Description:FIELD OF INVENTION
The present invention relates to the field of intelligent knowledge management systems. Specifically, it provides systems and methods for capturing, transforming, and utilizing contextual organizational data to facilitate seamless model updates while retaining and transferring local knowledge in a customer environment.

BACKGROUND
The increasing demand for adaptive and efficient knowledge management systems has underscored the need for solutions that can capture, retain, and transfer contextual data seamlessly. Knowledge management is a critical function in modern organizations, involving the collection, organization, and dissemination of information to improve operational efficiency and decision-making. Effective knowledge transition systems are essential to ensure that valuable organizational knowledge is not lost during processes such as employee turnover, project handovers, or technological upgrades.
Traditionally, knowledge management systems have relied on static databases or relational models to store and process organizational data. These conventional approaches often lack the dynamic capabilities required to manage evolving datasets effectively. They fail to encapsulate complex interdependencies between data points and struggle to adapt to incremental updates without significant downtime or reprocessing. Moreover, traditional methods typically result in data silos, limiting cross-functional insights and efficient knowledge transfer. These limitations hinder the ability of organizations to maintain operational efficiency and adapt to new data paradigms.
Another major drawback of traditional systems is their inability to integrate diverse data sources effectively. Heterogeneous data formats and structures pose significant challenges for creating a unified knowledge base. These systems often require extensive manual intervention to normalize and reconcile data, which is time-consuming and prone to errors. Furthermore, the static nature of traditional models makes it difficult to incorporate real-time updates, leading to outdated or incomplete knowledge bases. As a result, organizations face difficulties in leveraging their data assets for strategic decision-making and innovation.
PRIOR ART
WO2019220128A1 provides methods and apparatus for generating a graph neural network (GNN) model based on an entity-entity graph. This graph comprises multiple entity nodes connected by relationship edges, each assigned attention weights that reflect their relevance. The invention focuses on optimizing GNN model weights through a loss function, enabling efficient filtering of the graph based on these weights. While it effectively manages entity relationships within a graph structure, it does not address the broader requirements of incremental model updates and the creation of compact, queryable knowledge packets for fine-tuning.
US20160019272A1 extends methods, systems, and computer program products for managing data ingestion units. It employs a pluggable architecture channel service for raw data ingestion unit and conversion into a common format, such as key-value pairs. The system’s EAV storage functionality allows consumers to define multiple entities across datasets. Although it ensures data ingestion unit without loss, it lacks provisions for transforming ingested data into actionable knowledge structures, such as knowledge packets, or for handling model updates with minimal disruption.
WO2020236255A1 discloses a method for incremental learning using a grow-and-prune paradigm in neural networks. It introduces a training mechanism where connections are iteratively grown and pruned based on their gradients and magnitudes to achieve compact and accurate neural networks. While this method supports incremental learning, it is limited to neural network optimization and does not encompass knowledge transition systems that facilitate organizational model updates through localized data transformations.
WO2016118257A1 describes a method for compressing machine learning networks by replacing layers with compressed alternatives, followed by fine-tuning. This approach focuses on reducing the complexity of machine learning models and enhancing their efficiency. However, it does not extend to the integration of organizational context or the use of knowledge packets for transferring localized data during model updates.
Thus, the need to introduce a comprehensive system of the present invention for capturing, transforming, and transferring contextual knowledge is evident so as to address the gaps in the existing technologies.

OBJECTS OF THE INVENTION:
The primary object of the present invention is to provide a system and method for capturing, transforming, and utilizing contextual organizational data.
Yet another object of the present invention is to provide a seamless knowledge management and transition system that ensures the retention and transfer of organizational context during model updates.
Yet another object of the present invention is to enable the efficient transformation of diverse organizational data sources into a unified knowledge web using graph neural networks (GNNs).
Yet another object of the present invention is to facilitate the creation of self-contained and size-efficient knowledge packets that encapsulate relevant contextual information for model fine-tuning.
Yet another object of the present invention is to ensure minimal operational disruption by enabling incremental model updates using compact and query-able knowledge structures.
Further, the object of the present invention is to provide a dynamic system that adapts to evolving datasets and integrates real-time updates to maintain an up-to-date and contextually relevant knowledge base.

SUMMARY
Before the present invention is described, it is to be understood that present invention is not limited to particular methodologies and materials described, as these may vary as per the person skilled in the art. It is also to be understood that the terminology used in the description is for the purpose of describing the particular embodiments only, and is not intended to limit the scope of the present invention.
The present invention discloses a system and method for capturing, transforming, and utilizing contextual organizational data to facilitate seamless updates to operational models while retaining and transferring localized knowledge. The system addresses the limitations of traditional knowledge management systems, such as static architectures and data silos, by employing advanced techniques like graph neural networks (GNNs) to process and transform heterogeneous data into a unified knowledge web. This knowledge web captures the relationships and dependencies among data entities, ensuring contextual accuracy. The system generates compact, self-contained knowledge packets that encapsulate essential contextual data and metadata, optimized for efficient storage, transfer, and localized model fine-tuning. Through incremental update mechanisms, the system integrates new data in real-time, ensuring that models remain up-to-date without requiring full retraining. The method involves gathering and normalizing data from organizational sources, creating a graph-based knowledge web, extracting subgraphs relevant to specific tasks, and using these knowledge packets for model fine-tuning and transition. The system ensures minimal disruption during model updates, allowing for smooth deployment and synchronization of knowledge in real-time. Validation processes confirm the models' contextual accuracy and operational consistency. This scalable and adaptable system enhances organizational efficiency by enabling continuous model improvement while preserving vital contextual knowledge.

BRIEF DESCRIPTION OF DRAWINGS
A complete understanding of the present invention may be made by reference to the following detailed description which is to be taken in conjugation with the accompanying drawing. The accompanying drawing, which is incorporated into and constitutes a part of the specification, illustrates one or more embodiments of the present invention and, together with the detailed description, it serves to explain the principles and implementations of the invention.
FIG. 1 illustrates an overview of a system and method of context and knowledge transition data
FIG. 2 illustrates the procedures for knowledge packet creation and model fine-tuning workflow
FIG. 3 illustrates deployment, synchronization, and validation workflows.

DETAILED DESCRIPTION OF INVENTION:
Before the present invention is described, it is to be understood that this invention is not limited to methodologies described, as these may vary as per the person skilled in the art. It is also to be understood that the terminology used in the description is for the purpose of describing the particular embodiments only and is not intended to limit the scope of the present invention. Throughout this specification, the word “comprise”, or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps. The use of the expression “at least” or “at least one” suggests the use of one or more elements or ingredients or quantities, as the use may be in the embodiment of the invention to achieve one or more of the desired objects or results. Various embodiments of the present invention are described below. It is, however, noted that the present invention is not limited to these embodiments, but rather the intention is that modifications that are apparent are also included.
To understand the invention clearly, the various components of the system are referred as
below:
Sr. No. Component
100 System
120 data ingestion unit
130 Knowledge Web Creation
140 Knowledge Packet Creation
150 Fine-Tuning operation model
160 Incremental Updates
170 Model Transition Manager
180 New Model Deployment
190 Local Knowledge Transfer
200 Localized Fine-Tuning
210 Validation and Testing

The present invention discloses a system (100) and method (300) for capturing, transforming, and utilizing contextual organizational data. The system comprises an array of components and processes such as data ingestion unit (120), knowledge web creation (130), knowledge packet creation (140), fine-tuning operation model (150), incremental updates (160), model transition manager (170), new model deployment (180), local knowledge transfer (190), localized fine-tuning (200), and validation and testing (210); designed to capture, structure, and transfer organizational data with precision and contextual accuracy.
According to a preferred embodiment, the system (100) facilitates the ingestion of data using data ingestion unit (120) from various organizational sources including, but not limited to, repositories, ticketing systems, and documentation archives, where the data is normalized into a standardized schema for further processing; utilizes advanced methodologies to construct a knowledge web (130) that encapsulates the relationships and dependencies between data entities, employing graph-based representations to ensure comprehensive contextual mapping; and generates knowledge packets (140) which are compact, self-contained units that encapsulate relevant contextual data and metadata. These packets are optimized for efficient storage and seamless transfer, enabling their application in fine-tuning (150) operational models within specific environments.
According to another embodiment of the invention, the system (100) employs a stepwise method for incremental model updates (160), wherein new data is integrated to maintain contextual relevance without requiring extensive retraining. Real-time synchronization of the knowledge web ensures regular updates of relationships and contextual data. Validation and testing (210) procedures are incorporated to ensure compliance with predefined performance benchmarks, preserving contextual integrity and operational consistency. The architecture of the system is scalable, adaptable to diverse organizational scenarios while maintaining essential contextual knowledge, thereby enhancing operational efficiency and organizational intelligence.
According to a preferred embodiment, the present invention provides a system for the seamless transition and utilization of contextual knowledge within organizational environments; wherein the data ingestion unit (120) mechanisms extract information from diverse organizational sources such as repositories, ticketing systems, and documentation archives. The data is normalized into a uniform schema to ensure compatibility and standardization whereby it lays the groundwork for creating a structured representation that captures the essence of organizational context with high precision and accuracy.
According to another embodiment, the invention employs a well-structured workflow to construct a knowledge web (130) that serves as a structured representation of the relationships and dependencies among organizational data entities. Each data entity is treated as a node, with its relationships to other entities defined through edges. These graph-based methodologies are designed to capture complex interactions and provide a holistic view of the contextual environment, ensuring that every critical dependency is adequately represented. The knowledge web (130) forms the backbone for subsequent data transformation and utilization stages.
According to yet another embodiment, the system (100) generates compact, self-contained knowledge packets (140) from the knowledge web (130); such that these packets encapsulate the most relevant contextual data and associated metadata, optimized for efficient storage, retrieval, and transfer. Each knowledge packet (140) is designed to be task-specific, enabling its use in fine-tuning operational models (150) within localized environments. The packetized design ensures that the contextual integrity is preserved while minimizing the data footprint, facilitating faster processing and reduced storage overhead.
According to yet another embodiment, the system (100) incorporates incremental update (160) mechanisms to adapt the operational models continuously without requiring complete retraining. These mechanisms utilize knowledge packets (140) to integrate new data dynamically, ensuring that the models remain up-to-date and contextually accurate. This approach reduces downtime and operational disruptions while enhancing the adaptability of the system to evolving organizational requirements.
According to yet another preferred embodiment, the system (100) facilitates model transition processes using the model transition manager (170) by leveraging the knowledge packets (140) to transfer critical contextual insights from one model iteration to another. This enables the smooth deployment of new models (180) while retaining local contextual knowledge, ensuring that the updated systems are immediately operational and effective in the target environment.
According to another embodiment, the system (100) includes real-time synchronization capabilities for updating the knowledge web (130). This ensures that the system remains contextually relevant by incorporating new data and relationships as they emerge. These updates are seamlessly integrated into the system (100) without disrupting ongoing operations, maintaining a continuously evolving knowledge base.
According to a further embodiment, the invention incorporates validation and testing (210) mechanisms to ensure compliance with performance benchmarks. The deployed models are rigorously tested to confirm their contextual accuracy and operational consistency, ensuring they meet predefined standards; thereby safeguarding the reliability and effectiveness of the system in dynamic and diverse organizational settings.
In another preferred embodiment of the invention, a method for capturing, transforming, and utilizing contextual organizational data is disclosed. The method comprises the following steps:
1. Establishing Connectors to Data Sources:
The process begins with the system (100) establishing connectors to diverse organizational data sources, such as repositories, ticketing systems, and documentation archives using the data ingestion unit (120) mechanism. The connectors retrieve raw data and normalize it into a standardized schema, ensuring compatibility and uniformity for subsequent stages of processing.
2. Creating the Knowledge Web (130):
The normalized data is processed using graph-based methodologies to construct a knowledge web (130), wherein each data entity is represented as a node and its relationships are captured through edges. Graph neural networks may be employed to calculate embeddings and edge weights, resulting in a comprehensive and contextually accurate representation.
3. Forming Knowledge Packets (140):
Subsequent to the construction of the knowledge web (130), specific subgraphs are extracted based on task-specific requirements such that the subgraphs are encapsulated into compact, self-contained knowledge packets (140) enriched with contextual metadata; and the packets are optimized for efficient storage, transfer, and utilization.
4. Fine-Tuning (150) Operational Models:
The knowledge packets (140) serve as input for fine-tuning operational models (150) within a localized environment; where the system utilizes incremental update mechanisms to adapt the models dynamically, thereby preserving contextual relevance and ensuring operational efficiency without necessitating full retraining.
5. Implementing Incremental Updates:
The system (100) continuously ingests new data (120) and integrates it into the knowledge packets (140) and fine-tuning operational models (150) such that the event-driven mechanisms enable real-time updates, maintaining the accuracy and contextual alignment of the models over time.
6. Facilitating Model Transition:
To ensure seamless deployment of updated models, the system leverages knowledge packets (140) to transfer critical contextual insights from previous iterations, ensuring that localized knowledge is retained, enabling a smooth transition without operational disruptions.
7. Synchronizing the Knowledge Web (130):
The system (100) periodically synchronizes the knowledge web (130) with new data and relationships, ensuring that it remains up-to-date and contextually relevant. This synchronization is performed dynamically and without interrupting ongoing operations.
8. Validating and Testing Updated Models:
The final step involves rigorous validation and testing (210) of the updated models against predefined performance benchmarks thereby confirming that the models meet required standards for contextual accuracy, operational reliability, and compliance, facilitating their deployment in diverse organizational scenarios.

While considerable emphasis has been placed herein on the specific elements of the preferred embodiment, it will be appreciated that many alterations can be made and that many modifications can be made in preferred embodiment without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the invention and not as a limitation.
, Claims:CLAIMS:
We claim,
1. A system and method for capturing, transforming, and utilizing contextual organizational data to facilitate seamless model updates while retaining and transferring local knowledge;
wherein the system comprises of data ingestion unit (120), knowledge web creation (130), knowledge packet creation (140), fine-tuning operation model (150), incremental updates (160), model transition manager (170), new model deployment (180), local knowledge transfer (190), localized fine-tuning (200), and validation and testing (210);
characterized in that:
the data ingestion unit (120) facilitates the ingestion of data from various organizational sources and normalize the data into a standardized schema;
the knowledge web creation (130) serves as a structured representation of the relationships and dependencies among organizational data entities; and where the graph-based methodologies are designed to capture complex interactions and provide a holistic view of the contextual environment, ensuring that every critical dependency is adequately represented;
the knowledge packet generation (140) where the system generates compact, self-contained knowledge packets (140) that encapsulate the most relevant contextual data and associated metadata, optimized for efficient storage, retrieval, and transfer;
the fine- tuning operation models (150) use the task-specific knowledge packets (140) within localized environments, ensuring that the contextual integrity is preserved while minimizing the data footprint, facilitating faster processing and reduced storage overhead;
the incremental update (160) utilises knowledge packets (140) to integrate new data dynamically, to adapt the operational models continuously without requiring complete retraining thereby ensuring that the models remain up-to-date and contextually accurate;
the model transition manager (170) facilitates model transition processes by leveraging the knowledge packets (140) to transfer critical contextual insights from one model iteration to another, enabling the smooth deployment of new models (180) while retaining local contextual knowledge;
the validation and testing (210) mechanisms ensure compliance with performance benchmarks where the deployed models are rigorously tested to confirm their contextual accuracy and operational consistency, ensuring they meet the predefined standards.

2. The system as claimed in claim 1, wherein the data ingestion unit (120) establishes connectors to organizational data sources including, but not limited to repositories, ticket systems, and documentation systems to fetch and normalize data.

3. The system as claimed in claim 1, wherein the knowledge web (130) is created using graph neural networks (GNNs) to compute node embeddings and edge weights, capturing the relationships and context within the data; and the synchronization of the knowledge web (130) incorporates new events and relationships to maintain real-time accuracy and relevance of the data.

4. The system as claimed in claim 1, wherein the knowledge packets (140) are created by extracting subgraphs from the knowledge web (130) and compressing them into compact, size-efficient formats enriched with metadata.

5. The system as claimed in claim 1, wherein the fine-tuning (150) of models involves using the knowledge packets (140) to apply incremental updates (160) that improve performance metrics such as contextual accuracy.

6. The system as claimed in claim 1, wherein incremental updates (160) are performed by continuously ingesting new data, updating knowledge packet (140), and retraining models without operational disruptions.

7. The system as claimed in claim 1, wherein the model transition manager (170) involves deploying a newly trained model in a customer environment and transferring context-specific tuning data to ensure seamless integration.

8. The system as claimed in claim 1, wherein validation processes include testing (210) models against real-world scenarios to ensure compliance with predefined performance benchmarks.

9. The system as claimed in claim 1, wherein the compressed knowledge packets (140) are structured to retain detailed descriptions of data attributes and relationships, enabling accurate reconstruction and efficient querying.

10. The method as claimed in claim 1, wherein the system (100) employs a method (300) comprising the steps of:
- establishing connectors to data sources;
- fetching and normalizing data into a standard schema;
- providing metadata for processing by data ingestion unit (120);
- extracting subgraphs relevant to specific tasks or events;
- compressing the subgraphs into packets with contextual data;
- querying and optimizing packet size for storage of metadata;
- fine-tuning the self-sufficient knowledge packets by knowledge web creation (130);
- representing each entity as a node in the graph;
- defining edges to capture relationships;
- using GNNs to calculate embeddings, edge weights and Graph databases;
- utilizing adjacency matrices for computational tasks;
- encapsulating organizational context by a knowledge packet creation (140);
- using knowledge packets (140) as input for model fine-tuning;
- applying incremental updates triggered by data events;
- validating model performance metrics;
- incrementing SGD and cross-validating;
- providing optimized models tailored to local data by fine-tuning operation model;
- continuing ingest new data;
- updating knowledge packets and retrain models incrementally;
- maintaining event logs for tracking updates;
- ensuring models remain up-to-date with minimal retraining by incremental updates;
- deploying a new model in the customer environment;
- transferring tuning data from the old model using packets;
- performing localized fine-tuning for the new model;
- achieving seamless transition with retained contextual knowledge by model transition manager;
- updating regularly the knowledge web with new events and relationships;
- synchronizing packets for real-time tuning;
- evolving the knowledge base continuously by local knowledge transfer;
- testing the new model against real-world scenarios;
- ensuring compliance with performance benchmarks;
- validating models for deployment by validation and testing.

Dated this 26th day of December, 2024.