architecture.
In an advantageous aspect, the codeless development platform architecture is a layered architecture structured to execute a plurality of complex enterprise application operations in an organized and less time-consuming manner due to faster processing as the underlining architecture is appropriately defined to execute the operations through shortest path. Further, the platform architecture enables secured data flow through applications and resolution of code break issues without affecting neighboring functions or application. Moreover, the large language model's (LLM) accuracy of processing any input to execute a task is dependent on the efficiency of processing real time datasets generated due to the codeless platform architecture. The LLM agent is configured to process inputs by considering the real time datasets generated in one or more application developed by codeless platform. The technical problem in accurately identifying varied intents for executing a task related to procurement, supply chain, application integration, application restructuring or application development is addressed through learning and processing by large language models.
In another advantageous aspect, the present invention utilizes Machine Learning algorithms, large language models, artificial intelligence-based process orchestration for data processing to identify user intent in varied scenarios for executing the required task.
BRIEF DESCRIPTION OF THE DRAWINGS
The disclosure will be better understood when consideration is given to the drawings and the detailed description which follows. Such description makes reference to the annexed drawings wherein:
FIG. 1 is an architecture diagram of a large language model-based data processing system configured for one or more applications developed by a codeless platform in accordance with an embodiment of the invention.
FIG. 2 a flow diagram of a large language model (LLM) based data processing method is provided in accordance with an embodiment of the invention.
FIG. 3 is a block diagram depicting a bot builder flow for the data processing system in accordance with an embodiment of the invention.
FIG. 4 is a block diagram depicting a bot builder training flow for the data processing system in accordance with an embodiment of the invention.
FIG. 5 shows a block diagram depicting a bot builder inference flow for the data processing system in accordance with an example embodiment of the invention.
FIG. 6 shows deep learning based large language model architecture with encoder and decoder in accordance with an example embodiment of the invention.
FIG. 6 A shows neural network of the data processing system in accordance with an example embodiment of the invention.
FIG. 7 shows a block diagram depicting a conversational assistant driven autonomous procurement system in accordance with an embodiment of the invention.
FIG. 8 , shows an electronic user interface screen of the data processing system having a chatbot for conversation orchestration to execute contract creation task in accordance with an example embodiment of the invention.
FIG. 8 A , shows an electronic user interface screen of the data processing system having a chatbot for conversation orchestration to execute contract creation task showing one or more templates in accordance with an example embodiment of the invention.
FIG. 8 B , shows an electronic user interface screen of the data processing system with contract template and recommended clauses in accordance with an example embodiment of the invention.
FIG. 8 C , shows a user interface of a contract module persona of the data processing system in accordance with an example embodiment of the invention.
FIG. 9 shows a user interface of a real time shipment tracking and multi-model shipment tracking in accordance with an example embodiment of the invention.
FIG. 8 A shows user input with a natural language command of the data processing system in accordance with an example embodiment of the invention.
FIG. 8 B shows a response of the data processing system to the user input in accordance with an embodiment of the invention.
FIG. 8 C shows a training data format of the data processing system in accordance with an example embodiment of the invention.
FIG. 9 shows an electronic user interface of the data processing system having a chatbot for conversation orchestration to execute contract creation task in accordance with an example embodiment of the invention.
FIG. 10 shows a table of how the data processing system creates a Supply Chain application by assembling the units of work of Codeless platform in accordance with an example embodiment of the invention.
FIG. 11 shows a graphical user interface (GUI) with a conversational assistant for application development or restructuring in accordance with an embodiment of the invention.
FIG. 12 shows a user interface with a conversational assistant configured for executing application integration tasks in accordance with an embodiment of the invention.
FIG. 12 A shows a user interface with options of integrating one application to another enterprise application in accordance with an embodiment of the invention.
FIG. 12 B shows a user interface with deployment of integration of one application with another ERP application in accordance with an example embodiment of the invention.
FIG. 12 C shows a user interface depicting supplier integration of master data and transaction data of one application with another enterprise application in accordance with an example embodiment of the invention.
DETAILED DESCRIPTION
Described herein are the various embodiments of the present invention, which includes large language model-based data processing system and method for one or more application developed by a codeless platform.
The various embodiments including the example embodiments will now be described more fully with reference to the accompanying drawings, in which the various embodiments of the invention are shown. The invention may, however, be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. In the drawings, the sizes of components may be exaggerated for clarity.
It will be understood that when an element or layer is referred to as being âon,â âconnected to,â or âcoupled toâ another element or layer, it can be directly on, connected to, or coupled to the other element or layer or intervening elements or layers that may be present. As used herein, the term âand/orâ includes any and all combinations of one or more of the associated listed items.
Spatially relative terms, such as âLarge language model (LLM),â âmachine learning (ML)â, or âLarge graph model (LGM),â and the like, may be used herein for ease of description to describe one element or feature's relationship to another element(s) or feature(s) as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations of the structure in use or operation in addition to the orientation depicted in the figures.
The subject matter of various embodiments, as disclosed herein, is described with specificity to meet statutory requirements. However, the description itself is not intended to limit the scope of this patent. Rather, the inventors have contemplated that the claimed subject matter might also be embodied in other ways, to include different features or combinations of features similar to the ones described in this document, in conjunction with other technologies. Generally, the various embodiments including the example embodiments relate to a large language model-based data processing system and method of procurement and supply chain application developed by codeless platform.
Referring to FIG. 1 , an architecture diagram of a large language model-based data processing system 100 in a procurement and supply chain application developed by a codeless platform is provided in accordance with an embodiment of the present invention. The architecture of the data processing system includes a codeless platform architecture 100 A and a large language model architecture 100 B.
The codeless platform architecture 100 A of the system 100 is a layered architecture 100 A configured to process complex operations of one or more applications including supply chain management (SCM) applications using configurable components of each layer of the architecture 100 A. The layered architecture enables faster processing of complex operations as the workflow may be reorganized dynamically using the configurable components. The layered architecture includes a data layer 101 , a foundation layer 102 , a shared framework layer 103 , an application layer 104 and a customization layer 105 . Each layer of the codeless platform architecture 100 A includes a plurality of configurable components interacting with each other to execute at least one operation of the SCM enterprise application. It shall be apparent to a person skilled in the art that while FIG. 1 provide essential configurable components, the nature of the components itself enables redesigning of the platform architecture through addition, deletion, modification of the configurable components and their positioning in the layered architecture. Such addition, modification of configurable components depending on the nature of the architecture layer function shall be within the scope of this invention.
In an exemplary embodiment, the configurable components enable an application developer user/citizen developer, a platform developer user and a SCM application user working with the SCM application to execute the operations to code the elements of the SCM application through configurable components. The SCM application user or end user triggers and interacts with the customization layer 105 for execution of the operation through application user machine 106 , a function developer user or citizen developer user triggers and interacts with the application layer 104 to develop the SCM application for execution of the operation through citizen developer machine, and a platform developer user through its computing device triggers the shared framework layer 103 , the foundation layer 102 and the data layer 101 to structure the platform for enabling codeless development of SCM applications.
In an embodiment the present invention provides one or more SCM enterprise application with an end user application UI and a citizen developer user application UI for structuring the interface to carry out the required operations. Further, the layered platform architecture reduces complexity as the layers are built one upon another thereby providing high levels of abstraction, making it extremely easy to build complex features for the SCM application. However, one or more applications developed through the platform architecture requires reconfiguration of task management in the application. Since the functions are added or removed or modified by the developer seamlessly, the reconfiguration of the system to manage the related changes in the task is cumbersome.
In one embodiment, the codeless platform architecture 100 A provides the cloud agnostic data layer 101 as a bottom layer of the architecture. This layer provides a set of microservices that collectively enable discovery, lookup and matching of storage capabilities to needs for execution of operational requirement. The layer enables routing of requests to the appropriate storage adaptation, translation of any requests to a format understandable to the underlying storage engine (relational, key-value, document, graph, etc.). Further, the layer manages connection pooling and communication with the underlying storage provider and automatically scales and de-scaling the underlying storage infrastructure to support operational growth demands.
In an example embodiment, a document data stores data abstraction of the data layer store all attributes of a document as a single record, much like a relational database system. The data is usually denormalized in these document stores, making data joins common in traditional relational systems unnecessary. Data joins (or even complex queries) can be expensive with this data store, as they typically require map/reduce operations which don't lend themselves well in transactional systems (OLTP-online transactional processing).
In another example embodiment, a relational data abstraction of the data layer allows for data to be sliced and analyzed in an extremely flexible manner.
In a related embodiment, the plurality of configurable components includes one or more data layer configurable components including but not limited to Query builder, graph database parser, data service connector, transaction handler, document structure parser, event store parser and tenant access manager. The data layer provides abstracted layers to the SCM service to perform data operations like Query, insert, update, delete and Join on various types of data stores document database (DB) structure, relational structure, key value structure and hierarchical structure.
In an embodiment the platform architecture provides the foundation layer 102 on top of the data layer 101 of the architecture 100 . This layer provides a set of microservices that execute the tasks of managing code deployment, supporting code versioning, deployment (gradual roll out of new code) etc. The layer collectively enables creation and management of smart forms (and templates), framework to define UI screens, controls etc. through use of templates. Seamless theming support is built to enable specific form instances (created at runtime) to have personalized themes, extensive customization of the user experience (UX) for each client entity and or document. The layer enables creation, storage and management of code plug-ins (along with versioning support). The layer includes microservice and libraries that enable traffic management of transactional document data (by client entity, by document, by template, etc.) to the data layer 101 , enables logging and deep call-trace instrumentation, support for request throttling, circuit breaker retry support and similar functions. Another set of microservice enables service to service API authentication support, so API calls are always secured. The foundation layer micro services enable provisioning (on boarding new client entity and documents), deployment and scaling of necessary infrastructure to support multi-tenant use of the platform. The set of microservices of foundation layer are the only way any higher layer microservice can talk to the data layer microservices. Further, machine learning techniques auto-scale the platforms to optimize costs and recommend deployment options for entity such as switching to other cloud vendors etc.
In an exemplary embodiment, the data layer 101 and foundation layer 102 of the architecture 100 function independent of the knowledge of the operation. Since, the platform architecture builds certain configurable component as independent of the operation in the application, they are easily modifiable and restructured.
In a related embodiment, the plurality of configurable components includes one or more foundation layer configurable components including but not limited to logger, Exception Manager, Configurator Caching, Communication Layer, Event Broker, Infra configuration, Email Sender, SMS Notification, Push notification, Authentication component, Office document Manager, Image Processing Manager, PDF Processing Manager, UI Routing, UI Channel Service, UI Plugin injector, Timer Service, Event handler, and Compare service for managing infrastructure and libraries to connect with cloud computing service.
In an embodiment, the platform architecture provides the shared framework layer 103 on top of the foundation layer 102 . This layer provides a set of microservices that collectively enable authentication (identity verification) and authorization (permissioning) services. The layer supports cross-document and common functions such as rule engine, workflow management, document approval (built likely on top of the workflow management service), queue management, notification management, one-to-many and many-to-one cross-document creation/management, etc. The layer enables creation and management of schemas (aka documents), and support orchestration services to provide distributed transaction management (across documents). The service orchestration understands different document types, hierarchy and chaining of the documents etc.
The shared framework layer 103 has the notion of our operational or application domains, the set of microservices that contribute this layer hosts all the common functionality so individual documents (implemented at the application layer 104 ) do not have to repeatedly to the same work. In addition to avoiding the reinventing the wheel separately by each developer team, this layer of microservices standardizes the capabilities so there is no loss of features at the document level, be it adding an attribute (that applies to a set of documents), supporting complex approval workflows, etc. The rule engine along with tools to manage rules is part of this layer.
In a related embodiment, the plurality of configurable components includes one or more shared framework configurable components including but not limited to license manager, Esign service, application marketplace service, Item Master Data Component, organization and accounting structure data component, master data, Import and Export component, Tree Component, Rule Engine, Workflow Engine, Expression Engine, Notification, Scheduler, Event Manager, and version service.
In one embodiment, architecture 100 provides the application layer 104 on top of the shared framework layer 103 of the architecture. The developer user of the platform will interact with application layer 103 for structuring the SCM application. This is also the first layer that defines SCM specific documents such as requisitions, contracts, orders, invoices etc. This layer provides a set of microservices to support creation of documents (requisition, order, invoice, etc.), support the interaction of the documents with other documents (ex: invoice matching, budget amortization, etc.) and provide differentiated operational/functional value for the documents in comparison to a competition by using artificial intelligence and machine learning. This layer also enables execution of complex operational/functional use cases involving the documents.
In an exemplary embodiment, a developer user or admin user will structure one or more SCM application and associated functionality by the application layer of microservices, either by leveraging the shared frameworks platform layer or through code to enable the notion of specific documents or through building complex functionality by intermingling shared frameworks platform capabilities with custom code. Besides passing on the entity metadata to the shared frameworks layer, this set of microservices do not carry any concern about where or how data is stored. Data modeling is done through template definitions and API calls to the shared frameworks platform layer. This enables this layer to primarily and solely focus on adding operational/functional value without worrying about infrastructure.
Further, in an advantageous aspect, all functionality or application services built at the application layer are exposed through an object model, so higher levels of application orchestrations of all these functionalities is possible to build by custom implementations for end users. The platform will stay pristine and clean and be generic, while at the same time, enables truly custom features to be built in a lightweight and agile manner. The system of the invention is configured to adapt to the changes in the application due to the custom features and operate the application to manage one or more tasks to be executed.
In an embodiment, architecture 100 provides the customization layer 105 as the topmost layer of the architecture above the application layer 104 . This layer provides microservices enabling end users to write codes to customize the operational flows as well as the end user application UI to execute the operations of SCM. The end user can orchestrate the objects exposed by the application layer 104 to build custom functionality, to enable nuanced and complex workflows that are specific to the end user operational requirement or a third-party implementation user.
In a related embodiment, the plurality of configurable components includes one or more customization layer configurable components including but not limited to a plurality of rule engine components, configurable logic component, component for structuring SCM application UI, Layout Manager, Form Generator, Expression Builder Component, Field & Metadata Manager, store-manager, Internationalization Component, Theme Selector Component, Notification Component, Workflow Configurator, Custom Field Component & Manager, Dashboard Manager, Code Generator and Extender, Notification, Scheduler, form Template manager, State and Action configurator for structuring the one or more SCM application to execute at least one SCM application operation.
In an exemplary embodiment, each of these layers of the platform architecture communicates or interacts only to the layer directly below and never bypasses the layers through operational workflow thereby enabling highly productive execution with secured interaction through the architecture.
Depending on the type of user the user interface (UI) of the application user machine 106 is structured by the platform architecture. The application user machine 106 with a application user UI is configured for sending, receiving, modifying or triggering processes and data object for operating one or more of a SCM application over a network 107 .
The computing devices referred to as the entity machine, server, processor etc. of the present invention are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, and other appropriate computers. Computing devices of the present invention further intend to represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed in this disclosure.
The system includes a server 108 configured to receive data and instructions from the application user machines 106 . The system 100 includes a support mechanism for performing various prediction through AI engine and mitigation processes with multiple functions including historical dataset extraction, classification of historical datasets, artificial intelligence-based processing of new datasets and structuring of data attributes for analysis of data, creation of one or more data models configured to process different parameters.
In an embodiment, the system is provided in a cloud or cloud-based computing environment. The codeless development system enables more secured processes.
In an embodiment the server 108 of the invention may include various sub-servers for communicating and processing data across the network. The sub-servers include but are not limited to content management server, application server, directory server, database server, mobile information server and real-time communication server.
In example embodiment the server 108 shall include electronic circuitry for enabling execution of various steps by server processor. The electronic circuitry has various elements including but not limited to a plurality of arithmetic logic units (ALU) and floating-point Units (FPU's). The ALU enables processing of binary integers to assist in formation of at least one table of data attributes where the data models implemented for dataset characteristic prediction are applied to the data table for obtaining prediction data and recommending action for codeless development of SCM applications. In an example embodiment the server electronic circuitry includes at least one Athematic logic unit (ALU), floating point units (FPU), other processors, memory, storage devices, high-speed interfaces connected through buses for connecting to memory and high-speed expansion ports, and a low-speed interface connecting to low-speed bus and storage device. Each of the components of the electronic circuitry, are interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate. The processor can process instructions for execution within the server 108 , including instructions stored in the memory or on the storage devices to display graphical information for a graphical user interface (GUI) on an external input/output device, such as display coupled to high-speed interface. In other implementations, multiple processors and/or multiple busses may be used, as appropriate, along with multiple memories and types of memory. Also, multiple servers may be connected, with each server providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system).
In an example embodiment, the system of the present invention includes a front-end web server communicatively coupled to at least one database server, where the front-end web server is configured to process data based on large language models and applying an AI based dynamic processing logic to automate prioritization of task in the application developed by the codeless development actions through orchestrator.
In an embodiment, the platform architecture 100 of the invention includes an application orchestrator 109 configured for enabling interaction of the plurality of configurable components in the layered architecture 100 for executing at least one SCM application operation and development of the one or more SCM application based on one or more LLM agent. The application orchestrator 109 includes plurality of components including an application programming interface (API) for providing access to configuration and workflow operations of SCM application operations, an Orchestrator manager configured for Orchestration and control of SCM application operations, an orchestrator UI/cockpit for monitoring and providing visibility across transactions in SCM operations and an AI based application orchestration engine configured for interacting with a plurality of configurable components in the platform architecture for executing SCM operations.
In an embodiment, the application orchestrator includes a blockchain connector for integrating blockchain services with the one or more SCM application and interaction with one or more configurable components. Further, Configurator User interface (UI) services are used to include third party networks managed by domain providers.
In a related aspect, the Artificial intelligence (AI) based orchestrator engine enables execution of SCM operation by at least one data model wherein the AI engine transfers processed data to the User Interface (UI) for visibility, exposes SCM operations through API and assist the manager for application orchestration and control.
In an exemplary embodiment, the AI engine employs machine learning techniques that learn patterns and generate insights from the data for enabling the application orchestrator to automate operations. Further, the AI engine with ML employs deep learning that utilizes artificial neural networks to mimic biological neural networks in human brains. The artificial neural networks analyze data to determine associations and provide meaning to unidentified or new dataset.
In another embodiment, the invention enables integration of Application Programming Interfaces (APIs) for plugging aspects of AI into the dataset characteristic prediction and operations execution for operating one or more SCM enterprise application.
In an embodiment, the system 100 of the present invention includes a workflow engine that enables monitoring of workflow across the SCM applications. The workflow engine with the application orchestrator enables the platform architecture to create multiple approval workflows. The task assigned to a user is prioritized through the AI based data processing system based on real time information.
In an embodiment the machine 106 may communicate with the server 108 wirelessly through communication interface, which may include digital signal processing circuitry. Also, the machine ( 106 ) may be implemented in a number of different forms, for example, as a smartphone, computer, personal digital assistant, or other similar devices.
In an embodiment, the large language model architecture 100 B includes a processor 110 configured for receiving the input from a user through the electronic user interface and generating a response on an electronic user interface. Processor 106 serves as the bridge between the user and the backend components of the LLM architecture. The LLM architecture 100 B includes an intent analyzer 111 configured to analyze intent of the input received at through a conversation assistant of an application. Architecture 100 B, further includes a bot builder 112 configured to build a cartridge of intents through which the intent analyzers identified the intent of the received input. Architecture 100 B also includes an AI engine 113 configured to process a plurality of historical application data and user activity data from a data lake. The architecture 100 B further includes a data network 114 configured for storing and processing of one or more dataset of the one or more application developed by the codeless platform, wherein a data network server is configured to receive the one or more dataset from a plurality of data source for structuring a multi-tier multi-party enabled data network 114 .
In an exemplary embodiment, the architecture 100 B may include a plurality of functional means configured for executing specific tasks related to one or more application developed by the codeless platform. The functional means include custom, curated and domain specific means including API (application programming interface) calls etc.
The processor 111 may be implemented as a chipset of chips that include separate and multiple analog and digital processors. The processor may provide coordination of the other components, such as controlling user interfaces, applications run by devices, and wireless communication by devices. The Processor may communicate with a user through control interface and display interface coupled to a display. The display may be, for example, a TFT LCD (Thin-Film-Transistor Liquid Crystal Display) or an OLED (Organic Light Emitting Diode) display, or other appropriate display technology. The display interface may comprise appropriate circuitry for driving the display to present graphical and other information to an entity/user. The control interface may receive commands from a user/demand planner and convert them for submission to the processor. In addition, an external interface may be provided in communication with processor, so as to enable near area communication of device with other devices. External interface may provide, for example, for wired communication in some implementations, or for wireless communication in other implementations, and multiple interfaces may also be used.
In a related embodiment, the LLM architecture 100 B includes a custom fine-tuned agent configured for selecting and changing a required set of means to execute a user specific task. This agent is supported by a finetuned LLM calibrated for tool selections and function execution tasks. The system includes a set of API executors of all major API's form part of the toolbox. Further, the tools also include databases and data source connector tools. The tool includes large language model (LLM) with access to a bundle of tools to achieve pre-defined objectives. These agents are driven by prompt(s) configured to enable process orchestration and tool selection.
In an embodiment, the LLM architecture 110 B includes a storage layer 115 configured to keep track of all the required data or information generated during data processing. This component of the LLM architecture 110 B is configured for storing information such as memory objects, the selected tools, the state of execution and the error messages among others.
In an exemplary embodiment, the memory or storage layer may be a volatile, a non-volatile memory or memory may also be another form of computer-readable medium, such as a magnetic or optical disk. The memory store may also include storage device capable of providing mass storage. In one implementation, the storage device may be or contain a computer-readable medium, such as a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid-state memory device or an array of devices, including devices in a storage area network or other configurations.
Referring to FIG. 2 , a flow diagram 200 of a large language model (LLM) based data processing method is provided in accordance with an embodiment of the invention. The method includes the step 201 generating by a processing device, a graphical user interface (GUI) having a conversational assistant configured for receiving at least one input from a user. The method includes step 202 of determining an intent of the user based on one or more data objects identified from the received input wherein the conversation assistant is configured to generate one or more query in response to the received input until the intent of the user is identified by an intent analyzer. The data processing method also includes the step 203 of triggering one or more LLM (large language model) agent for executing at least one task associated with the identified intent of the user wherein a process orchestrator invokes
CLAIMS
Claims ( 45 )
1 . A large language model-based data processing method for one or more applications developed by a codeless platform, the method comprising:
generating by a processing device, a graphical user interface (GUI) having a conversational assistant configured for receiving at least one input from a user; determining an intent of the user based on one or more data objects identified from the received input wherein the conversation assistant is configured to generate one or more query in response to the received input until the intent of the user is identified by an intent analyzer; triggering one or more LLM (large language model) agent for executing at least one task associated with the identified intent of the user wherein a process orchestrator invokes one or more tools identified by the LLM agent for executing the at least one task; and generating on the GUI, one or more graphical elements depicting one or more actionable data points associated with the at least one executed task.
2 . The method of claim 1 , wherein the intent analyzer is a bot configured to parse the intent of the user based on the identified data objects and mapping the intent with the LLM agent, wherein one or more one data scripts are identified based on the parsed intent to trigger the at least one task.
3 . The method of claim 2 , further comprises a bot builder configured to process one or more historical data for generating and storing, training artifacts and flow artifacts in an intent database wherein the intent analyzer processes the received input based on one or more intent data models to identify the intent.
4 . The method of claim 3 , wherein the intent analyzer is configured to analyze the intent from the at least one received input by:
converting the received input into numerical representation through embeddings; creating embeddings, one or more clusters during training and receiving sample prompts from users wherein each cluster represents a different intent; and identifying cluster nearest to an embedding representation of the received input to determine the intent, wherein a generative AI based reasoning model enables mapping of the received input to the intent in case the embedding representation is equally close to different clusters.
5 . The method of claim 4 , wherein parsing intent of the user includes predicting one or more procurement scenarios intended to be executed by the user as the at least one task, the bot identifies one or more nodes of a data network linked to the at least one task for parsing the intent.
6 . The method of claim 5 , further comprises:
processing by an AI engine coupled to a processor, a plurality of historical procurement and user activity data from a data lake based on one or more procurement data models to generate code for a recommended strategy to execute the at least one task through prediction analysis.
7 . The method of claim 6 , further comprises:
injecting by an intelligent bot, aggregated user activity data and procurement data patterns related to one or more procurement categories into the recommended strategy; identifying one or more suppliers for executing the recommended strategy; and encapsulating one or more recommended supplier awarding scenario on the GUI for selection.
8 . The method of claim 7 , wherein the generative AI model is configured to interact through the conversational assistant to accurately guide the user towards the intent.
9 . The method of claim 7 , further comprises the steps of breaking down complex procurement objectives received as the input into one or more actionable tasks through semantic analysis wherein the one or more data models trained on procurement datasets enable analysis of real-time procurement data and trends to recommend strategy including corrections or adjustments to procurement strategy.
10 . The method of claim 9 , wherein the one or more procurement scenarios include spend analysis, sourcing, supplier management, opportunity identification, contract management, and negotiation as part of procurement operations.
11 . The method of claim 10 , wherein spend analysis includes:
generating summary of spend across various parameters such as category, region, entity operation unit, supplier, payment terms, and diverse categories; detecting anomalies in spend within specific areas; identifying opportunities for cost avoidance in travel spend; and sharing metrics like Supplier diversity spend, Contract Compliance, Inventory Turnover, and Spend by Supplier Performance.
12 . The method of claim 11 , wherein the conversational assistance recommends potential areas and anomalies to user for exploring thereby not only providing on-demand insights but also proactively guiding users toward critical areas that require attention and deeper analysis through Generative AI.
13 . The method of claim 10 , wherein opportunity identification through generative AI and LLM based processing includes identifying vendor consolidation opportunities by category, identifying, and recommending payment term normalization opportunities, identifying, and proposing payment schedule for specific supplier, and identifying request and order consolidation opportunities.
14 . The method of claim 4 , wherein parsing intent of the user includes predicting one or more supply chain scenarios intended to be executed by the user as the at least one task, the bot identifies one or more nodes of a data network and associated data elements in the codeless platform linked to the at least one task for parsing the intent.
15 . The method of claim 14 , further comprises:
processing by an AI engine coupled to a processor, a plurality of historical supply chain data from a data lake based on one or more supply chain data models to generate code for a recommended strategy to execute the at least one task through prediction analysis.
16 . The method of claim 15 , further comprises:
injecting by an intelligent bot, aggregated supply chain data patterns related to the one or more data objects into the recommended strategy; identifying one or more entities for executing the recommended strategy; and encapsulating the one or more supply chain scenarios on the GUI for selection.
17 . The method of claim 16 , wherein the one or more supply chain scenarios include demand sensing, forward-reverse logistics, shipment tracking, as part of supply chain operations.
18 . The method of claim 14 , further comprises:
execution of operational function by a user based on user profile wherein the users have abstraction-based access control to one or more documents of a data network for executing the function.
19 . The method of claim 18 , further comprises:
modelling a network by a network builder configured to associate different relationship between organizations based on the user profile including buyer, supplier, shipper, receiver, carrier, payer, or payee to ensure validations of transaction and synchronization.
20 . The method of claim 19 , further comprising a multi-tier multi-party supply chain function execution through multi-party enablement in the one or more documents of the data network.
21 . The method of claim 4 , wherein parsing intent of the user includes predicting one or more application integration scenarios intended to be executed by the user as the at least one task, the bot identifies one or more nodes of a data network linked to the at least one task for parsing the intent.
22 . The method of claim 21 , further comprises:
processing by an AI engine coupled to a processor, a plurality of historical application integration data from a data lake based on one or more integration data models to generate code to execute the at least one task through prediction analysis.
23 . The method of claim 22 , further comprises:
identifying one or more entities, one or more application integration parameters, and the one or more integration data models from the data object for executing the at least one task of integration the one or more applications.
24 . The method of claim 23 , further comprises:
identifying source and target for executing the at least one task by automapping, wherein the automapping includes:
loading source and target files of syntax based structured data and extracting source path from a historical structured data database, and
tokenizing source path and fetching matching target paths from Inverted Index supported historical database for automapping source and target.
25 . The method of claim 24 , wherein matching includes:
converting each object of source to vector by word embedding; computing dot products and magnitude of the vectors to determine similarity, and determining similarity score for each object of target.
26 . The method of claim 25 , wherein
in response to determination of the application for integrations, generating one or more integration workflows by an intelligent bot; identifying by the bot, one or more configuration parameters for integration, and injecting by the bot, the configuration parameters into the one or more integration workflows for creating and deploying integration of the applications.
27 . The method of claim 4 , wherein parsing intent of the user includes predicting one or more application development or application restructuring scenarios intended to be executed by the user as the at least one task, the bot identifies one or more nodes of a data network linked to the at least one task for parsing the intent.
28 . The method of claim 27 , further comprises:
determining, by a processor, a requirement to restructure the one or more applications developed by a codeless platform as the at least one task; identifying by the one or more tools, one or more logical flow blocks to be invoked by the processor for creating one or more SCM application operation logical fragments configured to restructure the one or more applications; triggering a syntax data library by the processor, to enable the one or more tools to load one or more data library components on an extension tool interface for structuring the one or more logical flow blocks to create the one or more SCM application operation logical fragments; and restructuring the one or more applications by the one or more SCM application operation logical fragments to enable execution of at least one SCM application operation.
29 . The method of claim 28 , further comprises identifying by the processor, a plurality of configurable components of a layered codeless platform architecture based on the one or more LLM agent for restructuring one or more SCM applications to execute the SCM application operation, wherein the processor is coupled to an AI engine.
30 . The method of claim 29 , wherein the conversational assistant is configured to modify domain models, user interfaces, update code associated with new data elements, validate compatibility of the new data elements and recommend alternatives.
31 . The method of claim 27 , further comprises:
determining by a processor, a requirement to create one or more applications as the at least one task, wherein the one or more application is developed by a codeless platform; and identifying by one or more tools, a plurality of configurable components invoked by the processor to be structured on a user interface for creating the one or more application; wherein the plurality of configurable components interact through an application process orchestrator for executing the at least one task.
32 . The method of claim 31 , wherein the conversation assistant is configured to:
recommend one or more templates or components for customization of the one or more application; define data structures, relationships and rules, data models and validation rules; in response to a request for creating a user interface, recommend one or more interface components and generate a corresponding code for execution, and recommend data patterns and explain behavior and effects of different orchestrations.
33 . The method of claim 32 , wherein the one or more LLM agent is configured to be trained in a distributed structure with artificial intelligence controllers wherein different parts of the one or more LLM agent are distributed across a plurality of Graphics processing units (GPU) for parallel training of the LLM agent.
34 . The method of claim 32 , wherein the conversations assistant is configured to enable execution of data operations, such as denormalization, aggregation, filtering, sorting, and grouping, creation of custom AI models, such as regression, classification, clustering, and anomaly detection, and generation of insights and predictions from the data.
35 . The method of claim 1 , wherein the conversation assistant is configured to receive a text, image or voice input wherein the image or voice input is converted to text by one or more processors for enabling the LLM agent to identify the intent of the user and the at least one task to be executed.
36 . A large language model-based data processing system for one or more applications developed by a codeless platform, the method comprising:
one or more processors; and one or more memory devices including instructions that are executable by the one or more processor for causing the processor to
generate a graphical user interface (GUI) having a conversational assistant configured for receiving at least one input from a user;
determine an intent of the user based on one or more data objects identified from the received input wherein the conversation assistant is configured to generate one or more query in response to the received input until the intent of the user is identified by an intent analyzer;
trigger one or more LLM (large language model) agent for executing at least one task associated with the identified intent of the user wherein a process orchestrator invokes one or more tools identified by the LLM agent for executing the at least one task; and
generate on the GUI, one or more graphical elements depicting one or more actionable data points associated with the at least one executed task.
37 . The system of claim 36 , wherein the intent analyzer is a bot configured to parse the intent of the user based on the identified data objects and mapping the intent with the LLM agent, wherein one or more one data scripts are identified based on the parsed intent to trigger the at least one task.
38 . The system of claim 37 , further comprises a bot builder configured to process one or more historical data for generating and storing, training artifacts and flow artifacts in an intent database wherein the intent analyzer processes the received input based on one or more intent data models to identify the intent.
39 . The system of claim 38 , wherein the intent analyzer is configured to analyze the intent from the at least one received input by:
converting the received input into numerical representation through embeddings; creating embeddings, one or more clusters during training and receiving sample prompts from users wherein each cluster represents a different intent; and identifying cluster nearest to an embedding representation of the received input to determine the intent, wherein a generative AI based reasoning model enables mapping of the received input to the intent in case the embedding representation is equally close to different clusters.
40 . The system of claim 39 , wherein parsing intent of the user includes predicting one or more procurement scenarios intended to be executed by the user as the at least one task, the bot identifies one or more nodes of a data network linked to the at least one task for parsing the intent.
41 . The system of claim 39 , wherein the codeless platform includes:
a plurality of configurable components; a customization layer; an application layer; a shared framework layer; a foundation layer; a data layer; and an application orchestrator; wherein the at least one processor is configured to cause the plurality of configurable components to interact with each other in a layered architecture to:
customize the one or more application based on at least one operation to be executed using the customization layer;
organize at least one application service of the one or more application by causing the application layer to interact with the customization layer through one or more configurable components of the plurality of configurable components, wherein the application layer is configured to organize the at least one application service of the one or more application;
fetch shared data objects to enable execution of the at least one application service by causing the shared framework layer to communicate with the application layer through one or more configurable components of the plurality of configurable components, wherein the shared framework layer is configured to fetch the shared data objects to enable execution of the at least one application service, wherein fetching of the shared data objects is enabled via the foundation layer communicating with the shared framework layer, wherein the foundation layer is configured for infrastructure development through the one or more configurable components of the plurality of configurable components;
manage database native queries mapped to that at least one operation using a data layer to communicate with the foundation layer through one or more configurable components of the plurality of configurable components, wherein the data layer is configured to manage database native queries mapped to the at least one operation; and
execute the at least one operation and develop the one or more application using the application orchestrator to enable interaction of the plurality of configurable components in the layered architecture.
42 . The system of claim 41 , further comprises:
a data network configured for storing and processing of one or more dataset of the one or more application developed by the codeless platform, wherein a data network server is configured to receive the one or more dataset from a plurality of data source for structuring a multi-tier multi-party enabled data network; one or more data element nodes configured to create one or more sub-network through a graphical data structure wherein one or more data elements are extracted from the one or more dataset for relationship analysis to identify the one or more data elements to be ingested as one or more data element node of the data network; and one or more data connectors of the graphical data structure configured for connecting the one or more data element node to form the data network.
43 . The system of claim 42 , wherein the user interface includes an input component configured to receive the input, wherein the input component is a chatbot configured to receive a text, image or voice input wherein the image or voice input is converted to text by one or more processors for enabling the LLM agent to identify the intent of the user and the at least one task to be executed.
44 . The system of claim 43 , wherein different parts of the LLM agent are distributed across a plurality of GPU (Graphics processing Units) for parallel training including data parallelism, sequence parallelism, pipeline parallelism and tensor parallelism.
45 . A computer program product comprising a non-transitory computer readable storage medium that causes a processor to:
generate a graphical user interface (GUI) having a conversational assistant configured for receiving at least one input from a user; determine an intent of the user based on one or more data objects identified from the received input wherein the conversation assistant is configured to generate one or more query in response to the received input until the intent of the user is identified by an intent analyzer; trigger one or more LLM (large language model) agent for executing at least one task associated with the identified intent of the user wherein a process orchestrator invokes one or more tools identified by the LLM agent for executing the at least one task; and generate on the GUI, one or more graphical elements depicting one or more actionable data points associated with the at least one executed task.
US18/585,728
2024-02-23
2024-02-23
Large language model (llm) for enterprise applications developed by codeless platform
Pending
US20250272652A1
( en )
Priority Applications (1)
Application Number
Priority Date
Filing Date
Title
US18/585,728
US20250272652A1
( en )
2024-02-23
2024-02-23
Large language model (llm) for enterprise applications developed by codeless platform
Applications Claiming Priority (1)
Application Number
Priority Date
Filing Date
Title
US18/585,728
US20250272652A1
( en )
2024-02-23
2024-02-23
Large language model (llm) for enterprise applications developed by codeless platform
Publications (1)
Publication Number
Publication Date
US20250272652A1
true
US20250272652A1 ( en )
2025-08-28
Family
ID=96812061
Family Applications (1)
Application Number
Title
Priority Date
Filing Date
US18/585,728
Pending
US20250272652A1
( en )
2024-02-23
2024-02-23
Large language model (llm) for enterprise applications developed by codeless platform
Country Status (1)
Country
Link
US
( 1 )
US20250272652A1
( en )
Cited By (13)
* Cited by examiner, â Cited by third party
Publication number
Priority date
Publication date
Assignee
Title
US20250298970A1
( en )
*
2024-03-25
2025-09-25
Amazon Technologies, Inc.
Enhanced plugin selection in conversational artificial intelligence systems through contextual large language model prompts
US20250307744A1
( en )
*
2024-03-27
2025-10-02
Sap Se
Intelligent explanation of configuration keys
US20250335837A1
( en )
*
2024-04-25
2025-10-30
Dell Products L.P.
Method, device, and computer program product for development using agents
CN120875051A
( en )
*
2025-09-23
2025-10-31
ä¸ç§»(èå·)软件ææ¯æéå ¬å¸
Parameter optimization method, device, electronic equipment, storage medium and computer program product
US20250363297A1
( en )
*
2024-05-21
2025-11-27
Sap Se
Fine-tuned large language models for capability controller
US12489633B1
( en )
*
2024-05-31
2025-12-02
Onesource Solutions International, Inc.
System for federated compliance-token inheritance, digital artifact registry, and monetization in regulated environments using large language models
US20250390571A1
( en )
*
2024-06-21
2025-12-25
Bank Of America Corporation
System and Method for Securing Software Application Based Microservices Architectures
CN121326331A
( en )
*
2025-12-15
2026-01-13
å¦é¨å¸æ°æ°æ®æå¡è¡ä»½æéå ¬å¸
Code page generation method and system based on AI and history page
CN121387273A
( en )
*
2025-12-24
2026-01-23
å京德å¡ç²¾è¦ä¿¡æ¯ææ¯æéå ¬å¸
Application generation method, system, device, storage medium and program product based on low-code platform
CN121478248A
( en )
*
2026-01-08
2026-02-06
æµæ±å¤§å¦
Smart contract generation and interpretable negotiation system, method, medium and terminal
CN121614493A
( en )
*
2026-02-02
2026-03-06
æå·éç®ç§ææéå ¬å¸
Method and system for realizing low-code platform form combined query condition
US20260119554A1
( en )
*
2024-10-24
2026-04-30
Nomura Research Institute, Ltd.
Security countermeasure support system
US20260127535A1
( en )
*
2024-11-01
2026-05-07
Hitachi, Ltd.
Systems and methods for accelerating process transformation opportunity identification using generative ai
2024
2024-02-23
US
US18/585,728
patent/US20250272652A1/en
active
Pending
Cited By (15)
* Cited by examiner, â Cited by third party
Publication number
Priority date
Publication date
Assignee
Title
US20250298970A1
( en )
*
2024-03-25
2025-09-25
Amazon Technologies, Inc.
Enhanced plugin selection in conversational artificial intelligence systems through contextual large language model prompts
US20250307744A1
( en )
*
2024-03-27
2025-10-02
Sap Se
Intelligent explanation of configuration keys
US20250335837A1
( en )
*
2024-04-25
2025-10-30
Dell Products L.P.
Method, device, and computer program product for development using agents
US12591736B2
( en )
*
2024-05-21
2026-03-31
Sap Se
Fine-tuned large language models for capability controller
US20250363297A1
( en )
*
2024-05-21
2025-11-27
Sap Se
Fine-tuned large language models for capability controller
US12489633B1
( en )
*
2024-05-31
2025-12-02
Onesource Solutions International, Inc.
System for federated compliance-token inheritance, digital artifact registry, and monetization in regulated environments using large language models
US20250390571A1
( en )
*
2024-06-21
2025-12-25
Bank Of America Corporation
System and Method for Securing Software Application Based Microservices Architectures
US12524525B1
( en )
*
2024-06-21
2026-01-13
Bank Of America Corporation
System and method for securing software application based microservices architectures
US20260119554A1
( en )
*
2024-10-24
2026-04-30
Nomura Research Institute, Ltd.
Security countermeasure support system
US20260127535A1
( en )
*
2024-11-01
2026-05-07
Hitachi, Ltd.
Systems and methods for accelerating process transformation opportunity identification using generative ai
CN120875051A
( en )
*
2025-09-23
2025-10-31
ä¸ç§»(èå·)软件ææ¯æéå ¬å¸
Parameter optimization method, device, electronic equipment, storage medium and computer program product
CN121326331A
( en )
*
2025-12-15
2026-01-13
å¦é¨å¸æ°æ°æ®æå¡è¡ä»½æéå ¬å¸
Code page generation method and system based on AI and history page
CN121387273A
( en )
*
2025-12-24
2026-01-23
å京德å¡ç²¾è¦ä¿¡æ¯ææ¯æéå ¬å¸
Application generation method, system, device, storage medium and program product based on low-code platform
CN121478248A
( en )
*
2026-01-08
2026-02-06
æµæ±å¤§å¦
Smart contract generation and interpretable negotiation system, method, medium and terminal
CN121614493A
( en )
*
2026-02-02
2026-03-06
æå·éç®ç§ææéå ¬å¸
Method and system for realizing low-code platform form combined query condition
Similar Documents
Publication
Publication Date
Title
Chakraborti et al.
2020
From Robotic Process Automation to Intelligent Process Automation: âEmerging Trendsâ
Ramos et al.
2020
Systems evaluation methodology to attend the digital projects requirements for industry 4.0
Alam et al.
2024
From automation to augmentation: Redefining engineering design and manufacturing in the age of NextGen-AI
US20250217753A1
( en )
2025-07-03
Large language model (llm) based data processing in procurement and supply chain applications developed by codeless platform
Casciani et al.
2024
Conversational systems for AI-augmented business process management
Suri
2022
Functional automation and digital transformation
Indurthy
2024
The surge in AI-powered data analytics revolutionizing business intelligence
Zhou et al.
2025
The use of Generative Artificial Intelligence (GenAI) in operations research: Review and future research agenda
Truss et al.
2025
Human-centered ai product prototyping with no-code automl: conceptual framework, potentials and limitations
Pamisetty
2025
Agentic Intelligence and Cloud-Powered Supply Chains: Transforming Wholesale, Banking, and Insurance with Big Data and Artificial Intelligence
Chinnaraju
2025
AI-driven strategic decision-making on innovation: Scalable, ethical approaches and ai agents for startups
Melnyk et al.
2024
Prospects of business process management based on chatbots
Pulapaka et al.
2024
Empowering the public sector with generative AI
Dai et al.
2025
Artificial Intelligence and Operations: A Foundational Framework of Emerging Research and Practice
Kumar et al.
2024
Disruptive innovations using tech-business analytics
Jager
2023
Getting started with enterprise architecture
Huang
2025
Ai agents and business workflow
Lazzeri et al.
2024
Machine learning governance for managers
Weaver-Lambert
2024
The AI Value Playbook: How to make AI work in the real world
Dockara et al.
2024
Strategies for Gen AI enterprise adoption
Devaki et al.
2023
The Existing IT Functions and Robotic Process Automation
Zattera et al.
2023
Ultimate ChatGPT handbook for enterprises: Transform the enterprise landscape by leveraging ai capabilities, prompt engineering, GPT solution-cycles of ChatGPT with python and java
Koumpan et al.
2023
Procurement of the future: Investing today in the technologies of tomorrow
US20260086798A1
( en )
2026-03-26
Multi agent architecture, system and method for data processing in enterprise applications developed by codeless platform
US20260087447A1
( en )
2026-03-26
Artificial intelligence (ai) agents based data processing in enterprise applications developed by codeless platform
Legal Events
Date
Code
Title
Description
2024-06-14
AS
Assignment
Owner name : NB VENTURES, INC., DBA GEP, NEW JERSEY
Free format text : ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:MAKHIJA, SUBHASH;SETHI, SARATENDU;PATEL, POOJA;AND OTHERS;SIGNING DATES FROM 20240506 TO 20240605;REEL/FRAME:067728/0084
2024-06-14
STPP
Information on status: patent application and granting procedure in general
Free format text : DOCKETED NEW CASE - READY FOR EXAMINATION