A platform customized for each persona within the distribution chain.

Abstract

We provide a customized platform for all personas in the distribution industry. [Solution] The system is configured to use a registration module 110 to collect initial user data through the registration process, a role identification engine 120 to identify the user's role, an interaction tracker 130 to monitor user interactions, and a preference aggregator 140 to execute commands to aggregate user preferences. The system can generate a personalized user interface, dynamically update content using a dynamic content generator, continuously improve personalization by employing AI techniques via a learning AI, provide personalized recommendations through a recommendation system, monitor the effectiveness of the process using a performance monitor, and collect and integrate user feedback through a feedback integrator.

Description

Technical Field

[0001] In conventional IT distribution platforms, users from various roles such as MSP administrators, vendors, resellers, end customers, etc. often face many challenges. These platforms usually adopt static or semi-static interfaces that do not adequately meet the specific needs and preferences of different user personas. This lack of customization leads to inefficiencies as it presents irrelevant information, tools, and functions that do not match the individual roles of users.

[0002] Previous systems relied heavily on manual configuration and general content delivery, resulting in a sub-optimal user experience. For example, MSP administrators may need to sift through a cluttered interface with tools configured for vendors or sales representatives, leading to wasted time and decreased productivity. Similarly, vendors may find it difficult to access specific data and tools necessary to manage their products and interact efficiently with resellers.

[0003] Furthermore, conventional platforms lack the ability to dynamically adapt to changes in user behavior and preferences. The static nature of these systems often requires significant manual effort and time for interface or content delivery updates, resulting in old information being presented to users. This can potentially hinder the decision-making process as users are unable to access the latest and relevant data.

[0004] Traditional platforms lack advanced AI and machine learning techniques, exacerbating these problems. Without the ability to learn from user interactions and adapt accordingly, existing systems cannot provide personalized recommendations and content that align with evolving user needs. As a result, users experience generic, static interfaces that fail to meet their individual requirements, leading to dissatisfaction and decreased engagement.

[0005] In summary, traditional IT distribution platforms suffer from problems due to static interfaces, a lack of role-specific customization, manual content updates, and a lack of dynamic learning and adaptation. These limitations result in inefficient workflows, outdated information, and a substandard user experience, ultimately negatively impacting the overall efficiency and productivity of users across different roles within the IT distribution network. [Overview of the project]

[0006] The embodiments described herein provide a platform customized for each individual persona in distribution, addressing the need for personalized user experiences in IT distribution networks. The platform begins by collecting initial user data through a registration process using a registration module, and by analyzing this data, the user's role is identified using a role identification engine. User interactions are monitored through an interaction tracker, and preferences are aggregated by a preference aggregator to generate detailed user personas. A single-pane-of-glass user interface (SPoG UI) is generated, providing an intuitive interface that is personalized for each user.

[0007] In some embodiments, the system includes a real-time data mesh (RTDM) for efficiently managing data workflows. This includes modules for ingesting data from various sources, cleaning and standardizing the data, transforming the data for real-time analysis, managing metadata, and optimizing data storage. Advanced data processing capabilities ensure high-quality, consistent, and readily available data for a personalized user experience.

[0008] In some embodiments, the platform utilizes an Advanced Analytics and Machine Learning (AAML) module as its core analytics engine. This module performs complex data analysis using advanced AI models, integrates workflows across various components, adapts processing strategies based on new data insights, and facilitates data integration with external platforms. This ensures adaptive data processing and analysis, improving the overall functionality and intelligence of the platform.

[0009] In some embodiments, the platform features a dynamic and customizable user interface. A dynamic user interface engine adapts the interface to the user's role and preferences, an interactive visualization toolkit provides various data visualization options, and a real-time collaborative framework supports collaborative tools and real-time data manipulation. Security and compliance are maintained by implementing advanced security features such as biometric access control and advanced encryption standards.

[0010] In some embodiments, the platform employs learning AI to continuously learn from user interactions and improve personalization. This component uses deep learning models to analyze interaction data and user feedback, enabling the system to dynamically adapt to changes in user behavior and preferences. Personalized recommendations are provided by a recommendation system, and the effectiveness of these processes is monitored by a performance monitor. User feedback is collected and integrated by a feedback integrator to ensure continuous refinement and conformity to user expectations.

[0011] In some non-exclusive examples, the platform includes a dynamic content generator that updates content based on user interaction using real-time data streams and data integration techniques. This ensures that the content displayed on the SPoG UI is always up-to-date and relevant. Learning AI enhances the system's adaptive capabilities using deep learning models such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks.

[0012] The claims relating to this system concern a server configured to collect initial user data, analyze the data to identify the user's role, monitor user interactions, aggregate preferences, generate personalized user interfaces, dynamically update content, employ AI technology for continuous learning, provide personalized recommendations, monitor process effectiveness, and execute commands to collect user feedback. Additional functions include data ingestion, cleaning, transformation, metadata management, storage optimization, complex data analysis, workflow integration, processing adaptation, and external data integration.

[0013] The claims relating to this method include steps for collecting user data, analyzing the data to identify roles, monitoring interactions, aggregating preferences, generating personalized interfaces, dynamically updating content, employing AI for continuous learning, providing recommendations, monitoring effectiveness, and collecting feedback. Additional steps include data ingestion, cleaning, transformation, metadata management, storage optimization, performing complex analyses, workflow integration, adaptation of processing strategies, and facilitating external data integration.

[0014] Claims relating to computer-readable media include instructions for collecting user data, analyzing data to identify roles, monitoring interactions, aggregating preferences, generating personalized interfaces, dynamically updating content, employing AI for continuous learning, providing recommendations, monitoring effectiveness, and collecting feedback. Additional instructions include data ingestion, cleaning, transformation, metadata management, storage optimization, complex data analysis, workflow integration, processing adaptation, and integration of external data. [Brief explanation of the drawing]

[0015] [Figure 1] This is a diagram of a system for persona identification according to several embodiments. [Figure 2] This is a diagram of a system for personalized UI / UX, based on several embodiments. [Figure 3] This is a diagram of a system for content customization, according to several embodiments. [Figure 4] This is a diagram of a system for AI learning and personalization according to several embodiments. [Figure 5] This is a diagram of a system for a customized dashboard, according to several embodiments. [Figure 6] This is a flowchart of a method for performing customized content delivery and interface personalization, according to several embodiments. [Modes for carrying out the invention]

[0016] This embodiment may be implemented in hardware, firmware, software, or any combination thereof. Alternatively, this embodiment may be implemented as instructions stored in a machine-readable medium, which can be read and executed by one or more processors. The machine-readable medium may include any mechanism for storing or transmitting information in a format readable by a machine (e.g., a computing device). For example, the machine-readable medium may include read-only memory (ROM), random-access memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, and others. Furthermore, firmware, software, routines, and instructions may be described herein as performing specific actions. However, such descriptions are merely for convenience, and it should be understood that such actions are actually the results obtained by a computing device, processor, controller, or other device executing the firmware, software, routines, instructions, etc.

[0017] The actions shown in the illustrative methods are not exhaustive, and it should be understood that other actions may similarly be performed before, after, or between any of the illustrated actions. In some embodiments of this disclosure, the actions may be performed in a different order and / or different order.

[0018] Figure 1 illustrates a system 100 for persona identification. System 100 can be configured to recognize a user's role and preferences based on initial registration data, login credentials, and ongoing interaction patterns. System 100 may include a registration module 110, a role identification engine 120, an interaction tracker 130, a preference aggregator 140, and a data processor 150.

[0019] The registration module 110 can be the first point of interaction with new users. It collects detailed user data through a comprehensive registration form, which may include fields such as name, contact information, organization, role, and specific preferences. The registration module 110 is integrated with an identity verification system to ensure the authenticity of user data. The registration module 110 works with external databases and directories to obtain additional user details that help form an accurate initial user profile.

[0020] The role identification engine 120 analyzes data collected during registration to determine the user's role within the distribution network. Using a set of predefined rules and machine learning algorithms, the role identification engine 120 can classify users into categories such as MSP administrators, sales representatives, vendors, resellers, and end customers. The role identification engine 120 can use natural language processing (NLP) to parse free-text input fields and extract relevant information that may indicate the user's role and responsibilities. The classification model is continuously refined by learning from new data and feedback.

[0021] The interaction tracker 130 monitors user activity and interactions within the platform. It logs actions such as navigation paths, time spent in different sections, frequency of feature use, and responses to notifications and alerts. The interaction tracker 130 can capture real-time interaction data using an event-driven architecture, which can then be stored in a centralized repository. Using composite event processing (CEP), the interaction tracker 130 can detect patterns and anomalies in user behavior, providing insights into user preferences and engagement levels.

[0022] The preference aggregator 140 compiles the user's preferences based on the data from the registration module 110 and the interaction tracker 130. The preference aggregator 140 aggregates this data to create a comprehensive preference profile for each user. The preference aggregator 140 can use techniques such as collaborative filtering and content-based filtering to predict the user's preferences and recommend relevant features, content, and actions. In addition, the preference aggregator 140 enables manual adjustment of preferences through a user-friendly interface, ensuring that the system aligns with the evolving needs of the user.

[0023] The data processor 150 integrates the data from all components to generate a detailed user persona. The data processor 150 can combine structured and unstructured data using data fusion techniques, ensuring a comprehensive view of the user. The data processor 150 can use data mining algorithms to extract actionable insights from the aggregated data. The data processor 150 performs statistical analysis to identify trends and correlations, which helps refine the user profile. In addition, the data processor 150 ensures data consistency and integrity by implementing data verification and cleansing procedures.

[0024] When accessing the platform, the user can be input-requested to fill in the registration form provided by the registration module 110. This form collects basic data such as name, contact information, type of organization, specific role within the organization, etc. The registration module 110 verifies the accuracy and completeness of the data and collaborates with an external identity verification system if necessary. In addition, the registration module 110 queries an external database to enrich the user profile with additional information.

[0025] Once the registration data is collected, the role identification engine 120 processes this data to classify the user into specific role categories. The role identification engine 120 can analyze the data using a combination of rule-based logic and machine learning algorithms. For example, NLP techniques can be used to extract keywords and phrases indicating job titles, department names, and responsibilities. The role identification engine 120 compares these with pre-defined role classification methods and makes a determination of the user's role. As the role identification engine 120 learns from new data and feedback, the role classification can be refined over time.

[0026] When the user starts an interaction with the platform, the interaction tracker 130 logs all actions in real time. This can include navigation paths, dwell times in various sections, frequency of function use, responses to system notifications and alerts. The interaction tracker 130 can use an event-driven architecture to capture interaction data and store it in a central repository. The interaction tracker 130 can use CEP to detect important patterns and anomalies in user behavior and provide deeper insights into the user's engagement and preferences.

[0027] The preference aggregator 140 compiles data from the registration module 110 and the interaction tracker 130 to create a comprehensive preference profile for the user. The preference aggregator 140 can analyze the similarity between the current user and other users with similar profiles using collaborative filtering techniques. Also, the preference aggregator 140 can use content-based filtering to recommend functions and content based on the user's past interactions and set preferences. The preference aggregator 140 continuously updates the user profile as new interaction data becomes available.

[0028] The data processor 150 integrates data from all components to generate detailed user personas. This involves data fusion techniques to combine structured data, such as registration details, with unstructured data, such as interaction logs. The data processor 150 can use data mining algorithms to extract valuable insights and identify trends and correlations. For example, it may discover that users with certain roles tend to use certain features more frequently. The data processor 150 ensures data consistency and integrity through validation and cleansing procedures, creating refined and actionable user personas.

[0029] System 100 can be configured to continuously learn from user interactions and adapt accordingly. The AI ​​components within the role identification engine 120 and preference aggregator 140 can be regularly updated with new data, allowing for refinement of models and predictions. This ensures the platform remains responsive to users' changing needs and behaviors. A feedback loop can be established to incorporate user feedback into the system, improving its accuracy and relevance over time.

[0030] Based on the generated user personas, the platform dynamically adjusts its interface and functionality. Individualized UI / UX components utilize data from data processor 150 to display widgets, shortcuts, and actions customized to the user's role and preferences. This customization enhances the user experience by providing a focused and efficient interface that aligns with the user's specific needs within the IT distribution network.

[0031] System 100 can parse and analyze free-text input using NLP within the role identification engine 120, extract relevant information, and classify the user's role. Machine learning algorithms can be used to refine role classification and preference predictions based on historical data and ongoing interactions. The interaction tracker 130 can capture real-time user interactions using an event-driven architecture, enabling immediate logging and analysis. CEP can be applied to detect patterns and anomalies in user behavior and provide insights into user engagement. The preference aggregator 140 can recommend relevant features and content using collaborative filtering and content-based filtering techniques. The data processor 150 can integrate structured and unstructured data using data fusion techniques to ensure a comprehensive view of the user. Data mining algorithms can be used to extract actionable insights from the aggregated data and identify trends and correlations.

[0032] This detailed embodiment in Figure 1 outlines the technical aspects and operation of System 100 for persona identification, highlighting the integration and interaction of various components to deliver a user-customized platform experience within the IT distribution network.

[0033] Figure 2 illustrates a system 200 for personalized UI / UX. System 200 can be configured to dynamically adjust the interface based on identified personas. System 200 may include a UI engine 210, a widget selector 220, a shortcut manager 230, an action recommender 240, a real-time data integrator 250, a customization module 260, and a learning AI 270.

[0034] The UI engine 210 can be configured to generate a user interface based on user persona data received from the system 100. The UI engine 210 ensures that it can build the basic framework of the interface and be flexible and adaptable to fit various widgets and components according to the user's role and preferences. The UI engine 210 works closely with other components of the system 200 to integrate all individualized elements.

[0035] The widget selector 220 plays a crucial role in customizing the user interface by selecting the appropriate widgets that align with the user's role and preferences. The widget selector 220 utilizes data from user personas to determine which widgets can be most beneficial to the user. For example, an MSP administrator might have widgets related to quick order initiation, quotes, invoice payments, and sales interactions, while a vendor might see widgets related to orders, shipments, marketing activities, and partner performance. The widget selector 220 continuously updates its widget selection based on real-time interaction data and feedback from the learning AI 270.

[0036] The Shortcut Manager 230 enhances the user experience by displaying frequently used shortcuts customized to the user's role and interaction patterns. The Shortcut Manager 230 analyzes the user's navigation and usage history to identify the most frequently accessed functions. These shortcuts can then be prominently displayed on the user interface, allowing the user to quickly access the tools and information they need. The Shortcut Manager 230 dynamically adjusts shortcuts as the user's interaction patterns evolve over time.

[0037] Action Recommender 240 suggests actions based on the user's role-specific needs and ongoing activities. It can anticipate user requests and suggest appropriate actions using predictive analytics and machine learning algorithms. For example, if an MSP administrator frequently reviews quotes and invoices, Action Recommender 240 might suggest actions related to these tasks. Action Recommender 240 can be continuously refined by the learning AI 270, improving the accuracy and relevance of its suggestions.

[0038] The Real-Time Data Integrator 250 updates the user interface with real-time data related to the user's role and preferences. The Real-Time Data Integrator 250 collects data from various sources, including internal databases, external APIs, and third-party services, ensuring that users have access to the latest information. This component ensures that the data displayed on the interface is up-to-date, improving the user's ability to make informed decisions. The Real-Time Data Integrator 250 works closely with the UI Engine 210 to dynamically update the interface as new data becomes available.

[0039] The customization module 260 allows users to manually adjust the interface to better suit their preferences. The customization module 260 provides a user-friendly interface where users can select and place widgets, adjust settings, and personalize their experience. This module records user adjustments and preferences, feeding this data back to the learning AI 270 to further refine the system's ability to predict and meet user needs. The customization module 260 ensures users have control over the entire interface, increasing overall platform satisfaction.

[0040] The learning AI 270 enables continuous learning and improvement within the system 200. The learning AI 270 analyzes interaction data, user feedback, and system performance to refine the algorithms used by the widget selector 220, shortcut manager 230, action recommender 240, and real-time data integrator 250. The learning AI 270 can identify patterns and trends in user behavior using machine learning techniques, enabling the system to make more accurate predictions and adjustments over time. The learning AI 270 ensures that the system 200 always responds to the changing needs of the user and maintains adaptability.

[0041] This process begins with the UI engine 210 building the basic framework of the user interface based on user persona data. Next, the widget selector 220 determines the widgets most likely to be relevant to the user and integrates them into the interface. The shortcut manager 230 analyzes the user's navigation and usage history to identify frequently used functions, which can then be displayed as shortcuts on the interface. The action recommender 240 can use predictive analytics to suggest relevant actions based on the user's role-specific needs and ongoing activities. The real-time data integrator 250 ensures that the data displayed on the interface is always up-to-date by collecting and integrating real-time data from various sources. The customization module 260 allows the user to manually adjust the interface, and these adjustments can be recorded and fed back to the learning AI 270. The learning AI 270 continuously refines the system's algorithms based on interaction data, user feedback, and system performance, ensuring that the system remains responsive and adaptable to user needs.

[0042] The learning AI270 can improve personalization and recommendation processes by utilizing a wide variety of machine learning algorithms. Some non-limiting examples include decision trees for role classification, support vector machines (SVMs) for refining user preferences, and the use of reinforcement learning to dynamically adjust shortcuts and interface elements based on user interactions. In addition, the learning AI270 can employ deep learning models such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks to analyze sequential data and recognize long-term user behavior patterns. These models are continuously trained using real-time interaction data to ensure that the system effectively adapts to changes in user preferences and behavior.

[0043] The UI engine 210 is configured to generate the user interface, ensuring flexibility and adaptability. The widget selector 220 customizes the interface by selecting relevant widgets and updating the selection based on real-time interaction data and feedback from the learning AI 270. The shortcut manager 230 displays frequently used shortcuts and dynamically adjusts to the evolution of the user's interaction patterns. The action recommender 240 suggests actions based on the user's needs using predictive analytics and machine learning algorithms refined by the learning AI 270. The real-time data integrator 250 updates the interface with the latest data, ensuring informed decision-making. The customization module 260 allows for manual adjustments, improving user control and satisfaction. The learning AI 270 analyzes data to refine the system algorithms, ensuring continuous improvement and adaptability.

[0044] System 200 can deliver personalized UI / UX experiences using a combination of technologies. The UI engine 210 can accommodate various widgets and components using a flexible framework. The widget selector 220 can determine relevant widgets using data analysis and update selections based on real-time data. The shortcut manager 230 can display frequently used functions using navigation and usage history analysis. The action recommender 240 can suggest relevant actions using predictive analytics and machine learning. The real-time data integrator 250 can ensure the display of up-to-date information using data collection and integration techniques. The customization module 260 can use a user-friendly interface for manual adjustments. The learning AI 270 can analyze data and refine system algorithms using machine learning.

[0045] In summary, Figure 2 illustrates a system 200 for personalized UI / UX, highlighting the integration and interaction of components such as a UI engine 210, widget selector 220, shortcut manager 230, action recommender 240, real-time data integrator 250, customization module 260, and learning AI 270, providing a dynamic user experience customized for each user within the IT distribution network.

[0046] Figure 3 illustrates a system 300 for content customization. The system 300 can be configured to customize content based on user behavior and needs. The system 300 may include a static content generator 310, a dynamic content generator 320, a behavior analyzer 330, a content delivery network 340, and a feedback loop 350.

[0047] The static content generator 310 provides role-specific static content to the user interface. The static content generator 310 utilizes predefined templates and content repositories to deliver information that may be relevant to the user's role and preferences. This static content can include user manuals, best practice guides, white papers, and other documents that remain relatively unchanged over a long period. The static content generator 310 ensures that each user has access to basic content customized to their specific needs within the IT distribution network.

[0048] The dynamic content generator 320 updates content in real time based on user interactions and actions. Using algorithms, the dynamic content generator 320 can analyze the user's ongoing activity and adjust the content presented accordingly. For example, if a user frequently searches for information about a particular product, the dynamic content generator 320 will prioritize displaying the latest updates, offers, and news related to that product. This component ensures that content remains relevant and timely, improving user engagement and decision-making processes.

[0049] The Behavior Analyzer 330 plays a crucial role in understanding and predicting user behavior. It collects and analyzes data from user interactions, including navigation patterns, search queries, and content consumption habits. By employing machine learning and statistical analysis techniques, the Behavior Analyzer 330 identifies user behavioral trends and patterns. These insights can be used to inform the content customization process, ensuring content aligns with users' evolving needs and preferences.

[0050] The content delivery network 340 ensures the rapid and efficient delivery of both static and dynamic content to the user interface. Using a distributed network of servers, the content delivery network 340 can deliver content with minimal latency and high availability. This component integrates with various content sources, including internal databases and external APIs, to fetch and deliver content in real time. The content delivery network 340 ensures that users receive a persona-based, customized content experience, regardless of their geographical location or the complexity of the content.

[0051] The feedback loop 350 collects user feedback and refines the content customization process. The feedback loop 350 gathers user insights about the content experience using various mechanisms, including user surveys, interaction logs, and direct feedback forms. This feedback can be analyzed and used to adjust the algorithms and strategies employed by the static content generator 310, dynamic content generator 320, and behavior analyzer 330. The feedback loop 350 ensures that the content customization process can be continuously improved based on actual user experience and preferences.

[0052] The process begins with a static content generator 310 providing basic content customized to the user's role and preferences. A dynamic content generator 320 then updates this content in real time based on user interactions and behavior, ensuring that the content remains relevant and timely. A behavior analyzer 330 collects and analyzes data from user interactions to identify trends and patterns, which can then be used to refine the content customization process. A content delivery network 340 ensures the rapid and efficient delivery of content to the user interface, integrating with various content sources to fetch and deliver content in real time. A feedback loop 350 collects user feedback, refining the content customization process and ensuring continuous improvement and alignment with user needs.

[0053] The static content generator 310 provides role-specific static content and utilizes predefined templates and content repositories. The dynamic content generator 320 updates content in real time, employing algorithms to analyze user activity and adjust content presentation. The behavior analyzer 330 collects and analyzes data from user interactions, employing machine learning and statistical analysis techniques to identify trends and patterns. The content delivery network 340 employs a distributed network of servers and integrates with various content sources to achieve fast and efficient content delivery. The feedback loop 350 collects user feedback, gathers insights using various mechanisms, and adjusts the content customization process.

[0054] System 300 can achieve customized content customization using a combination of technologies. The static content generator 310 can deliver role-specific static content using predefined templates and content repositories. The dynamic content generator 320 can analyze user activity using algorithms and update content in real time. The behavior analyzer 330 can identify user behavior trends and patterns using machine learning and statistical analysis techniques. The content delivery network 340 can ensure fast and efficient content delivery using a distributed network of servers. The feedback loop 350 can collect user feedback using various mechanisms and refine the content customization process.

[0055] In summary, Figure 3 illustrates a system 300 for content customization, highlighting the integration and interaction of components such as a static content generator 310, a dynamic content generator 320, a behavior analyzer 330, a content distribution network 340, and a feedback loop 350, providing customized content based on user behavior and needs within the IT distribution network. This system ensures that users receive relevant, timely, and engaging content, improving the overall experience and decision-making process.

[0056] Figure 4 illustrates a system 400 for AI learning and personalization. System 400 can be configured to continuously learn from user interactions using AI and improve personalization. System 400 may include an interaction logger 410, a behavior modeler 420, a personalization engine 430, a recommendation system 440, and a performance monitor 450.

[0057] The interaction logger 410 can be configured to log all user interactions within the platform. The interaction logger 410 captures data such as navigation paths, time spent in different sections, frequency of feature use, responses to notifications, and actions taken. This data can be stored in a centralized repository and serves as the foundation for learning and personalization processes. The interaction logger 410 ensures that comprehensive and accurate interaction data is available for analysis.

[0058] The behavior modeler 420 analyzes data collected by the interaction logger 410 and models user behavior. The behavior modeler 420 can identify patterns, trends, and correlations in user behavior using machine learning algorithms and statistical analysis techniques. These behavior models can be continuously refined as new interaction data becomes available. The behavior modeler 420 helps understand user preferences, habits, and needs for effective personalization.

[0059] The personalization engine 430 can dynamically adjust the interface and content using behavioral models generated by the behavior modeler 420. Using AI techniques, the personalization engine 430 can predict in real time which features, content, and actions are most relevant to each user. This component ensures that the user interface and content can be customized to the user's individual needs and preferences, improving the overall experience. The personalization engine 430 interacts closely with other components of the platform, implementing these adjustments in real time.

[0060] The recommendation system 440 provides personalized recommendations based on user behavior models and interaction data. Using collaborative filtering, content-based filtering, and hybrid recommendation techniques, the recommendation system 440 can suggest products, features, and actions that are likely to interest the user. These recommendations can be presented in a contextually appropriate manner, ensuring they are timely and useful. The recommendation system 440 continuously updates its recommendation algorithms based on user feedback and interaction data to ensure the accuracy and relevance of recommendations are maintained.

[0061] In some embodiments, the recommendation system 440 can employ collaborative filtering, content-based filtering, and hybrid recommendation techniques to suggest products and services that are highly relevant to the user. This may include cross-selling and upselling opportunities, and the system analyzes purchase patterns and user behavior to recommend complementary products and services. For example, if a user purchases a laptop, the system may suggest additional warranties, software packages, and accessories that enhance the main product. These recommendations are generated using a combination of matrix decomposition techniques for collaborative filtering and cosine similarity for content-based filtering.

[0062] The Performance Monitor 450 monitors the effectiveness of AI-driven personalization and recommendation processes. It collects data on key performance indicators (KPIs) such as user engagement, satisfaction, and retention. This component can use statistical analysis and machine learning techniques to assess the impact of the personalization and recommendation processes on these KPIs. The Performance Monitor 450 provides feedback to other components, enabling continuous improvement and refinement of the AI-driven processes.

[0063] The process begins with the interaction logger 410 capturing detailed user interaction data. The behavior modeler 420 then analyzes this data to create a behavioral model, identifying user behavior patterns and tendencies. The personalization engine 430 uses these behavioral models to dynamically adjust the interface and content, ensuring they are tailored to the user's preferences and needs. The recommendation system 440 provides personalized recommendations based on the behavioral models and interaction data, presenting them in a contextually appropriate manner. The performance monitor 450 evaluates the effectiveness of the personalization and recommendation processes and provides feedback for continuous improvement.

[0064] The interaction logger 410 ensures that comprehensive user interaction data can be captured and all relevant actions and behaviors can be logged. The behavior modeler 420 can create and refine behavior models using machine learning algorithms and statistical analysis techniques. The personalization engine 430 can dynamically adjust the interface and content based on the behavior model using AI techniques. The recommendation system 440 can provide personalized recommendations using collaborative filtering, content-based filtering, and hybrid recommendation techniques. The performance monitor 450 can evaluate the effectiveness of AI-driven processes and provide feedback for continuous improvement using statistical analysis and machine learning techniques.

[0065] System 400 can use a combination of technologies to achieve continuous learning and personalization. Interaction logger 410 can capture detailed interaction data using an event-driven architecture. Behavior modeler 420 can model user behavior using machine learning algorithms and statistical analysis techniques. Personalization engine 430 can dynamically adjust interfaces and content using AI techniques. Recommendation system 440 can provide personalized recommendations using collaborative filtering, content-based filtering, and hybrid recommendation techniques. Performance monitor 450 can evaluate the effectiveness of AI-driven processes using statistical analysis and machine learning techniques.

[0066] In summary, Figure 4 illustrates the system 400 for AI learning and personalization, highlighting the integration and interaction of components such as the interaction logger 410, behavior modeler 420, personalization engine 430, recommendation system 440, and performance monitor 450, providing a continuously improving and highly personalized user experience within the IT distribution network. This system ensures that the platform remains responsive to changing user needs and preferences, improving overall satisfaction and engagement.

[0067] Figure 5 shows System 500, an advanced configuration for a customized dashboard in an IT distribution platform, according to several embodiments of the present disclosure. System 500 can be configured to provide a dynamic and personalized dashboard by leveraging integrated subcomponents that enhance data handling, processing, and presentation capabilities. System 500 may include multiple layers, each with specific functionality that contributes to the overall system performance.

[0068] The System 500's Real-Time Data Mesh (RTDM) 510 can be configured to integrate legacy systems into a distribution platform as an AI-based, vendor- and customer-independent framework. RTDM 510 efficiently manages complex data workflows. This layer can include an ingestion module 511, which automates the data ingestion process from various sources such as IoT devices, crowdsourced data, and traditional databases. The normalization and cleansing module 512 can use advanced algorithms and machine learning models to clean and standardize incoming data, ensuring quality and consistency. The data transformation module 513 supports real-time data streaming transformations, facilitating immediate analysis and decision-making processes. The metadata management module 514 effectively manages metadata, enhancing data governance and discoverability. The storage optimization module 515 optimizes data storage and retrieval, tailoring data storage methods and structures based on usage patterns and access frequency.

[0069] In some embodiments, RTDM510 can leverage technologies such as Apache Kafka for efficient data ingestion and streaming, Apache Flink for real-time data processing, Apache Storm for event-driven data analysis, or other platforms. These technologies enable RTDM to handle large volumes of data from various sources and ensure that the data is processed and integrated in real time. The distribution platform maintains up-to-date and relevant information, improving the overall user experience.

[0070] The Advanced Analytics and Machine Learning (AAML) module 520 of System 500 functions as the core analytics engine, capable of performing complex data processing and analysis. The Advanced Analytics Engine 521 uses state-of-the-art artificial intelligence models to perform complex data analysis, including predictive and prescription analytics. The Process Automation Hub 522 integrates complex workflows across various components and external systems to improve operational efficiency. The Learning and Adaptive Module 523 contains self-learning algorithms that adapt processing strategies based on new data insights and operational feedback. The Integration Gateway 524 facilitates data integration with external platforms, enabling System 500 to function within a larger ecosystem of business tools.

[0071] The single-pane-of-glass (SPoG) user interface (UI) 530 of system 500 provides dynamic and customizable user interaction capabilities. The dynamic user interface engine 531 enhances user engagement by providing a highly customizable interface tailored to the user's role and individual preferences. In some embodiments, the dynamic user interface engine 531 allows users to interactively customize the interface through features such as drag-and-drop functionality for widgets and real-time switching of interface elements. These customization options can be implemented using React.js and asynchronous data processing, or other preferred technologies, to ensure smooth and responsive user interaction. The system can track these customizations and implement reinforcement learning to prioritize frequently used features and dynamically adjust the interface layout based on user behavior.

[0072] The interactive visualization toolkit 532 can include a wide range of data visualization options, such as 3D modeling and predictive scenario visualization, enabling users to interact with data in innovative ways. The real-time collaboration framework 533 supports advanced collaboration tools, including virtual workspaces and real-time data manipulation, to facilitate effective teamwork. The security and compliance module 534 implements advanced security features, such as biometric access control and advanced encryption standards, to ensure data integrity and compliance with global data protection regulations.

[0073] System 500's cross-layer services 540 provide services across the data management layer, processing layer, and presentation layer. The audit and compliance tracker 541 monitors and records all operations within the system to ensure compliance with internal and external regulations. The performance optimization engine 542 dynamically adjusts system resources and processing parameters to optimize performance across all layers. The unified communications portal 543 integrates communication tools across the platform, enabling users to interact within the system environment through voice, video, and text.

[0074] This process begins with the Real-Time Data Mesh (RTDM) 510 managing data ingestion, normalization, cleansing, transformation, and storage optimization. The ingestion module 511 collects data from various sources, and the normalization and cleansing module 512 cleans and standardizes it. The data transformation module 513 performs real-time transformations, and the metadata management module 514 and storage optimization module 515 handle metadata and storage efficiency, respectively.

[0075] Next, the Advanced Analytics and Machine Learning (AAML) module 520 processes the data. The Advanced Analytics Engine 521 performs complex analyses, including predictive and prescription analytics. The Process Automation Hub 522 integrates the workflow, and the Learning and Adaptive Module 523 can adapt strategies based on new insights using self-learning algorithms. The Integration Gateway 524 ensures data integration with external platforms via RTDM.

[0076] The Single Pane of Glass (SPoG) user interface (UI) 530 then presents the user with a personalized dashboard. The dynamic user interface engine 531 can customize the interface according to the user's role and preferences. The interactive visualization toolkit 532 provides various data visualization options, while the real-time collaboration framework 533 supports collaboration tools and real-time data manipulation. The security and compliance module 534 ensures data integrity and compliance.

[0077] Ultimately, the cross-layer service 540 enhances the overall system performance and compliance. The audit and compliance tracker 541 monitors system operations, the performance optimization engine 542 optimizes resources and processing parameters, and the unified communications portal 543 integrates communication tools across the platform. System 500 provides a scalable and secure platform capable of supporting a wide range of IT distribution operations by offering dynamic, personalized dashboards. This system ensures that users receive relevant, timely, and engaging content tailored to their roles and preferences within the IT distribution network.

[0078] The actions shown in the illustrative methods are not exhaustive, and it should be understood that other actions may similarly be performed before, after, or between any of the illustrated actions. In some embodiments of this disclosure, the actions may be performed in a different order and / or different order.

[0079] Figure 6 is a flowchart of Method 600 for performing customized content delivery and interface personalization in an IT distribution platform, according to some embodiments of the present disclosure. In some embodiments, Method 600 provides operational steps for customizing the user interface and content based on individual user personas. In some embodiments, Method 600 performs continuous learning and adaptation to improve the user experience. Based on the disclosure herein, the operations in Method 600 may be performed in different orders and / or different orders.

[0080] In operation 605, computing devices can collect initial user data through a registration process. This data may include user information such as name, contact information, organization, role, and specific preferences. The registration module interfaces with external databases to enrich user profiles with additional information, using APIs to retrieve data from social media profiles, professional networks such as LinkedIn®, and industry databases. This initial data collection provides a comprehensive view of the user and lays the foundation for personalized experiences.

[0081] In operation 610, the computing device analyzes the collected data and identifies the user's role within the IT distribution network. The role identification engine can classify users into categories such as MSP administrators, sales representatives, vendors, resellers, and end customers, using predefined rules and machine learning algorithms such as decision trees and support vector machines (SVMs). For example, a decision tree could be used to classify users based on job titles and department names extracted from registration data, while an SVM could refine this classification by considering additional context and nuances.

[0082] In operation 615, computing devices monitor user interactions within the platform. Interaction trackers log actions such as navigation paths, time spent in different sections, frequency of feature use, and responses to notifications and alerts. This interaction data can be stored in a centralized repository and serve as the foundation for AI-driven personalization processes. For example, interaction trackers can efficiently capture real-time interaction data using an event-driven architecture in combination with Apache Kafka.

[0083] In operation 620, the computing device aggregates user preferences from collected data and interaction logs. The preference aggregator uses collaborative filtering algorithms such as matrix factorization and content-based filtering techniques such as TF-IDF (term frequency-inverse document frequency) to compile a comprehensive preference profile for each user, predicting user preferences and recommending relevant features, content, and actions. For example, collaborative filtering may suggest products based on similarity with other users, while content-based filtering may recommend items that share characteristics with previously interacted content.

[0084] In Operation 625, computing devices can generate personalized user interfaces. The UI engine builds the interface framework, while the widget selector can group similar widgets using algorithms such as k-means clustering to determine which widgets are most relevant to the user's role and preferences. The shortcut manager can prioritize shortcuts based on past usage patterns using reinforcement learning, and the action recommender can suggest actions based on user needs using predictive analytics models such as logistic regression. This customization improves user efficiency and satisfaction by presenting a personalized and intuitive interface.

[0085] In operation 630, computing devices dynamically update the content displayed on the user interface. A dynamic content generator can update content based on user interaction using real-time data streams and technologies such as Apache Storm. This ensures that the content remains relevant and timely, reflecting the latest information and trends. A real-time data integrator can ensure that the displayed information is up-to-date and accurate by integrating data from various sources, including internal databases, external APIs, and third-party services, using tools such as Apache Flink.

[0086] In operation 635, computing devices can use AI techniques to continuously learn from user interactions and improve personalization. Learning AI can analyze interaction data and user feedback using deep learning models such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. These models are particularly good at processing sequential data and identifying long-term dependencies, enabling the system to recognize patterns and trends in user behavior over time. For example, an RNN may learn that a particular user tends to order certain types of products at the end of each quarter and adjust the interface and content to highlight these products as the end of the quarter approaches. Learning AI continuously updates the model with new data, ensuring that the system dynamically adapts to changes in user preferences and behavior.

[0087] In operation 640, the computing device provides personalized recommendations to the user. The recommendation system can use a hybrid recommendation approach that combines collaborative filtering, content-based filtering, and knowledge-based systems. For example, collaborative filtering may use matrix factorization to identify potential factors in user-item interactions, while content-based filtering leverages cosine similarity to recommend items similar to those previously interacted with. Knowledge-based systems further refine recommendations using domain-specific rules. These recommendations are presented in a contextually relevant manner, ensuring they improve the user's decision-making process and engagement with the platform.

[0088] In operation 645, the computing device monitors the effectiveness of the personalization and recommendation process. The performance monitor collects data on key performance indicators (KPIs) such as user engagement, satisfaction, and retention. This component can use statistical analysis techniques such as ANOVA (analysis of variance) and machine learning models such as random forests to evaluate the impact of personalization efforts. The performance monitor provides feedback to other components, enabling continuous improvement and refinement of the AI-driven process. For example, if a decline in engagement is detected, the performance monitor may trigger a re-evaluation of the recommendation algorithm and adjustment of the interface.

[0089] In Operation 650, the computing device collects and integrates user feedback to refine the system. The feedback integrator uses natural language processing (NLP) techniques such as sentiment analysis and topic modeling to gather insights from user surveys, interaction logs, and direct feedback forms. This feedback can be analyzed to identify areas for improvement and adjust the algorithms and strategies employed by the system. By incorporating user sentiment and direct suggestions into the refinement process, the feedback integrator ensures that the system remains aligned with user expectations and requirements.

[0090] In operation 655, computing devices continuously adapt user interfaces and content based on sophisticated algorithms and updated behavioral models. This operation ensures that the platform remains responsive to evolving user needs and preferences, thereby improving overall user satisfaction and engagement. The system's continuous adaptive capacity ensures that the platform evolves with the user, always providing a highly relevant and personalized experience. For example, adaptive algorithms such as multi-armed bandit strategies can be used to dynamically test and deploy different interface variations to optimize user engagement and satisfaction.

[0091] In some embodiments, Method 600 provides a comprehensive approach to delivering customized content and personalized interfaces within an IT distribution platform, leveraging AI and real-time data to enhance the user experience. The operations in Method 600 can be adapted to suit specific implementation requirements and can be executed in different sequences. Novel aspects of Method 600, particularly the continuous learning and adaptation process and dynamic content updates facilitated by learning AI, differentiate the Method from conventional static or semi-static systems. The Method ensures that each user receives a highly personalized and engaging experience tailored to their unique role and preferences within the IT distribution network.

[0092] It should be understood that the detailed description section, rather than the abstract and summary sections, is intended to be used to interpret the claims. The abstract and summary sections may describe one or more, but not all, exemplary embodiments of the invention as intended by the inventors, and are therefore not intended to limit the invention and the appended claims in any way.

[0093] The present invention has been described above with the assistance of function-building blocks illustrating the implementation of specific functions and their relationships. The boundaries of these function-building blocks are arbitrarily defined herein for the sake of explanation. Alternative boundaries can be defined, as long as the specific functions and their relationships are adequately implemented.

[0094] The prior description relating to specific embodiments fully illustrates the general nature of the invention, and such specific embodiments can be readily modified and / or adapted to various uses without departing from the general concept of the invention, without requiring any unnecessary experimentation, by applying the knowledge of those skilled in the art. Such adaptations and modifications are therefore intended to be within the meaning and scope of equivalents of the disclosed embodiments, based on the teachings and guidance presented herein. The expressions or terms herein are for illustrative purposes only and not intended to limit, and therefore should be understood to those skilled in the art to be interpreted in light of the teachings and guidance.

[0095] The breadth and scope of the present invention should not be limited by any of the exemplary embodiments described above, but should be defined solely by the following claims and their equivalents.

Claims

1. A system for providing a customized platform for all personas in distribution. A server connected to a processor, The registration module collects initial user data through the registration process. The role identification engine analyzes the collected data to identify the user's role within the IT distribution network. The interaction tracker monitors user interactions within the platform. The preference aggregator aggregates user preferences from the collected data and interaction logs. The Single Pane of Glass User Interface (SPoG UI) generates personalized user interfaces. The dynamic content generator dynamically updates the content displayed on the SPog UI. By using a learning-type AI, we continuously learn from user interactions and employ AI techniques to improve personalization. The recommendation system provides personalized recommendations to the user. The performance monitor monitors the effectiveness of the personalization and recommendation process, The system includes a server configured to execute commands to collect and integrate user feedback and refine the system, A system defined by the personalized Spog UI and content based on at least one of the user's role, preferences, and interactions.

2. The aforementioned server, The Real-Time Data Mesh (RTDM) ingestion module allows you to ingest data from various sources. The normalization and cleansing module cleans and standardizes the ingested data. The data conversion module converts the data for real-time analysis. The metadata management module manages metadata, The system according to claim 1, further configured by a storage optimization module to execute instructions that optimize the storage and retrieval of data.

3. The aforementioned server, The advanced analysis engine performs complex data analysis. The process automation hub integrates workflows across various components. Learning and adaptive modules adapt processing strategies based on new data insights. The system according to claim 1, further configured to execute instructions that facilitate data integration with an external platform via an integrated gateway.

4. The aforementioned server, The aforementioned SPoG UI provides a dynamic and customizable user interface. The interactive visualization toolkit provides a variety of data visualization options. The real-time collaboration framework supports collaborative tools and real-time data manipulation. The system according to claim 1, further configured by security and compliance modules to execute instructions that ensure data consistency and compliance.

5. The aforementioned dynamic content generator The system according to claim 1, configured to update content based on user interaction using real-time data streams and data integration techniques.

6. The aforementioned learning-type AI, The system according to claim 1, which employs deep learning models such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks to analyze interaction data and user feedback.

7. The aforementioned performance monitor, The system according to claim 1, which uses statistical analysis and machine learning models to evaluate key performance indicators (KPIs) such as user engagement, satisfaction, and retention rate.

8. The registration module collects initial user data through the registration process, The role identification engine analyzes the collected data to identify the user's role within the IT distribution network, The interaction tracker monitors user interactions within the platform, The preference aggregator aggregates user preferences from the collected data and interaction logs, The Single Pane of Glass User Interface (SPoG UI) generates personalized user interfaces, The dynamic content generator dynamically updates the content displayed on the SPoG UI, By employing AI techniques that continuously learn from user interactions using a learning-type AI, and by improving individualization, The recommendation system provides personalized recommendations to the user, The performance monitor is used to monitor the effectiveness of the personalization and recommendation process, A computer implementation method comprising: collecting and integrating user feedback using a feedback integrator to refine the system.

9. The Real-Time Data Mesh (RTDM) ingestion module allows for the ingestion of data from various sources, The normalization and cleansing module cleans and standardizes the imported data, The data conversion module converts the data for real-time analysis, The metadata management module manages metadata and The method according to claim 8, further comprising optimizing data storage and retrieval using a storage optimization module.

10. The advanced analysis engine enables the execution of complex data analysis, The process automation hub integrates workflows across various components, The learning and adaptive modules adapt processing strategies based on new data insights, The method according to claim 8, further comprising facilitating data integration with external platforms through an integrated gateway.

11. The aforementioned SPoG UI provides a dynamic and customizable user interface, The interactive visualization toolkit provides a variety of data visualization options, The real-time collaboration framework supports collaborative tools and real-time data manipulation, The method according to claim 8, further comprising a security and compliance module executing instructions to ensure data consistency and compliance.

12. The aforementioned dynamic content generator The method according to claim 8, wherein content is updated based on user interaction using real-time data streams and data integration techniques.

13. The aforementioned learning-type AI, The method according to claim 8, wherein interaction data and user feedback are analyzed by employing deep learning models such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks.

14. The aforementioned performance monitor, The method according to claim 8, wherein statistical analysis and machine learning models are used to evaluate key performance indicators (KPIs) such as user engagement, satisfaction, and retention rate.

15. A tangible, non-temporary computer-readable medium having stored instructions, wherein when an instruction is executed by a computer device, the computer device... The registration module collects initial user data through the registration process, The role identification engine performs the operation of analyzing the collected data in order to identify the role of the user within the IT distribution network, The interaction tracker monitors user interactions within the platform, The preference aggregator performs the operation of aggregating user preferences from the collected data and interaction logs, The Single Pane of Glass User Interface (SPoG UI) generates personalized user interfaces, The dynamic content generator dynamically updates the content displayed on the SPoG UI, Learning AI continuously learns from user interactions and employs AI techniques to improve personalization, The recommendation system provides personalized recommendations to the user, The performance monitor operates to monitor the effectiveness of the personalization and recommendation process, A tangible, non-transient, computer-readable medium that causes a feedback integrator to perform the operation of collecting and integrating user feedback to refine the system.

16. The instruction is given to the computer device, The Real-Time Data Mesh (RTDM) ingestion module allows for the ingestion of data from various sources, The normalization and cleansing module cleans and standardizes the imported data, The data conversion module converts the data for real-time analysis, The metadata management module manages metadata and A tangible, non-temporary computer-readable medium according to claim 15, further comprising a storage optimization module that optimizes the storage and retrieval of data.

17. The instruction is given to the computer device, The advanced analysis engine enables the execution of complex data analysis, The process automation hub integrates workflows across various components, The learning and adaptive modules adapt processing strategies based on new data insights, The tangible, non-temporary computer-readable medium according to claim 15, further comprising an integrated gateway that facilitates data integration with external platforms.

18. The instruction is given to the computer device, The aforementioned SPoG UI provides a dynamic and customizable user interface, The interactive visualization toolkit provides a variety of data visualization options, The real-time collaboration framework supports collaborative tools and real-time data manipulation, The tangible, non-temporary computer-readable medium according to claim 15, further comprising security and compliance modules to ensure data consistency and compliance.

19. The aforementioned instruction is given to the dynamic content generator, A tangible, non-temporary, computer-readable medium according to claim 15, which uses real-time data streams and data integration techniques to update its content based on user interaction.

20. The aforementioned instruction is given to the learning AI, A tangible, non-temporary computer-readable medium according to claim 15, which employs deep learning models such as recurrent neural networks (RNNs) and long-term short-term memory (LSTM) networks to perform analysis of interaction data and user feedback.

Images