Description:FIELD OF THE INVENTION

[0001] The present invention relates to an AI (artificial intelligence)-powered cloud file management system that is develop to categorize, manage unused files by identifying, migrating to a unique address to optimize local storage and enhancing overall file accessibility and system efficiency.

BACKGROUND OF THE INVENTION

[0002] Cloud file management system analyze file usage to optimize local storage, enable seamless offline access and file organization for easy accessibility. Users face storage limitations due to accumulation of rarely accessed or outdated files, leading to degraded device performance and productivity. Cloud storage solutions provide offloading capabilities but typically lack personalized control and seamless offline accessibility. There is a growing need for a smarter file management system that not only optimizes storage but also adapts to user behavior and usage patterns. The requirement is for a solution that autonomously categorize and manage files based on usage frequency, file type, size and last accessed time, users demand to access files offline with minimal disruption and to maintain real-time synchronization when connectivity is restored.

[0003] Traditional file management systems rely heavily on manual file organization, requiring users to categorize, archive or delete files, which leads to inefficient storage usage and user errors. Standard cloud storage solutions do not automatically categorize files or adapt to a user’s behavior, resulting in suboptimal storage and frequent overloading of local devices and also fail to integrate seamlessly with local file systems, requiring users to access cloud files separately, without offline accessibility. Traditional methods don’t predict which files is needed offline, meaning users struggle to access important data during periods of no internet connectivity. When files are moved to the cloud, synchronization occurs only when the device is online, which cause delays or version conflicts. As a result, users experience inefficiencies in storage management, file retrieval and access, when dealing with large volumes of data. The lack of automation, predictive syncing and seamless integration with local systems, highlighting the need for more adaptive cloud file management solution.

[0004] US8620879B2 relates to a server receives from a user's computer a request to store a file and a file hash value. The server determines whether a file with the same hash value is stored on the server. If so, the server grants access to the server's file copy. If not, the server requests the user to upload the file and stores it. The server grants access to the copy by sending the user a pointer to the copy's storage location and associating the user with the pointer in a database. The server can challenge the user's right to access the copy by requesting a file password or a portion of the file stored on the user's computer. The server can limit access to the server's copy to users who successfully respond to the challenge.

[0005] US9280683B1 relates to a method for storage management of client files in a multi-service cloud environment is provided. The method includes receiving a mapped list of available cloud storage services of the multi-service cloud environment. The method further includes receiving categorization of the client files. The method further includes performing a qualitative analysis of the received mapped list of available cloud storage services and the categorized client files, to generate a decision data structure representative of cloud storage preferences of a client. The method further includes storing the client files in the multi-service cloud environment. The method further includes determining whether to encrypt the stored client files. The method further includes tagging individual files of the stored client files, or groups of client files of the stored client files, or a combination of the individually stored client files or the groups of client files for encrypting the stored client files.

[0006] Conventionally, many systems have been developed for cloud file management, however the devices mentioned in the prior arts have limitations pertaining to categorize file by their size, type, identifies unused file to optimize storage, create cloud storage instance with unique address for storing files for easy access, creating virtual placeholders for cloud-stored files for easy access their cloud data, pre-load files for offline use and synchronize modifications to offline-accessed files with the cloud storage instance when the system reconnects to the internet.

[0007] In order to overcome the aforementioned drawbacks, there exists a need in the art to develop a system that is required to be capable of classify files based on their size and type, detect unused files to enhance storage, generate a uniquely address in cloud storage space for storing files with convenient access, create virtual file representations of cloud-stored data for seamless local browsing, preload frequently used files for offline availability and update any changes to the cloud once internet connectivity is restored.

OBJECTS OF THE INVENTION

[0008] The principal object of the present invention is to overcome the disadvantages of the prior art.

[0009] An object of the present invention is to develop a system that is capable of managing cloud storage and provide offline access while maintaining up to date synchronization with cloud instances.

[0010] Another object of the present invention is to develop a system that is capable of autonomously monitors file usage patterns and identifies unused files and categorize based on access frequency, size and type to optimize local storage.

[0011] Another object of the present invention is to develop a system that is capable of creating virtual placeholders for cloud-stored files to ensures that users access easily as local files without switching between different interfaces or apps.

[0012] Yet another object of the present invention is to develop a system that is capable of access files offline with minimal delay and automatically synchronizes any offline modifications with the cloud, maintaining file integrity and consistency across devices and storage environments.

[0013] The foregoing and other objects, features, and advantages of the present invention will become readily apparent upon further review of the following detailed description of the preferred embodiment as illustrated in the accompanying drawings.

SUMMARY OF THE INVENTION

[0014] The present invention relates to an AI (artificial intelligence)-powered cloud file management system that is develop to pre-load frequently accessed cloud-stored files for offline availability, ensuring seamless user access even without internet connectivity and maintains up-to-date synchronization with the cloud when reconnected for minimizing access delays.

[0015] According to an embodiment of the present invention, an AI (artificial intelligence)-powered cloud file management system comprises of an AI (artificial intelligence) file analysis module configured to monitor files on a device, categorize files based on last accessed time, usage frequency, file type, file size and identify unused files for migration, the AI File Analysis Module is further configured to learn user behavior over time and adjust file categorization based on observed patterns, a cloud storage allocation module connected to the AI file analysis module to create a cloud storage instance tied to a unique device address and store unused files identified by the AI file analysis module, the Cloud Storage Allocation Module is further configured to integrate the cloud storage instance with the system’s local file directory, making cloud-stored files appear as part of the local file system.

[0016] According to another embodiment of the present invention, the system further, comprises of a file migration module connected to the AI file analysis module and the cloud storage allocation module to transfer unused files to the cloud storage instance and store metadata locally, the system further comprising allowing a user to override file migration decisions using a User Control Module connected to the File Migration Module, the User Control Module enables customization of migration thresholds based on file age or type, an offline access module connected to the file migration module, configured to provide access to cloud-stored files using locally stored metadata and enable on-demand retrieval of files, the Offline Access Module is further configured to generate placeholder files with thumbnail previews for cloud-stored files to enhance user interaction in the local file manager, a predictive sync module connected to the offline access module, configured to pre-load frequently accessed cloud-stored files for offline use, wherein the system autonomously manages file storage to optimize local device storage and maintain file accessibility, the Predictive Sync Module is further configured to synchronize modifications to offline-accessed files with the cloud storage instance when the system reconnects to the internet.

[0017] While the invention has been described and shown with particular reference to the preferred embodiment, it will be apparent that variations might be possible that would fall within the scope of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0018] These and other features, aspects, and advantages of the present invention will become better understood with regard to the following description, appended claims, and accompanying drawings where:
Figure 1 illustrates a flow chart depicting an AI (artificial intelligence)-powered cloud file management system.

DETAILED DESCRIPTION OF THE INVENTION

[0019] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the invention as defined in the claims.

[0020] In any embodiment described herein, the open-ended terms "comprising," "comprises,” and the like (which are synonymous with "including," "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of," consists essentially of," and the like or the respective closed phrases "consisting of," "consists of, the like.

[0021] As used herein, the singular forms “a,” “an,” and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.

[0022] The present invention relates to an AI (artificial intelligence)-powered cloud file management system that is develop to identify and migrate unused files to cloud storage to optimize local storage space, ensures offline access through locally stored metadata, enables predictive syncing of frequently used files and synchronize with the cloud to provide efficient, continuous and easy file accessibility.

[0023] Referring to Figure 1, illustrate a flow chart depicting an AI (artificial intelligence)-powered cloud file management system.

[0024] The system disclosed herein comprises of an AI file analysis module to manage local file storage by continuously monitoring and analyzing files stored on a device. The AI file analysis module categorizes files using multiple criteria, including last accessed time, usage frequency, file type, file size enabling a understanding of how files are utilized over time. By artificial intelligence and machine learning techniques, the module identifies patterns in user behavior to distinguish between frequently used files and those that are never accessed. Files deemed "unused" are flagged for potential migration to cloud storage, optimizing local storage space without compromising user access. The module operates autonomously in the background, refining classification, ability to evolve through behavioral insights allows to improve the accuracy of file categorization over time for supporting an adaptive storage management system that minimizes manual intervention.

[0025] After categorization of files, a cloud storage allocation module functions as the bridge between the local device and remote cloud infrastructure, working in direct coordination with the AI File Analysis Module. When the AI module identifies files as unused or infrequently accessed, this module creates a dedicated cloud storage instance linked to the device’s unique address. This ensures that the cloud space is securely personalized and isolated for each user or device. Once provisioned, the module organizes and stores the flagged files in this cloud instance, preserving their directory structure and metadata for easy retrieval and also ensures encrypted data transfer and storage to maintain file integrity and security. The cloud storage instance is scalable, allowing to expand as more files are migrated and integrates seamlessly with the local file system to give users the impression that their files remain locally accessible.

[0026] The Cloud Storage Allocation Module is integrating the cloud storage instance with the local file directory, creating the illusion that cloud-stored files reside on the device. This is achieved through the use of file system drivers that link placeholders or symbolic representations of cloud files to the local directory structure. As a result, users browse, search and interact with these files through their regular file manager without needing to distinguish between local and cloud-stored content. When a user attempts to open or modify a cloud-based file, the system fetches the full content on-demand, ensuring a smooth and consistent experience.

[0027] After that, a file migration module serves as operational core for transferring unused or low-priority files, identified by the AI file analysis module, to the cloud storage instance created and managed by the cloud storage allocation module. Once the AI module flags files for migration based on parameters such as last accessed time, usage frequency, file type and size, the file migration module securely moves these files to the designated cloud storage while preserving the file hierarchy and structure. Importantly, stores lightweight metadata such as file name, location, size and last modified date locally on the device to maintain user visibility and enable seamless offline referencing or on-demand retrieval.

[0028] To enhance user control and adaptability, the system includes a User Control Module connected directly to the File Migration Module. This module empowers users to override automatic migration decisions, allowing them to customize migration rules and thresholds based on individual preferences. For instance, a user chooses to exclude certain file types from migration or adjust the file age threshold. These custom settings are applied in real-time, ensuring that the migration logic respects user-defined parameters. This dual-mode approach automated intelligence from the AI module combined with manual user input ensures optimal storage efficiency without compromising on user agency. The file migration module balances automated decision-making with personalized control, effectively managing device storage while maintaining a high level of accessibility and transparency for the user.

[0029] An offline access module maintains the visibility and accessibility of files that have been migrated to the cloud, connected to the file migration module, utilizes locally stored metadata including file names, paths, sizes and timestamps to represent cloud-stored files within the local file directory. This metadata is used to create placeholder files that mimic the appearance and structure of actual files, allowing users to browse and interact with them just as they would with locally stored data. To further enhance usability, the module is equipped to generate thumbnail previews for supported file types providing visual cues that help users quickly identify the content of each cloud-stored file within their file manager.

[0030] When a user attempts to open or interact with a placeholder file, the offline access module initiates an on-demand retrieval process that fetches the full file from the cloud storage instance and temporarily restores to local storage for access or editing. This is done transparently and efficiently, ensuring that users experience minimal delay. The module cache recently accessed cloud files for quicker future access, further optimizing the balance between performance and storage conservation. Importantly, the module operates even when the device is offline by allowing users to view metadata and thumbnails and queues any access requests to be fulfilled once connectivity is restored. The offline access module ensures that cloud-migrated content remains intuitively accessible and user-friendly, bridging the gap between remote storage and local usability without requiring users to switch apps or interfaces.

[0031] A Predictive Sync Module enhances the functionality of the Offline Access Module by anticipating which files the user is most likely to access in the near future and pre-loading those files from the cloud to local storage for offline use. By analyzing access patterns, including file usage frequency, time of day and recent interactions, the module predicts the user's behavior and ensures that the most relevant cloud-stored files are available on the device, even when there is no active internet connection. This preemptive approach minimizes wait times when users need to access files, ensuring a smooth experience with no delays due to file retrieval from the cloud.

[0032] To improving offline usability, the Predictive Sync Module helps optimize local device storage by dynamically managing which files remain on the device and which are offloaded to the cloud, ensures that storage is not overburdened by rarely accessed files while retaining the most frequently used data for quick access. Furthermore, when the device reconnects to the internet, the module automatically syncs any modifications made to offline-accessed files back to the cloud storage instance, ensuring that the cloud reflects the most up-to-date version of each file. This synchronization is performed seamlessly in the background, ensuring that the cloud and local file systems are always in sync without requiring manual intervention from the user. Through the predictive sync module ensures that both file accessibility and storage efficiency are maximized, enhancing the user experience while maintaining the integrity of data across multiple environments.

[0033] The present invention works best in the following manner, where the system disclosed herein comprises the Ai file analysis module monitors files on a device to categorizing them based on criteria such as last accessed time, usage frequency, file type, and file size and identifies unused files for migration to optimize local storage and learns user behavior over time, adapting categorization decisions based on observed patterns. Users can further personalize the migration process through the user control module, which allows customization of migration thresholds based on file age or type. The cloud storage allocation module creates a unique cloud storage instance tied to the device address and stores the unused files identified by the AI module and integrates cloud-stored files into the local file directory, making them appear as part of the local file system. The file migration module is responsible for transferring these files to the cloud storage and retaining metadata locally to ensure file accessibility. The offline access module provides access to cloud-stored files by leveraging locally stored metadata, allowing for on-demand retrieval and generates placeholder files with thumbnail previews to enhance user interaction in the local file manager. The predictive sync module works with the offline access module to pre-load frequently accessed files for offline use, ensuring seamless access even without an internet connection and also synchronizes modifications made to offline files with the cloud storage instance once the device reconnects to the internet. The system autonomously manages file storage, optimizing local device storage while maintaining file accessibility.

[0034] Although the field of the invention has been described herein with limited reference to specific embodiments, this description is not meant to be construed in a limiting sense. Various modifications of the disclosed embodiments, as well as alternate embodiments of the invention, will become apparent to persons skilled in the art upon reference to the description of the invention. , Claims:1) An AI (artificial intelligence)-powered cloud file management system, comprising:

i) an AI (artificial intelligence) file analysis module configured to monitor files on a device, categorize files based on last accessed time, usage frequency, file type, and file size, and identify unused files for migration;
ii) a cloud storage allocation module connected to the AI file analysis module, configured to create a cloud storage instance tied to a unique device address and store unused files identified by the AI file analysis module;
iii) a file migration module connected to the AI file analysis module and the cloud storage allocation module, configured to transfer unused files to the cloud storage instance and store metadata locally;
iv) an offline access module connected to the file migration module, configured to provide access to cloud-stored files using locally stored metadata and enable on-demand retrieval of files; and
v) a predictive sync module connected to the offline access module, configured to pre-load frequently accessed cloud-stored files for offline use, wherein the system autonomously manages file storage to optimize local device storage and maintain file accessibility.

2) The system as claimed in claim 1, wherein the AI File Analysis Module is further configured to learn user behavior over time and adjust file categorization based on observed patterns.

3) The system as claimed in claim 1, wherein the system further comprising allowing a user to override file migration decisions using a User Control Module connected to the File Migration Module, wherein the User Control Module enables customization of migration thresholds based on file age or type.

4) The system as claimed in claim 1, wherein the Cloud Storage Allocation Module is further configured to integrate the cloud storage instance with the system’s local file directory, making cloud-stored files appear as part of the local file system.

5) The system as claimed in claim 1, wherein the Offline Access Module is further configured to generate placeholder files with thumbnail previews for cloud-stored files to enhance user interaction in the local file manager.

6) The system as claimed in claim 1, wherein the Predictive Sync Module is further configured to synchronize modifications to offline-accessed files with the cloud storage instance when the system reconnects to the internet.