Classifying and assigning hyperlinks of content item into different folders method and system

Abstract

The present invention discloses a method and system for classifying and assigning hyperlinks of content items into different folders. The method comprises training a classification model by tracking user selection of content item hyperlinks, allowing users to select or create folder names, checking user profile data, analysing content characteristics, and continuously updatingthe Al model based on usage patterns. New content items are classified by analysing their characteristics, type, context, and source, then applyingthe classification model based on user profile and content attributes. The system includes modules for user interface, profile management, Al model training, content classification, folder management, social media integration, and content indexing. The invention enables intelligent, adaptive content organization that evolves with user behaviour, reducing manual effort while improving content findability and accessibility across multiple platforms.

Description

CLASSIFYING AND ASSIGNING HYPERLINKS OF CONTENT ITEM INTO DIFFERENT FOLDERS METHOD AND SYSTEMTECHNICAL FIELD

[0001] The present invention relates to the field of classifying and assigning hyperlinks of content items into different folders.BACKGROUND ART

[0002] In the digital age, users are constantly accumulating vast amounts of content across various platforms, including websites, social media, and local storage systems. The management of these content items, particularly hyperlinks to online content, has become increasingly challenging. Traditional folder organization systems typically rely on manual categorization, which is time-consuming and often leads to inconsistent classification as users' organizational preferences evolve over time.

[0003] Existing content organization solutions often lack the ability to adapt to user preferences and behaviour patterns. Most systems require users to manually create folder structures and assign content to these folders without providing intelligent assistance based on content characteristics or user history. This results in disorganized digital spaces, difficulty in locating specific content items, and inefficient content management.

[0004] Various approaches have attempted to address these challenges, includingtag- based systems and search functionalities. However, these solutions often fail to provide a personalized organization experience that evolves with user behaviour and preferences. Furthermore, they typically do not leverage machine learning technologies to automate and improve content classification overtime.

[0005] There is a need for an intelligent system that can learn from user behaviour to automatically classify and organize content hyperlinks while continuously adapting to changing user preferences and content types.SUMMARY OF INVENTION

[0006] The present invention discloses a method and system for classifying and assigning hyperlinks of content items into different folders. The invention leverages artificial intelligence to analyse content characteristics and user behaviour patterns, providing a personalized content organization experience that improves overtime.

[0007] According to one aspect of the invention, a method for classifying and assigning hyperlinks of content items into different folders comprises the following steps:• Training a classification model by tracking user-selected content item hyperlinks and the folder names users create or select for organizing these hyperlinks;• Checking user profile data to understand user preferences and behaviour patterns;• Analysing text, parameters, and characteristics of selected content to identify relevant features for classification;• Adding training data comprising user content selection and selected folder name to the Al model and updating user profile data;• Continuously updatingthe Al model based on training data and usage of content items, where content items are assigned, scores based on their usage frequency and patterns;• Classifying new content items by analysing content characteristics, type of content, context, and source;• Applying the classification Al model based on user profile and content characteristics, type, context, and source to suggest appropriate folder placements.

[0008] In some embodiments, the method further comprises generating suggested folder names based on content characteristics and user preferences, enabling users to more effectively organize their digital content.

[0009] In additional embodiments, the method includes analysing social media content to classify incoming content from social network resources, thereby extending the organization system to multiple content platforms.

[0010] The invention also provides a system for implementingthe method, comprising multiple interconnected modules that handle user interaction, profile management, Al model training, content classification, folder management, social media integration, and content indexing.

[0011] The present invention offers significant advantages over existing content organization systems by providing intelligent, adaptive classification that evolves with user behaviour, reducing the manual effort required for content organization while improving content findability and accessibility.The present invention discloses a method and system for classifying and assigning hyperlinks of content items into different folders, said method comprising the following steps: o Training a classification model by:■ Tracking user selection of content item hyperlinks;■ Allowing users to select or create new folder names for selected content;■ Checking user profile data;■ Analysing text, parameters, and characteristics of selected content;■ Adding training data comprising user content selection and selected folder name to an Al model and user profile data;■ Continuously updating the Al model based on training data and usage of content items, where content items are assigned, scores based on their usage; o Classifying new content items by:■ Analysing content characteristics, type of content, context, and source;■ Applying the classification Al model based on user profile and content characteristics type, context, and source.According to some embodiments of the present invention the method fu rther comprising the step of suggesting folder names to users based on content characteristics and user profile data.According to some embodiments of the present invention the method further comprising the step of creating an index of tags for content hyperlinks to facilitate search and retrieval wherein the indexing involves categorizing content based on themes, subjects, and relevant metadata to facilitate precise content retrieval and recommendationAccording to some embodiments of the present invention the method further comprising the step of classifying social media content by analysing content received through social network platforms.According to some embodiments of the present invention the method further comprising the step of matching users based on similarities in their content organization patterns and folder structures, wherein matching is based on a combination of factors for this identification, including user profiles, the context of classified content items, folder names, and the indexing of these content items.According to some embodiments of the present invention the method further comprising the step of generating content recommendations based on user profiles and content classification patterns, wherein the content items are made forfolders that are deemed equivalent between the users, based on the content type, context, and user behaviour patterns.According to some embodiments of the present invention the Al model utilizes vector representation indexing for content similarity analysis.According to some embodiments of the present invention the method further comprisingthe step of analysingtrending patterns of content usage and generating heat maps to visualize these patterns.According to some embodiments of the present content items can be assigned to multiple folders with varying degrees of relevance.According to some embodiments of the present invention the method further comprisingthe steps of:Analysing User Search Request -aiming to identify the nature and intent of the information the user is attempting to retrieve.• Identifying Contextual Clues and Parameters -The system extracts contextual hints from the user’s request, including at least one temporal marker environmental condition participant in a conversation or meeting, current software or application usage, and ongoing project identifiers.• initiates a personalized interactive chat session with the with targeted questions or statements referencing the relevant context in order to stimulate memory and elicit clarifying details from the user.• based on the user’s responses during the chat, the system performs a search across stored folders using metadata associated with the identified context parameters, applying searching by associative tags, temporal data, participant identifiers, or application context to locate the most relevant content item or hyperlink.The present invention provides a system for classifying and assigning hyperlinks of content items into different folders, said system comprising: o A user interface module configured to facilitate user interaction with the system, by Allowing users to select or create new folder names for selected content and tracking user selection of content item hyperlinks; o A profile managing module configured to manage and update user profile data; o An Al model training module configured to train a classification model based on user content selection and folder assignment by analysing text,parameters, and characteristics of selected content;Analysing content characteristics, type of content, context, and source;■ A classification module configured to classify content items by 0 Applying the classification Al model based on user profile and content characteristics type, context, and source and; o A folder database, configured to store folder structures and metadata.According to some embodiments of the present invention the system further comprising a folder name update module configured to suggestfolder names based on content characteristics;According to some embodiments of the present invention the system further comprising a social media content classification module configured to classify content from social network sources;According to some embodiments of the present invention the system further comprising an indexing module configured to tag and index content hyperlinks by creating an index of tags for content hyperlinks to facilitate search and retrieval wherein the indexing involves categorizing content based on themes, subjects, and relevant metadata to facilitate precise content retrieval and recommendationAccording to some embodiments of the present invention the system further comprising matching module configured to match between users based on similarities in their content organization patterns and folder structures, wherein matching is based on a combination of factors for this identification, including user profiles, the context of classified content items, folder names, and the indexing of these content items.According to some embodiments of the present invention the system further comprising content recommendations module configured to create recommendations base on user profiles and content classification patterns, wherein the content items are made forfolders that are deemed equivalent between the users, based on the content type, context, and user behaviour patterns.According to some embodiments of the present invention the system further comprising Recall Module configured to preform the steps of: o Analysing User Search Request -aiming to identify the nature and intent of the information the user is attempting to retrieve.- Identifying Contextual Clues and Parameters, contextual hints from the user’s request, including at least one temporal marker environmental conditions of participants in a conversation or meeting, current software or application usage, and ongoing project identifiers.initiating a personalized interactive chat session with the with targeted questions orstatements referencing the relevant context in order to stimulate memory and elicit clarifying details from the user. based on the user’s responses during the chat, the system performs a search across stored folders using metadata associated with the identified context parameters, applying searching by associative tags, temporal data, participant identifiers, or application context to locate the most relevant content item or hyperlinkBRIEF DESCRIPTION OF DRAWINGSThe present invention will be more readily understood from the detailed description of embodiments thereof made in conjunction with the accompanying drawings of which:

[0012] Figure 1 is a block diagram illustrating a Storage organization platform according to some embodiments of the present invention.

[0013] Fig. 2 is an illustration flow chart of the Al content classification module according to some embodiments of the preset invention.

[0014] Fig. 3 is an illustration flow chart of the Classification module accordingto some embodiments of the preset invention.

[0015] Fig. 4 is an illustration flow chart of the Profile management / update module according to some embodiments of the preset invention.

[0016] Fig. 5 is an illustration flow chart of the Folder name update module forthe user according to some embodiments of the preset invention.

[0017] Fig. 6 is an illustration flow chart of the Classification of social media content module according to some embodiments of the preset invention.

[0018] Fig. 7 is an illustration flow chart of the Indexing / tagging of hyperlinks module according to some embodiments of the preset invention.

[0019] Fig. 8 is an illustration flow chart of the matching module according to some embodiments of the preset invention.

[0020] Fig. 9 is an illustration flow chart of the Recommendation Module according to some embodiments of the preset invention.Fig. 10 is an illustration flow chart of the recall Module according to some embodiments of the preset invention.DETAILED DESCRIPTION

[0021] Before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not limited in its application to the details of construction and the arrangement of the components set forth in the following description or illustrated in the drawings. The invention is applicable to other embodiments or of being practiced or carried out in various ways. Also, it is to be understood that the phraseology and terminology employed herein is forthe purpose of description and should not be regarded as limiting.Storage Organization Platform Overview

[0022] Figure 1 is a block diagram illustrating a Storage organization platform according to some embodiments of the present invention. Storage organization platform is comprised of User interface module 50, Profile managing model 300, Al model training module 100 configured to train classification based on user manual selection of content classification, Classification module 200, configured to classify different content items based on Al model, Folder name update module 500 configured to provide recommendation forfolder name based associated content items, Folder database 600, Classification of social media content 700 configure to classify incoming content from social network resources and indexing module 800 configured to tag content items hyperlinks.

[0023] The storage organization platform is communicating with Designated Social network 20. The Social network is comprised of Profile update 2000 configured to update user preferences-based users' selections and usage behaviour and matching module 2100 configured to match between users based on their profile. The storage organization platform is communicating with third party social network 3000 and content platforms 4000.

[0024] Figure 1 represents a block diagram of a Storage Organization Platform as described in some embodiments of the present invention. This platform is a comprehensive system designed to enhance content organization and accessibility through various interconnected modules and external network communications. Below is an elaborated description of each component:

[0025] Storage organization platform is comprised of:• User Interface Module (50): configured to act as the front-end interface through which users interact with the platform. It provides tools for users to manage and access their stored content, offering a user-friendly environment for navigation and content manipulation.• Profile ManagingModule(300): Responsibleformanaginguser profiles, including the collection, storage, and analysis of user data to understand preferences,behaviours, and needs. This module aids in personalizingthe user experience based on the collected profile information.• Al Model Training Module (100): Configured to train Al models for content classification based on user manual selections and interactions. This module learns from user input to enhance the accuracy and relevance of content classification algorithms.• Classification Module (200): Utilizes the Al model to classify different content items, organizing them into appropriate folders based on content characteristics and user interactions, thereby facilitating easier access and management.

[0026] According to some embodiments of the present invention the Al classifier holds a Llama vector representation indexing vector and / or other indexing / representation of the content. This indexing can be used for finding content similarities, users having similar content or associate similar content in different folders.• Folder Name Update Module (500): Provides recommendations forfolder names based on associated content items. This module analyses the contents of a folder and suggests more descriptive and relevant folder names to improve content findability and organization.• Folder Database (600): A repository where folder structures and metadata are stored, enabling quick retrieval and management of content within the platform. Content item may be saved in more than one platform.• Classification of Social Media Content (700): Configured to classify incoming content from social network resources, ensuring that content sourced from social media is appropriately categorized and stored within the platform.• Indexing Module (800): Tags content items and hyperlinks, facilitating efficient search and retrieval of information within the platform.External Network Communications

[0027] External Network Communications:• Designated Social Network (20): The platform interfaces with a designated social network, comprising: o Profile Update (2000): Updates user preferences based on selections and usage behaviour observed within the social network. o Matching Module (2100): Matches users based on their profiles, enhancing social connectivity and content relevance.• Third-Party Social Network (3000) and Content Platforms (4000): The platform also communicates with third-party social networks and content platforms,integrating external content and social data into the user's storage organization experience.

[0028] This Storage Organization Platform, as depicted in the block diagram, leverages advanced Al and machine learning techniques to adaptto user behaviours and preferences, thereby offering a dynamic and personalized content organization experience.

[0029] According to certain embodiments of the present invention, the system includes a trend module. This module is designed to monitor and analyse the frequency history of how different users interact with various content items. By collecting and evaluating this usage data, thetrend module can create a heat map. This heat map visually represents the trending patterns of the content items over various time periods, illustrating which items are gaining or losing popularity.Al Content Classification Module

[0030] Fig. 2 is an illustration flow chart of the Al content classification module 200 according to some embodiments of the preset invention.

[0031] TheTraining classification model apply thefollowings steps:• Enabling Userto select / create new folder name for new selected content hyperlink. The user may save content hyperlink in more than one folder, creating new folders if necessary; 102• The model provides an interface or mechanism that allows users to select or create a new folder name for the content hyperlink they wish to categorize or organize. This folder name will serve as a label or category for the selected content.• Check user profile data; 103 The model retrieves and processes the user's profile data, which may include information such as preferences, interests, browsing history, or any other relevant data that can help personalize the classification process forthat particular user.• Analyse text, parameters characteristics of selected content 104 The model analyses the selected content, extracting relevant features, parameters, and characteristics. This analysis may include techniques such as text mining, natural language processing, and feature extraction from other types of content (e.g., images, audio, video). The extracted features and characteristics aid in understanding the content and its potential categorization. The extraction of content form video, audio or images, can be preformed by designated Al module which scan the video or image, or transcript the audio, enabling to summarize and categorize the content.• Add training data comprising user content selection and selected folder name to Al model in association with user profile data 106; The model incorporates the user's content selection, the selected folder name (or category label), and the associated user profile data as training data. This training data is used to continuously update and improve theAl model's ability to classify and categorize content accurately.• Continuously update Al model based on training data and usage of content items, where content items are assigned, weights based on their usage; TheAl model is continuously updated based on the accumulated training data and the usage patterns of content items. As users interact with and access various content items, the model tracks and assigns weights to these items based on their usage frequency, recency, or other relevant metrics. This usage data is then incorporated into the model's training process, allowing itto adapt and refine its classification capabilities overtime.Classification Module

[0032] Fig. 3 is an illustration flow chart of the Classification module accordingto some embodiments of the preset invention.

[0033] The process of organizing new content items involves a sophisticated methodology that enhances the user experience through personalized classification and folder suggestions. This process is systematically broken down into several key stages:1 . Analysing Content Characteristics (202): The initial step involves a thorough examination of the content item in question. This analysis includes understanding the content's intrinsic properties, such as its type (e.g., text, image, video), the context within which it is placed, and its source. By dissecting these characteristics, the system gains a comprehensive insight into the nature of the content, enabling itto predict the most relevant organizational structure for it.2. Applying a Classification Al Model (204): With the content's characteristics fully analysed, the next step leverages an Artificial Intelligence (Al) model tailored for classification. Applying classification Al model based on user profile and content characteristics type, context and sourceThe context / accusation parameters include time, events, geographic location, phone activities: messages, conversion call, environment parameters (landscape, weather, smell, music), participants within call or meeting, current application usage, current projects in process, 204This model isn't one-size-fits-all; it is finely tuned to accountforthe user's profile, incorporating their preferences, historical interactions, and specific needs. By doing so, the Al model can intelligently classify content based on a blend of its intrinsic properties and the user's unique profile and behaviour patterns.3. Presentation of Classification Suggestions (206): Following the Al's processing, users are presented with recommendations on how the content should be organized. These suggestions manifest as proposed folder names that the system deems most suitableforthe content item. Alternatively, if the Al deduces that existing categories do not adequately capture the essence of the content, it may propose the creation of a new folder.4. User Interaction for Approval (208): Once the classification suggestions are presented, the user plays a crucial role in the decision-making process. They can approve the Al's recommendation, reject it, or propose an alternative folder name. This stage is critical as it ensures that the user retains control over the organization of their content, allowing for a personalized and satisfactory user experience.5. Content Organization (210): Following the user's decision, the content is promptly updated and placed in the selected folder. This step marks the completion of the content's journey from being unclassified to being neatly organized within the user's digital space.6. Continuous Al Model Refinement (212): The interaction doesn't end with the content's placement. The Al model responsible forthe classification is continuously updated and refined based on the user's reactions to the suggestions. Each approval, rejection, or alternative suggestion feeds back into the model, enhancing its accuracy and personalization capabilities overtime. This ongoing learning process ensures that the system becomes increasingly adept at predicting the user's preferences, making future classifications even more precise and aligned with the user's organizational habits.

[0034] Through this meticulous process, content classification transcends mere automation, becoming a dynamic interaction between the user and the Al. This not only streamlines the organization of digital content but also ensures that the system evolves in harmony with the user's changing preferences and behaviours.Profile Management / Update Module

[0035] Fig. 4 is an illustration flow chart of the Profile management / update module according to some embodiments of the preset invention.

[0036] Updated user profile preferences based on said analysis: GUI for Updating Personal Data (Step 302): Provide a user-friendly interface for users to update their personal information such as: Gender, Location, age, Profession, Preferences or Hobbies.

[0037] Analysing Viewed and Saved Content Characteristics (Step 304):1 . Implement a mechanism to track and analyse the user's content viewing and saving behaviour, including: o Type of content (articles, videos, podcasts, etc.) o Context or subject matter of the content (news, entertainment, sports, technology, etc.) The context / accusation parameters include time, events, geographic location, phone activities: messages, conversion call, environment parameters (landscape, weather, smell, music), participants within call or meeting, current application usage, current projects in process. o Source of the content (specific websites, platforms, or channels)

[0038] Updating User Profile Preferences Based on Analysis:• Analyse the user's content viewing and saving patterns to identify their interests and preferences. (304); user's preferences based on their historical data and the content classifications.• Update the user's profile preferences automatically or suggest personalized recommendations for preferences based on the analysis.• Provide options forthe user to review and manually adjust the suggested preferences if desired;• Continuously refine and improve the preference updates based on ongoing user behaviour and feedback.

[0039] By following these steps, the profile management / update module will enable users to easily update their personal information, analyse their content consumption patterns, and automatically update their profile preferences based on their behaviour and interests. This will lead to a more personalized and relevant experience forthe users.Folder Name Update Module 500;

[0040] Fig. 5 is an illustration flow chart of the Folder name update module forthe user according to some embodiments of the preset invention.

[0041] The Folder Name Update Module operates through a detailed, multi-step process designed to optimize folder names based on the content they contain, user profiles, and Al-driven naming conventions. Here's an elaboration on each step for clarity and depth:1 . Content Classification within Each Folder (Step 502): Initially, the module scans each folder to catalogue all classified content, which includes the retrieval and examination of hyperlink contents. This step ensures a comprehensive understanding of the folder's current content landscape.2. Analysis of Content Characteristics (Step 504): Following content classification, the module conducts a thorough analysis of each content piece's characteristics. This involves identifying the content's context, source, and type (e.g., text, image, video). The goal is to deeply understand the essence and thematic elements of the content within each folder.3. Relevancy Check of Folder Name (Step 506): With content characteristics identified, the module evaluates the current folder name's relevance to these characteristics and the overarching context. This step assesses whether the existing folder name accurately reflects the content it houses.4. Comparison with Similar User Profiles (Step 508): The module then examines folder names used by users with similar profiles who possess folders containing at least some content items that are similar or equivalent. This comparative analysis helps in understanding naming conventions and preferences among users with aligned interests or content collections.5. Suggestion of New Folder Names (Step 510): Leveraging the insights gained, the module suggests at least one newfolder name. This suggestion is based on the relevance of current and other users' folder names, the characteristics of the folder's content, and the application of an Al-based naming model. The Al model incorporates naming trends, user behaviour, and content analysis to generate appropriate and contextually relevant folder names.6. Collection of User Feedback (Step 520): Users are then presented with the suggested names and asked fortheirfeedback, specifically their approval or rejection of the suggestions. This step is crucial for understanding user preferences and ensuring that any folder name changes align with user expectations and satisfaction.7. Folder Name Update or Association (Step 522): Based on user feedback, if a suggested name is approved, the folder is renamed accordingly. If the suggestion is rejected, the module saves at least one of the suggested names in associationwith the current folder name. This associative data can be valuable forfutu re naming suggestions and user preference learning.8. Al ModelTraining Data Update (Step 524): Finally, the Al naming model's training data is updated with information from user reactions and the names suggested during the process. This continuous learning approach allows the Al model to improve its naming suggestions overtime, adaptingto evolving user preferences and content trends.

[0042] In some embodiments, the module may also recommend splitting or joining folders based on the content analysis. Specifically, it could suggestjoining folders when the content items within them are similar, thereby streamliningthe organization and improving accessibility. Conversely, the module may propose splitting a single folder into multiple folders if it identifies significant distinctions between the types of content items it contains. This function allows for a more refined classification system, enabling users to manage and access theirfiles more efficiently by minimizing content overlap and enhancing the categorization of diverse content types.Classification of Social Media Content Module

[0043] Fig. 6 is an illustration flow chart of the Classification of social media content module according to some embodiments of the preset invention.

[0044] Parse received content item hyperlink through social network 702; Parse Received Content Item: The module receives a content item, typically in the form of a hyperlink, from a social network or other online platform. It extracts the necessary information from the hyperlink to identify and access the content item.

[0045] Analyse characteristics of content item hyperlink: context 704; Analyse Content Characteristics and Context: The module analyses various characteristics and contextual information associated with the content item. This may include analysing the content itself (text, images, videos, etc.), metadata, source, related social interactions, and any other relevant information that could aid in understanding the nature and context of the content.

[0046] Classify content item using Al classification module 706; Classify Content using Al Classification Module: Leveraging an Al-based classification model, the module categorizes the content item into predefined classes ortopics. This classification is based on the analysed characteristics and context, as well as the trained Al model's ability to recognize patterns and make informed decisions.

[0047] Suggest user classification folder name 708; Suggest User Classification Folder Name: Based on the Al classification results; the module suggests a folder name or category label for the user to organize and store the content item. The suggested foldername should be descriptive and meaningful, accurately reflecting the content's nature and topic.

[0048] Received user feedback, approval save content item hyperlink to folder 710; Receive User Feedback and Approval: The module presents the suggested folder name to the user and receives feedback or approval. The user can either accept the suggested name, modify it, or providetheir own folder name based on their preferences and understanding of the content.

[0049] Upon user approval saving item hyperlink in folder712; Save Content Item Hyperlink to Folder: Upon receiving user approval, the module saves the content item hyperlink to the specified folder or category. This step ensures that the content is organized and easily accessible forfuture reference or retrieval.

[0050] Update training data of namingAI model 714; UpdateTraining Data of NamingAI Model: The module incorporates the user's feedback and approved folder name into the training data forthe Al naming model. This step helps the Al model learn from user preferences and improve its ability to suggest more accurate and relevant folder names in the future.

[0051] Continuous Learning and Improvement: The module should continuously update and refine the Al classification and naming models based on user interactions, feedback, and newly acquired data. This iterative learning process ensures that the module becomes more accurate and effective overtime, providing better content organization and retrieval capabilities.

[0052] By following these steps, the Classification of Social Media Content Module streamlines the process of organizing and categorizing content from various social media platforms. It leverages Al technology to analyse and classify content, while also incorporating user feedback and preferences to improve the overall user experience and content management capabilities.Indexing / Tagging of Hyperlinks Module 800

[0053] Fig. 7 is an illustration flow chart of the Indexing / tagging of hyperlinks module according to some embodiments of the preset invention.

[0054] The Indexing / tagging of hyperlinks module apply the following steps:1 . Parsing Hyperlink Content (Step 802): This initial step involves the systematic breakdown and examination of the content accessible via each hyperlink. The process entails extracting the text, images, and any other relevant multimedia elements forfurther analysis. This comprehensive parsing aims to gather a thorough understanding of the content's nature and scope, preparing it for in - depth examination in thesubsequent stages.2. Analysing Content's Main Subjects and Context (Step 804): Following the parsing of the hyperlink content, this step focuses on identifying the core subjects and the overall context of the content. Advanced natural language processing (NLP) techniques and context analysis algorithms are employed to discern the primary themes, topics, and the context in which they are presented. This analysis is crucial for understandingthe content's focus, its target audience, and the environment (temporal, geographical, cultural) it pertains to.3. Determining Relevant Tags Based on Analysis (Step 806): Leveraging the insights gained from the content analysis, this phase involves the selection of relevant tags that accurately represent the main subjects and themes identified. This step requires a delicate balance between specificity and generality to ensure the tags are sufficiently descriptive yet broad enough to facilitate effective indexing and retrieval. The process may involve the use of predefined tag libraries, ontologies, or dynamic tag generation based on the content's uniqueness.4. Generating Short Description (Step 807): With the relevant tags determined, a concise and informative description of the hyperlink content is generated. This summaryaimsto capture the essence of the content, highlightingits main points and unique aspects. The description serves as a quick reference for users and systems, offering a snapshot of the content's value and relevance without the need forfull exploration.5. Creating an Index Representing Determined Tags (Step 808): The final step involves the compilation of the determined tags into a structured index. This index acts as a metadata framework, enabling efficient categorization, search, and retrieval of the hyperlink content based on its thematic and contextual markers. The indexing process ensures that each piece of content is appropriately tagged and described, making it easily accessible through search queries and enhancing the overall navigability of the digital content landscape.Matching Module 2100

[0055] Fig. 9 is an illustration flow chart of the matching module according to some embodiments of the preset invention.

[0056] Checking User Folder Names, Classified Content Item Context, and Indexing of User Folders (Step 2102):• User Folder Names Examination: This step involves analysing the names of folders created by users to gain insights into their interests, personal preferences, and potential content categories they prioritize. Folder names can offer valuable cues about the types of content a user interacts with or considers important.• Classified Content Item Context Analysis: Here, the context surrounding classified content items — such as documents, images, and videos within a user's storage — is scrutinized. This includes examining metadata, content themes, and any classification tags previously assigned. The goal is to understand the content's nature and relevance to the user.

[0057] Matching Between Users Based on User Profile, Classified Content Item Context, Folder Names, and Indexing of Classified Content Item (Step 2014):• User Profile ComparisomThisstep involves analysingand comparing the profiles of users, including their stated interests, demographics, and any other available profile information. The aim is to identify similarities and potential areas of shared interest or relevance.• Classified Content Item Context Matching: The contexts of classified content items across users are compared to find matches orsimilarities in content themes, subjects, or categories. This involves looking at the metadata, tags, and other contextual indicators to align users with similar content preferences.• Indexing of Classified Content Item for Cross-User Comparison: This involves usingthe indexed information of classified content items from one user to find matches with the indexed content of another user. The process relies on the structured indices created from content classification and tagging, enabling precise matching based on content themes, subjects, and context.

[0058] Together, these steps form a comprehensive approach to matching users in a system. By leveraging information from user profiles, folder names, content context, and indexing, the module aims to facilitate meaningful connections, recommendations, or content sharing opportunities based on shared interests and content interactions. This methodical approach ensures that the matching is grounded in relevant data points, enhancingthe relevance and quality of matches.

[0059] The module may further recommend following or connecting of similar people having similar content.Recommendation Module 400

[0060] Figure 9 depicts the flowchart of the Recommendation Module as per certain embodiments of the present invention. This module is designed to enhance user experience through personalized content recommendations by following a detailed process:1 . Checking User Folder Names and Indexing Classified Content (Step 402): o User Folder Names Examination: The module begins by inspectingthe names of the folders created by users, aiming to understand theorganizational method and the implied interest areas of the user. Folder names can reflect the type of content stored and the user's personal or professional interests. o Classified Content Item Context Indexing: Alongside, it indexes classified content items within these folders, analysing the context of each content piece. This indexing involves categorizing content based on themes, subjects, and other relevant metadata to facilitate precise content retrieval and recommendation.2. Identifying Users Based on Profiles and Content Context (Step 404): o This step involves identifying users with potentially shared interests or content needs. The module uses a combination of factors forthis identification, including user profiles, the context of classified content items, folder names, and the indexing of these content items. The aim is to find users whose content preferences and organizational habits show significant overlap orsimilarity.3. Recommending Content Items from Matched Users (Step 406): o Once users with similar profiles and content categorizations are identified, the module recommends content items from these matched users' folders to the current user. Recommendations are specifically madeforfolders that are deemed equivalent between the users, based on the content type, context, and user behaviour patterns. This approach ensures that the recommendations are highly relevant and potentially of interest to the user.

[0061] The Recommendation Module operates on a sophisticated analysis of user behaviour, content classification, and indexing to facilitate personalized content suggestions. It leverages detailed profiling and matching algorithms to ensure that users receive recommendations that are not only relevant but also aligned with their specific needs and preferences, thereby enhancingthe overall user experience.Fig. 10 is an illustration flow chart of the recall Module according to some embodiments of the preset invention.This module is designed to enhance user information recall experience through personalized chat by following a detailed process:Analyzing user search information request 902Identifying hint / clues in user requested of context Parmeter 404, including: time, events, geographic location, phone activities: messages, conversion call, environmentparameters (landscape, weather, smell, music), participants within call or meeting, current application usage, current projects in process,Preparing interactive chat with users, by identifying different relevant context parameters and inquiringthe userwith the relevant context parameters 906Based on user response in chat, searching the folder using metadata of context / association parameters 908Fig. 10 is a flowchart illustrating the Recall Module, according to some embodiments of the present invention. This module is designed to enhance the user’s information recall experience through an interactive, context-aware chat interface. The process includes the following steps:• Step 902: Analysing User Search Request -The module receives and analyses a user-initiated search or recall request, aiming to identify the nature and intent of the information the user is attempting to retrieve.• Step 904: Identifying Contextual Clues and Parameters -The system extracts contextual hints from the user’s request, which may include, but are not limited to: temporal markers (e.g., time of day, date), associated events, geographic location, mobile activity (e.g., calls, messages), environmental conditions (e.g., weather, surrounding sounds, landscape visuals, scents, music), participants in a conversation or meeting, current software or application usage, and ongoing project identifiers.• Step 906: Interactive Chat Preparation - Usingthe identified contextual parameters, the module initiates a personalized interactive chat session with the user. The system poses targeted questions or statements referencing the relevant context, in orderto stimulate memory and elicit clarifying details from the user.• Step 908: Context-Based Folder Search - Based on the user’s responses during the chat, the system performs a search across stored folders using metadata associated with the identified context parameters. This includes searching by associative tags, temporal data, participant identifiers, or a pplication context to locate the most relevant content item or hyperlinkAdditional Applications

[0062] According to other embodiments of the present invention the Storage organization platform may be used for desktop / personal computer storage such as windows explorer of Mac finder: newly acquired or generated content file can be storedin folders using the classification modules and Al model as described above. Tags index can be created to the content files, optionally using hash algorithm.According to other embodiments of the present invention the Storage organization platform may be used for organizing Favorites in browser application.

[0063] Storage of content under folders is known as hierarchical, while each item can belong to one folder. A newly suggested way is to be able to look and find the same item under several related folders, perhaps with higher or lower relations.

[0064] Trending aspect is part of the content properties, each item has an historical heatmap that will enable trending understanding, as well as going back in time to what used to be a trend in required period (mainly when searching or relatingto similar items).

[0065] Aspects of the invention to serve also for content classification and folder allocation, also forfile browsers, such as Windows Explorer and Mac Finder: As a new item (document, image, email, SW code, etc....) is being acquired (download, saving, creating as new file), it can be classified by the same folder classification methods as suggested in this doc. Adding suggested hashtag while saving, either automatically suggested or by the user can help when lookingforthe item. Interconnectivity between files, learned by their relations, history of usage, initial hierarchical saving. Thus, when looking for a specific item or even when finding it, similarfiles can be presented nextto it under different clouds representing different relations. E.g. the obvious hierarchical, hashtag related, time related, content related...

Claims

CLAIMS1 . A method for classifying and assigning hyperlinks of content items into different folders, said method comprising the following steps: o Training a classification model by:■ Tracking user selection of content item hyperlinks;■ Allowing users to select or create new folder names for selected content;■ Checking user profile data;■ Analysing text, parameters, and characteristics of selected content;■ Adding training data comprising user content selection and selected folder name to an Al model and user profile data;■ Continuously updatingthe Al model based on training data and usage of content items, where content items are assigned, scores based on their usage; o Classifying new content items by:■ Analysing content characteristics, type of content, context, and source;■ Applying the classification Al model based on user profile and content characteristics type, context, and source.

2. The method of claim 1 , further comprising suggesting folder names to users based on content characteristics and user profile data.

3. The method of claim 1 , further comprising creating an index of tags for content hyperlinks to facilitate search and retrieval wherein the indexing involves categorizing content based on themes, subjects, and relevant metadata to facilitate precise content retrieval and recommendation4. The method of claim 1 , further comprising classifying social media content by analysing content received through social network platforms.

5. The method of claim 1 , further comprising matching users based on similarities in their content organization patterns and folder structures, wherein matching is based on a combination of factors for this identification, including user profiles, the context of classified content items, folder names, and the indexing of these content items.

6. The method of claim 5, further comprising generating content recommendations based on user profiles and content classification patterns, wherein the content items are made forfolders that are deemed equivalent between the users, based on the content type, context, and user behaviour patterns.

7. The method of claim 1 , wherein the Al model utilizes vector representation indexingfor content similarity analysis.

8. The method of claim 1 , further comprising analysing trending patterns of content usage and generating heat maps to visualize these patterns.

9. The method of claim 1 , wherein content items can be assigned to multiple folders with varying degrees of relevance.10.The method of claim 1 , further com prising the steps of o Analysing User Search Request -aiming to identify the nature and intent of the information the user is attempting to retrieve.- Identifying Contextual Clues and Parameters, contextual hints from the user’s request, including at least one temporal marker environmental conditions of participants in a conversation or meeting, current software or application usage, and ongoing project identifiers. initiating a personalized interactive chat session with the with targeted questions orstatements referencing the relevant context in order to stimulate memory and elicit clarifying details from the user. based on the user’s responses during the chat, the system performs a search across stored folders using metadata associated with the identified context parameters, applying searching by associative tags, temporal data, participant identifiers, or application contextto locate the most relevant content item or hyperlink.1 1 . A system for classifying and assigning hyperlinks of content items into different folders, said system comprising: o A user interface module configured to facilitate user interaction with the system, by Allowing users to select or create new folder names for selected content and tracking user selection of content item hyperlinks; o A profile managing module configured to manage and update user profile data; o An Al model training module configured to train a classification model based on user content selection and folder assignment by analysing text, parameters, and characteristics of selected content;Analysing content characteristics, type of content, context, and source;■ A classification module configured to classify content items by 0 Applying the classification Al model based on user profile and content characteristics type, context, and source and; o A folder database, configured to store folder structures and metadata.

12. The system of claim 1 1 further comprising: a folder name update module configured to suggest folder names based on content characteristics;13. The system of claim 10 further comprising: a social media content classification module configured to classify content from social network sources;14. The system of claim 1 1 further comprising an indexing module configured to tag and index content hyperlinks by creating an index of tags for content hyperlinks to facilitate search and retrieval wherein the indexing involves categorizing content based on themes, subjects, and relevant metadata to facilitate precise content retrieval and recommendation15. The system of claim 1 1 further comprising matching module configured to match between users based on similarities in their content organization patterns and folder structures, wherein matching is based on a combination of factors for this identification, including user profiles, the context of classified content items, folder names, and the indexing of these content items.

16. The system of claim 1 1 further comprising content recommendations module configured to create recommendations base on user profiles and content classification patterns, wherein the content items are made forfolders that are deemed equivalent between the users, based on the content type, context, and user behaviour patterns.

17. The system of claim 1 1 , further comprising Recall Module configured to preform the steps of: o Analysing User Search Request -aiming to identify the nature and intent of the information the user is attempting to retrieve.- Identifying Contextual Clues and Parameters, contextual hints from the user’s request, including at least one temporal marker environmental conditions of participants in a conversation or meeting, current software or application usage, and ongoing project identifiers. initiating a personalized interactive chat session with the with targeted questions orstatements referencing the relevant context in order to stimulate memory and elicit clarifying details from the user. based on the user’s responses during the chat, the system performs a search across stored folders using metadata associated with the identified context parameters, applying searching by associative tags, temporal data, participant identifiers, or application contextto locate the most relevant content item or hyperlink.

18. The method of claim 1 , wherein the Al model utilizes vector representation indexingfor content similarity analysis.

19. The method of claim 1 , further comprising ana lysing trending patterns of content usage and generating heat maps to visualize these patterns.

20. The method of claim 1 , wherein content items can be assigned to multiple folders with varying degrees of relevance.

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