Smart analysis and attribution of digital content across social media and e-commerce platforms

Abstract

The present invention pertains to an advanced system and method designed for accurately tracing and attributing the origin of various forms of digital content. Utilizing a combination of machine learning algorithms and data analytics tools, this invention aims to automate the attribution process for a wide array of digital content across social media platforms such as Facebook, Twitter, Instagram, LinkedIn, TikTok, Pinterest, and Snapchat, but not limited to these, as well as emails, text messages, and documents. Specifically, the system is platform-agnostic, functioning seamlessly with an extensive range of current social media and e-commerce platforms such as WooCommerce, Shopify, custom-coded websites, BigCommerce, Magento, Wix, and Webflow. Moreover, the invention is engineered to be forward-compatible, capable of adapting to any future platforms or technologies that facilitate the sharing, distribution, or exchange of digital information in any format or medium.

Description

[0001] Smart Analysis and Attribution of Digital Content Across Social Media and E- Commerce Platforms

[0002] Technical Field of The Invention

[0003] The present invention pertains to an advanced system and method designed for accurately tracing and attributing the origin of various forms of digital content. Utilizing a combination of machine learning algorithms and data analytics tools, this invention aims to automate the attribution process for a wide array of digital content across social media platforms such as Facebook, Twitter, Instagram, Linkedln, TikTok, Pinterest, and Snapchat, but not limited to these, as well as emails, text messages, and documents. Specifically, the system is platform-agnostic, functioning seamlessly with an extensive range of current social media and e-commerce platforms such as WooCommerce, Shopify, custom-coded websites, BigCommerce, Magento, Wix, and Webflow. Moreover, the invention is engineered to be forward-compatible, capable of adapting to any future platforms or technologies that facilitate the sharing, distribution, or exchange of digital information in any format or medium.

[0004] Background of the Invention

[0005] In the rapidly evolving landscape of digital communication, social media platforms have become integral to the way individuals, organizations, and communities interact, share information, and express themselves. The advent of social media has given rise to a diverse array of content, ranging from informative articles to personal anecdotes, and from professional updates to entertaining multimedia. Thus, in the digital age, it is common for individuals and organizations to share large amounts of information through various digital platforms, such as social media but not limited to it, also through email, text messages, and instant messaging and various other digital platforms that allows sharing of information. However, one challenge that has persisted within these platforms is the issue of accurate user attribution for the content posted. Examples of the content that is shared are postings of text, images, audio clips, video clips and the like. So, with this increased sharing of information comes the challenge of determining the origin of the content and identifying the author or sender. This is particularly true for social media platforms, where the content is often shared, re-shared, and modified, making it difficult to trace the origin of the post. A social media account (or simply “account”) is an account on one or more social networking platforms (“social network”). An account can be associated with an individual (e.g., a natural person) or it can be associated with an organization (e.g., created by an employee of the organization) for broadcasting information about the organization or its products or services. Creating accounts on these social networks is relatively simple and users can create a “presence” on the social networks for themselves, their products, their brands and / or their initiatives. To present a consistent social identity across social networking platforms, individuals and organizations can create a social media account on each social networking platform using similar or identical names, pictures and descriptions. The individual or organization can then broadcast similar or identical content across multiple or even all of their social media accounts to reach the audiences or constituents on each social networking platform to which they are connected.

[0006] With the advent of social networking sites, it has become increasingly common for people to publicly share information. Social media networks can be characterized by large and evolving communities, with each user account being connected with multiple other users. Social networking sites typically allow a user to create and maintain their own unique user profile that contains personal information about themselves (e.g., their name, residence, hobbies, etc.). Once a user becomes a member of a social networking site, the user can communicate with other users of the social networking site (e.g., with a specific individual, with a group of individuals, or with all users of the social networking site). The connections are likely to be based on existing real world relationships, such as family members, friends, colleagues and classmates but can also represent a much larger community based on a shared interest. The users have control over the connections that are made but most of the time the number of connections is not restricted since the increase in the number of connections has particular advantages. When social media content is posted on a user account, that post will be available to the connections and / or networks selected by the user. Since the connections and networks are typically based on some existing real-world relationships or common interest, the post is likely to be received by a relatively large number of people, most of whom are important for the content creator. As a result, social media networks can be an effective platform for mass content distribution, which needs to be done responsibly, when considerations for potential impact on the audience are taken into account.

[0007] Currently a vast number of identities and identity systems exist to perform identifications within a variety of different contexts. Traditional paper or physical identities include state issued licenses, passports and the like, which are visually inspected by authorities when needed. Commercial identities include bank cards, credit cards, membership cards and the like which are used in order to enable transactions by magnetically scanning. The identity of the person using the card can be inspected at the time of scanning the card. Biometric identities include fingerprints, facial characteristics, iris patterns, DNA sequences and the like, which are optically scanned, chemically tested to enable access to systems and services. However there are also digital identities and these carry a different type of challenge for inspection. Digital identities include accounts, usernames and passwords of social media accounts wherein a server requests to enable access to content of the accounts. Since, multiple people may use the username and password of a particular social media account and since the connection is made through a computer, the digital identities bring in several complexities to the world of identities.

[0008] Current social media systems predominantly rely on a username or user handle associated with an account to represent the entity behind the posted content. While this provides a superficial identifier, it often falls short in offering a robust and transparent method for attributing individual actions and contributions within an account to the corresponding users. This limitation can lead to confusion, misrepresentation, and potentially deceptive practices, where the true originators of content remain obscured.

[0009] A further problem arises when there are multiple users of a particular social media account, that belongs for instance to a certain business. Thus, it is an even more complex issue when multiple users operate a single digital account. This is not limited to traditional organizational settings like businesses, non-profits, or educational institutions, but also extends to modern digital entities such as personal influencer brands, content creator collectives, and collaborative online communities. In these diverse settings, the challenges of attribution extend beyond individual posts to shared responsibilities for content creation, curation, and management. The layer of personal branding mixed with professional output in the case of influencers further complicates the tracing of content origin, as it blurs the lines between personal and commercial content. This multiplicity of users and content types raises concerns around data security, privacy, and poses difficulties in accountability, especially in instances of misinformation or unauthorized actions. Furthermore, the problem is exacerbated when these accounts are integrated across multiple platforms and services, making it increasingly difficult to manage, monitor, and attribute activity effectively. Since there is only one account managed by multiple users, it is not possible to track down and determine which content has been shared by which user. Actually, there is a need for tracking down and linking all kinds of activity on the account with the user who is responsible from that activity. Social media systems do not provide any analytical tools for providing information on the actual identity of the person who is using the digital identity. But this is important both for considerations on responsibility arising from individual actions and at the same time on considerations for the contributions and performance of individuals.

[0010] Computer implemented technologies assist users in communicating with other users over communication networks, in particular through social media. However, these existing technologies and others do not provide functionality for intelligently determining the identity of users that share or manage a single social media account. The problem of determining the origin of digital content has been addressed in the past through various methods, such as manual analysis of the content. However, these methods are often time-consuming and error-prone, and they do not provide a satisfactory solution.

[0011] Summary of the Invention

[0012] This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

[0013] Aspects of this disclosure relate to computerized systems for determining an identity of one or more users that share the same social media account in order to share content. Thus, aspects of the present invention encompass advanced computerized systems aimed at resolving the intricate challenges associated with determining the identity of one or more users sharing a single digital account. These accounts can span a wide array of platforms, including but not limited to social media networks, e- commerce sites, content management systems, and collaborative work platforms. People may communicate with each other using social media networks. In certain situations, multiple users share a single social media account such that any user shares their own content through the same account. Such sharing arrangements are common not only in traditional businesses and organizations but also among modern digital entities like personal influencer brands, content creator collectives, and various forms of online communities. For example, the single social media account of a business may be managed by 2 people wherein each user must post the content they create through the same account. In these multi-user scenarios, individuals may contribute diverse types of content, such as text posts, images, videos, multimedia stories, product listings, and even code snippets. Beyond merely identifying who posted what, existing technologies often fall short in several key areas. They lack the capability to differentiate between the performance metrics of different users managing the same account, such as engagement rates or conversion metrics. Moreover, they are ill-equipped to handle challenges related to data security, privacy, compliance with regulations, and accountability, especially when incorrect or misleading information is posted. Additionally, these technologies are generally not optimized for real-time monitoring or historical tracking, making it difficult to provide timely insights or retrospectively analyze user contributions. As described herein, existing technologies lack the functionality to not only adequately identify a user of the single social media account having multiple users, but these technologies also lack the functionality to differentiate between the performance of different users while managing the single account.

[0014] The present invention seeks to address these problems by introducing a novel system and method for authentic user attribution of social media posts (images, shorts, stories, reels and etc.) and in general all kinds of digital activity. Thus, the present invention introduces an innovative system and method that redefine the landscape of authentic user attribution across an expansive array of digital activities and platforms; not limited to social media actions like images, shorts, stories, and reels, the system also covers emails, e-commerce interactions, digital documents, and other forms of online communication and transactions. It is uniquely configured to handle a variety of scenarios, ranging from traditional organizational settings to modern digital entities like personal influencer brands, content creator collectives, and online communities. For example, in the context of a personal influencer who employs multiple virtual assistants (VAs), the system enables each VA to respond with a unique identifier, thus creating an organic and authentic interaction from the influencer's perspective. This application extends to e-commerce platforms where different team members may manage inventory, customer service, or marketing tasks, each with their unique identifiers for seamless yet distinct execution. By leveraging a multi-pronged approach that incorporates cutting-edge user authentication techniques, machine learning-based data analysis algorithms, cryptographic principles, and real-time analytics, the system provides a robust framework for accurately associating a wide range of digital actions — be it social media posts, comments, emails, e-commerce transactions, or nuanced interactions like shares and likes — with their rightful creators. The system is also designed for both real-time and historical attribution, meeting the dynamically evolving needs of modern digital ecosystems. The invention dramatically elevates the efficiency and accuracy of digital content attribution, effectively overcoming the limitations and inaccuracies inherent in previous methods such as manual analysis or rudimentary algorithms. Moreover, it assures the highest levels of data security and compliance with existing regulations, making it a comprehensive solution for the challenges of digital identity and attribution. Certain aspects of the present disclosure are directed to improving these technologies by determining an identity of one or more users that share a single social media account. By leveraging advanced user authentication techniques, data analysis algorithms, and cryptographic principles, the proposed system aims to establish a more accurate and reliable framework for associating specific digital actions, such as posts, comments, and interactions, with their rightful creators. Thus, the present invention provides a more efficient and accurate way to attribute digital content, and it overcomes the shortcomings of prior art methods, such as manual analysis. The system of the present invention enables organizations and individuals to trace the origin of information quickly and easily. Additionally, it allows for the tracking and analysis of user posts, user performance, store performance and social media account performance, providing valuable insights and metrics for business and marketing purposes. The present invention, elaborated in greater detail herein, provides a comprehensive overview of the system's multifaceted architecture, underscored by its diverse technological features, wide-ranging functionalities, and extensive potential applications across various digital platforms. While the system's cornerstone objective remains enhancing transparency, trustworthiness, and accountability in the domain of social media interactions, its ambitions extend far beyond this. By establishing a robust and direct link between user activities and the individuals responsible for those actions, the system transcends traditional challenges to enable a host of advanced functionalities. These include real-time tracking of user behaviors, performance analytics across multiple metrics, and assessing the uniqueness of content and interactions, thereby facilitating a nuanced understanding of user engagement and impact. Furthermore, the invention serves as a powerful tool for content validation, digital identity verification, and fostering a new level of user-driven authenticity in the digital landscape. Designed to be adaptable, the system is equipped to handle not just social media but also emails, e-commerce interactions, and other forms of digital communication and transaction. This makes it an invaluable asset for a wide range of users, from organizations and businesses to personal influencers and individual users, each of whom may have different needs for tracking, analytics, and identity verification.

[0015] Another object intended with the present invention is to determine the performance of different individuals who are using the same account, by attributing the specific activity on the account to the specific person who is responsible from all the actions in relation to that activity.

[0016] In order to meet the intended objects, the present invention provides a system and method for automatically attributing digital content, which effectively overcomes the shortcomings and limitations of existing technologies. Utilizing a blend of sophisticated Al algorithms, natural language processing, and machine learning techniques, the system of the present invention offers nuanced content analysis to accurately identify the origin of an array of digital activities. The system equips users with a broad suite of attribution identifiers, each enhanced by Al functionalities for maximum precision and security. In addition to traditional identifiers like emojis, symbols, letters, tags, or words, the system supports advanced options such as digital signatures, hashtags, mentions, multimedia elements, QR codes, barcodes, timestamps, geolocation tags, contextual keywords, and custom tokens. Hash data is also incorporated as an Al- augmented identifier, adding an extra layer of data integrity and verification to the attribution process. This is supplemented by other technical data identifiers like IP address, device ID, location IP, contact information, and user ID. Al-powered features like predictive user matching, sentiment analysis, and anomaly detection further refine the identification process. Additionally, Al algorithms bolster real-time tracking of user behaviors, performance analytics, and even offer predictive analytics to forecast future user actions. This expansive set of Al-enhanced identifiers and functionalities ensures unparalleled accuracy, robust security, and contextually relevant attribution. The system is versatile, catering to a wide range of user needs — from organizations and businesses to personal influencers and individual users — across various digital platforms and scenarios.

[0017] The system and method encompassed by the present invention are devised to facilitate the automatic analysis and attribution of an expansive range of digital content, encompassing but not limited to social media posts, e-commerce product descriptions, emails, and multimedia content. Leveraging natural language processing (NLP) and machine learning algorithms, the system not only analyzes content based on a multitude of attributes like sentiment, keywords, and engagement metrics but also seamlessly extracts valuable data for analytics and decision-making purposes. Designed to be platform-agnostic, the system is pre-integrated with a wide array of e- commerce platforms such as WooCommerce, Shopify, and BigCommerce, thereby offering comprehensive insights that span both social media and online retail ecosystems. Furthermore, the system is forward-compatible, built to adapt to emerging platforms and evolving communication mediums.

[0018] The present invention is useful for businesses, individuals, and organizations that rely on accurate attribution and analytics of digital content, whether it's for legal, research or marketing purposes. It is also useful for those who need to verify the authenticity of digital content and the authorship of the content. Thus, present invention holds significant utility for a diverse range of entities and applications, far exceeding conventional boundaries. While businesses, individuals, and traditional organizations will find immense value in its capabilities for accurate attribution and analytics of digital content, its applicability also extends to modern digital entities like personal influencer brands, content creator collectives, online communities, and even governmental agencies. The system is designed to serve various purposes, from legal compliance and intellectual property management to academic research, marketing optimization, public relations, and crisis management. It is particularly beneficial in scenarios that require meticulous tracking and auditing, such as regulatory reporting or forensic investigations. Moreover, the invention is versatile enough to adapt to a multitude of digital platforms — social media networks, e-commerce sites, email services, collaborative workspaces, educational platforms, and many more. Given its wide- ranging functionalities and Al-enhanced features, the system is not merely a tool but a comprehensive solution for anyone invested in the responsible and effective management of digital content across multiple facets of human interaction and enterprise.

[0019] Detailed Description of the Invention

[0020] The present invention relates to a system and method for automatically analyzing and attributing digital content across both social media and e-commerce platforms. The system utilizes natural language processing, machine learning, Deep Learning, ensemble learning, time series analysis, clustering algorithms, transfer learning and reinforcement learning techniques to extract useful data for analytics purposes, such as tracking social media insights and user behavior analysis. Additionally, the system allows for easy attribution of the content to its origin, making it useful for legal, research or marketing purposes.

[0021] The present invention may be a system, a method, and / or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.

[0022] The programs described herein are identified based upon the application for which they are implemented in a specific embodiment of the invention. However, it should be appreciated that any particular program nomenclature herein is used merely for convenience, and thus the invention should not be limited to use solely in any specific application identified and / or implied by such nomenclature.

[0023] The system comprises several key components: An interface for receiving digital content, such as social media posts, emails, text messages, documents and e-commerce product descriptions. Social media contents can include any posting distributed to a social media account within a social media network. The posting can include various types of data, including text, graphics, videos.

[0024] Uniform Resource Locators (URLs): The system is capable of handling various types of data, including but not limited to Uniform Resource Locators (URLs), and can even process combinations of different data types for more advanced analytics.

[0025] An Analyzing Module: This component is engineered with cutting-edge natural language processing and machine learning techniques. While the base functionalities include the extraction of relevant data using words, tags, letters, symbols, and emojis, it goes beyond the basics to support:

[0026] Digital Signatures

[0027] Hashtags

[0028] Mentions

[0029] Multimedia Elements

[0030] QR Codes

[0031] Barcodes

[0032] Timestamps

[0033] Geolocation Tags

[0034] Contextual Keywords

[0035] Custom Tokens

[0036] Additional Features in Analyzing Module:

[0037] Al-Augmented Identifier: Incorporates hash data to ensure data integrity and verification.

[0038] Technical Identifiers: Utilizes IP address, device ID, location IP, contact information, and user ID for a more exhaustive analysis. Al-Powered Analytics: Employs predictive user matching, sentiment analysis, and anomaly detection to refine data extraction and determine the content's origin with high accuracy.

[0039] An Attribution Module: Beyond allowing users to select simple attributes like emojis, symbols, letters, or words for identification, this module offers a more comprehensive range of identifiers. It's designed to read the caption and determine who shared the content, thereby providing efficient and accurate attribution. The module is also enriched with Al-powered features for predictive user matching, sentiment analysis, and anomaly detection, ensuring unparalleled accuracy in attributing digital content.

[0040] A data storage module, storing the extracted data including insights on user posts, user performance, store performance, and social media account performance and which allows the tracking and analysis of user posts, user performance, store performance, and social media account performance.

[0041] E-Commerce Integration, supporting platforms such as WooCommerce, Shopify, custom development, BigCommerce, Magento, Wix, Webflow, and adaptability to future platorms, enabling cross-platform analysis and insights.

[0042] In another embodiment of the present invention the system comprises,

[0043] The system features an interactive interface designed for ingesting a comprehensive array of digital content. This includes, but is not limited to, social media postings, emails, text messages, multimedia documents, and e-commerce-related material. Within the realm of social media, the system is capable of processing various types of postings distributed across multiple social media accounts and platforms. These postings may encompass text, graphics, videos, Uniform Resource Locators (URLs), emojis, and tags, either individually or in any combination thereof. For e-commerce platforms, the system is proficient in parsing and analyzing a multitude of content types. This extends beyond simple product descriptions to include meta data, description tags, product names, SKU (Stock Keeping Unit) identifiers, tags, product collections, categories, slugs, customer reviews, and pricing information. The system is also capable of handling specialized e-commerce attributes such as promotional codes, inventory levels, and associated multimedia content like product images and videos. Additionally, it can process structured data formats like JSON-LD used for SEO (Search Engine Optimization) purposes. The analyzing module uses natural language processing techniques to extract relevant data from the digital content, such as keywords, hashtags, and mentions, and uses them to determine the origin of the post. Additionally, it uses machine learning techniques to analyze user behavior, store performance, and social media account performance. In addition to machine learning, the analyzing module uses, deep learning, ensemble learning, time series analysis, clustering algorithms, transfer learning and reinforcement learning. The data storage module stores extracted data and allows for tracking and analysis, providing valuable insights and metrics for business and marketing purposes.

[0044] The system integrates an advanced and highly configurable attribution module. This module is built on a multi-layered architecture that leverages cutting-edge machine learning algorithms, natural language processing, and pattern recognition technologies. It allows users to select from an extensive range of attributes for pinpointed attribution. These attributes go beyond basic elements like emojis, symbols, letters, and words, and extend to intricate data forms such as QR codes, barcodes, geolocation tags, timestamp data, unique identifiers like Blockchain hashes, metadata, alt-text, and embedded codes. Furthermore, it supports multimedia elements like images and audio snippets for even more comprehensive attribution. The module is designed to be context-aware and is capable of understanding the sentiment and semantic relevance of the chosen attributes, thereby enhancing the accuracy and reliability of the attribution process. It also incorporates an Al-powered recommendation engine that suggests optimal attributes for attribution based on historical data and predictive analytics. For scalability, the module offers both batch and real-time processing modes, allowing for mass attributions across multiple platforms and types of content, as well as real-time attributions for time-sensitive scenarios. An accompanying dashboard provides a suite of analytics tools, furnishing users with in-depth insights into attribution success rates, historical trends, and predictive forecasts. The dashboard can also generate automated reports and alerts, streamlining the monitoring and decision-making processes. To augment user convenience, the module features an intuitive drag-and-drop interface and offers API integration capabilities for seamless interoperability with third-party platforms and services. This feature offers dual utility. First, it enables more efficient and accurate attribution of digital content by allowing users to specify the criteria based on which attribution should occur. Second, it affords organizations and individuals the agility to trace the origin of diverse types of information swiftly, thereby facilitating real-time decision-making and content management. Additionally, the module supports batch processing, allowing users to perform mass attributions across multiple platforms and content types simultaneously. It also features a user-friendly dashboard that provides real-time analytics and tracking capabilities, offering insights into attribution success rates, trends, and potential areas of improvement.”

[0045] The invention is a novel and non-obvious solution that combines content attribution and analytics in one platform, it uses natural language processing and machine learning machine learning, deep Learning, ensemble learning, time series analysis, clustering algorithms, transfer learning and reinforcement learning to analyze digital content, providing data-driven insights on user behavior, store performance, and social media account performance. It also allows for easy attribution of the content to its origin, making it useful for legal, research or marketing purposes.

[0046] The described embodiments relate to methods and systems for managing social media account performance and in particular, determining the activity of the actual content creator who is responsible from that activity in a single account with multiple users who are the content creators.

[0047] The invention's core algorithm integrates natural language processing (NLP) and machine learning (ML) techniques, performing multidimensional analysis and attribution across both social media and e-commerce platforms: The algorithm of the present invention is described in detail below:

[0048] Attribute Identification: Identifies key elements in digital content.

[0049] Natural Language Processing (NLP): Content Parsing and Semantic Analysis.

[0050] Machine Learning (ML) Analysis: Behavior Analysis, Pattern Recognition.

[0051] Cross-Platform Integration: Seamless integration with various platforms.

[0052] Real-Time Analytics: Processing large volumes of data in real time.

[0053] Error Detection and Correction: Identifying and correcting discrepancies.

[0054] Al-Driven Analysis of Users, Accounts, Products, Orders, and Customers: User Behavior Analysis: Personalized insights, segmentation, and targeting. Account Performance Monitoring: Real-time monitoring, predictive analytics.

[0055] Product Analysis and Optimization: Performance tracking, inventory management.

[0056] Order Processing and Fulfillment: Automated order analysis, fraud detection.

[0057] Customer Relationship Management (CRM): Customer segmentation, churn prediction.

[0058] Multi-Language Support and Global Applicability: Language Detection and Translation: Automatic detection, dynamic translation.

[0059] Culturally Aware Analysis: Cultural sensitivity, localized insights.

[0060] Language-Specific NLP: Language-specific models, adaptation to linguistic changes.

[0061] User-Centered Design: Language preferences, accessibility for non-English speakers.

[0062] The system of the present invention comprises several components:

[0063] An interface for receiving digital content, such as social media posts, emails, text messages, documents, and e-commerce product descriptions.

[0064] An Analyzing Module: This component leverages advanced natural language processing and machine learning techniques to extract relevant data. While it can process basic elements like words, tags, letters, symbols, and emojis, its capabilities are far more expansive. It supports:

[0065] Digital Signatures

[0066] Hashtags

[0067] Mentions

[0068] Multimedia Elements

[0069] QR Codes

[0070] Barcodes

[0071] Timestamps

[0072] Geolocation Tags

[0073] Contextual Keywords Custom Tokens

[0074] Additional Features in Analyzing Module:

[0075] Al-Augmented Identifier: Incorporates hash data for bolstered data integrity and verification.

[0076] Technical Identifiers: Utilizes IP address, device ID, location IP, contact information, and user ID for a more nuanced analysis.

[0077] Al-Powered Analytics: Employs predictive user matching, sentiment analysis, and anomaly detection to refine the data extraction and origin determination process.

[0078] An Attribution Module: This module allows users to select an attribute — be it an emoji, symbol, letter, or word — for identification purposes. However, it doesn't stop there. It also supports advanced identifiers and leverages Al algorithms for predictive user matching, sentiment analysis, and anomaly detection. This ensures unparalleled accuracy and contextually relevant attribution.

[0079] A Data Storage Module: This is where the magic happens. Not only does it store the extracted data, but it also categorizes and archives various insights on:

[0080] User posts

[0081] User performance

[0082] Store performance

[0083] Social media account performance

[0084] The storage is designed for high data integrity and is capable of holding advanced identifiers and analytics data, making it a robust solution for tracking and performance analysis.

[0085] E-Commerce Integration, supporting platforms such as WooCommerce, Shopify, custom development, BigCommerce, Magento, Wix, Webflow, and adaptability to future platforms, enabling cross-platfor Use Case: Mavigadget's Multi-Attribute Analysis Across Social Media and E-Commerce Platforms.

[0086] One example method includes receiving from an entity an indication of interfaces that are used to share content, registering the user interfaces including associating the user interfaces with the entity, tracking user interaction with the interfaces including attributing user interaction by a user in any of the interfaces as an interaction with the entity, and using the tracking information to determine all actions associated with a given user that are to be attributed to the entity. An indication can be received from the entity of one or more types of user interaction to track. The systems discussed here may provide one or more mechanisms for collecting personal information about users. Users may be provided with an opportunity to opt in / out of programs that may collect personalized information. In addition, certain data may be anonymized in one or more ways before it is stored or used, so that personally identifiable data is removed.

[0087] Use Case: Mavigadget's Multi-Attribute Analysis Across Social Media and E- Commerce Platforms

[0088] E-Commerce Integration:

[0089] Supported Platforms: The system is engineered to be highly adaptable, designed to integrate seamlessly with a variety of e-commerce platforms. This includes, but is not limited to:

[0090] WooCommerce

[0091] Shopify

[0092] Custom Development

[0093] BigCommerce

[0094] Magento

[0095] Wix

[0096] Webflow

[0097] Additionally, the architecture is future-proof, allowing for effortless adaptability to any new e-commerce platforms that may emerge over time. This is critical for staying ahead in a rapidly evolving digital landscape.

[0098] Product Attribution Across Platforms: One of the core functionalities of the system is its ability to correlate specific identifiers with product performance across various e- commerce platforms. While this includes basic elements like words, tags, letters, symbols, and emojis, the system goes a step further by supporting advanced identifiers such as: Digital Signatures

[0099] Hashtags

[0100] Mentions

[0101] Multimedia Elements

[0102] QR Codes

[0103] Barcodes

[0104] Timestamps

[0105] Geolocation Tags

[0106] Contextual Keywords

[0107] Custom Tokens

[0108] Additional Features in Product Attribution:

[0109] Al-Augmented Identifier: Hash data is used for an extra layer of data integrity and verification in the attribution process.

[0110] Technical Identifiers: IP address, device ID, location IP, contact information, and user ID are also used for enhanced accuracy.

[0111] Al-Powered Analytics: Features like predictive user matching, sentiment analysis, and anomaly detection are employed to offer a nuanced understanding of how attributes in product descriptions, reviews, and social media posts influence sales.

[0112] By leveraging this expansive set of identifiers and analytics features, the system provides invaluable insights to businesses like Mavigadget. This enables them to refine their marketing strategies and optimize sales activities with unprecedented accuracy and context-relevance.

[0113] Cross-Platform Analysis: The system provides insights that span across different e- commerce platforms, allowing Mavigadget to compare performance and identify best practices.

[0114] Error Detection: It identifies discrepancies or mistakes in product information shared on these e-commerce platforms using specific attributes, enabling quick corrections. Sales Correlation: The system correlates sales data with social media posts containing specific attributes, providing insights into how social media activities influence e- commerce sales across various platforms.

[0115] Adaptability to Future Platforms: The system is designed with flexibility to adapt and integrate with new e-commerce platforms as they emerge, ensuring that Mavigadget can continue to leverage the system's capabilities as the e-commerce landscape evolves.

[0116] Outcome:

[0117] Mavigadget leverages these insights across a wide range of social media and e- commerce platforms, refining its marketing strategy, improving user performance, and optimizing sales activities. The ability to analyze multiple attributes and integrate with various e-commerce platforms — including WooCommerce, Shopify, custom development, BigCommerce, Magento, Wix, Webflow, and potential future platforms — provides Mavigadget with a comprehensive and adaptable solution to navigate the complex digital landscape.

[0118] ### Data:

[0119] Example caption from a social media account:

[0120] “ Check out our new product line at Mavigadget store! Product:”Multifuncational Kitchen Tool" #newproduct #shopping #XYZstore”

[0121] Attribution Data:

[0122] Identifier: “ <? ”

[0123] Origin: "Mavigadget"

[0124] Shared by: "John Doe" (assuming that John Doe is the user who shared the post on social media)

[0125] User Data:

[0126] User ID: 773 (unique identifier assigned to the user who shared the post)

[0127] User Name: "John Doe"

[0128] User Location: "Chicago, IL" (if the user's location is available) Number of posts shared by the user: 12 (number of posts shared by the user in total)

[0129] Number of posts shared by user: “123”

[0130] Number of total likes by the user: “123”

[0131] Number of total comment by the user: “123”

[0132] Number of total saves by the user: “123”

[0133] Number of total views by the user: “123”

[0134] Number of total impressions by the user: “123”

[0135] Store Data:

[0136] Store ID: 789012 (unique identifier assigned to the store)

[0137] Store Name: "Mavigadget"

[0138] Store Location: "Chicago, IL" (location of the store)

[0139] Number of posts shared by the store: 50 (number of posts shared by the store in total)

[0140] Number of products in the store: 1000 (number of products available in the store)

[0141] Product Name: “Multifuncational Kitchen Tool” :(product name)

[0142] Total number of the products Sold: “55”:

[0143] Total Revenue: “$10222”

[0144] Collections: “Kitchen, gadgets”

[0145] Social Media Data:

[0146] Social Media Platform: "Instagram" (assuming that this post was shared on Instagram)

[0147] Identifier: Emoji “ "

[0148] Post ID: 12345678 (unique identifier assigned to the post) Number of likes: 20 (number of likes on the post)

[0149] Number of shares: 10 (number of shares on the post)

[0150] Number of comments: “404”

[0151] Number of saves: “22”

[0152] Number of impressions: “10203442”

[0153] Number of hashtags “12”

[0154] Hashtags: "#newproduct #mavigadget”

[0155] Implementations of the various techniques described herein may be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in combinations of them. Implementations may be implemented as a computer program product, i.e., a computer program tangibly embodied in an information carrier, e.g., in a machine-readable storage device (computer-readable medium) or in a propagated signal, for processing by, or to control the operation of, data processing apparatus, e.g., a programmable processor, a computer, or multiple computers. A computer program, such as the computer program(s) described above, can be written in any form of programming language, including compiled or interpreted languages, and can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program can be deployed to be processed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network.

[0156] Implementations of the various techniques described herein may also be implemented in a mobile application.

[0157] Method steps may be performed by one or more programmable processors executing a computer program to perform functions by operating on input data and generating output. Method steps also may be performed by, and an apparatus may be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit).

[0158] Information carriers suitable for embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD ROM disks.

[0159] To provide for interaction with a user, implementations may be implemented on a computer having a display device, e.g., a cathode ray tube (CRT) or liquid crystal display (LCD) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0160] Implementations may be implemented in a computing system that includes a back-end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front-end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation, or any combination of such back-end, middleware, or frontend components. Components may be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (LAN) and a wide area network (WAN), e.g., the Internet.

[0161] While certain features of the described implementations have been illustrated as described herein, many modifications, substitutions, changes and equivalents will now occur to those skilled in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the scope of the embodiments. It should be understood that they have been presented by way of example only, not limitation, and various changes in form and details may be made. Any portion of the apparatus and / or methods described herein may be combined in any combination, except mutually exclusive combinations. The embodiments described herein can include various combinations and / or sub-combinations of the functions, components and / or features of the different embodiments described.

[0162] Various embodiments according to the present invention are explained below: A system for automatically analyzing and attributing digital content comprises several key components:

[0163] 1. ^Interface for Receiving Digital Content**: This is the initial point where digital content is collected, be it social media posts, emails, text messages, or any other form of digital communication.

[0164] 2. **Analyzing Module**: Utilizes both natural language processing (NLP) and machine learning techniques to extract pertinent data from the digital content. This module is capable of determining the origin of a post, analyzing sentiment, predicting user behavior, and detecting anomalies.

[0165] 3. **Attribution Module**: Allows a user to pick an identifier for the application to read the caption and determine who shared the post. While basic identifiers such as emojis, symbols, letters, or words are supported, the system also accommodates advanced attributes. These include but are not limited to:

[0166] - Digital signatures

[0167] - Hashtags

[0168] - Mentions

[0169] - Multimedia elements

[0170] - QR codes

[0171] - Barcodes

[0172] - Timestamps

[0173] - Geolocation tags

[0174] - Contextual keywords

[0175] - Custom tokens

[0176] **Additional Features**:

[0177] - **Hash Data**: Incorporated as an Al-augmented identifier to add an extra layer of data integrity and verification to the attribution process. - **Technical Data Identifiers**: Additional identifiers like IP address, device ID, location IP, contact information, and user ID supplement the basic identifiers for enhanced accuracy.

[0178] - **AI-Powered Functionalities**: Features such as predictive user matching, sentiment analysis, and anomaly detection further refine the identification process.

[0179] - **Real-Time Tracking and Analytics**: Al algorithms facilitate real-time tracking of user behaviors and performance metrics, and also offer predictive analytics to forecast future actions.

[0180] This integrated system offers unparalleled accuracy, robust security, and contextually relevant attribution. It is versatile, designed to meet a wide array of user needs across different digital platforms, from organizations and businesses to personal influencers and individual users." a data storage module storing the extracted data; a data analyzing module, analyzing the stored data to track user posts, user performance, store performance, and social media account performance; analyzing the stored data to track user posts, user performance, store performance, and social media account performance, providing valuable insights and metrics for business and marketing purposes.

[0181] Another embodiment according to the present invention relates to a method for managing access to a user account from a variety of electronic devices that belong to different individuals. This method encompasses the following steps:

[0182] Providing, by an identity service, a device token for the set of electronic devices that belong to a specific individual user. This token is a unique identifier that securely associates devices with a user.

[0183] Allowing the individual user to select an attribute as a supplementary identifier. Beyond traditional options like emojis, symbols, letters, or words, the system supports advanced attribute options, including but not limited to digital signatures, hashtags, mentions, multimedia elements, QR codes, barcodes, timestamps, geolocation tags, contextual keywords, and custom tokens. Associating the selected attribute with the individual user's set of electronic devices, further enhancing the accuracy and security of the identity verification process. Hash data, supplemented by technical data identifiers such as IP address, device ID, location IP, contact information, and user ID, is also used as an Al-augmented identifier for added data integrity and verification.

[0184] Transmitting, by the identity service, an identity certificate that includes an association between the selected attributes and the electronic devices of a specific user. This certificate serves to determine the activity of an individual on the user account securely and accurately.

[0185] Additional Features:

[0186] The system uses Al-powered functionalities like predictive user matching, sentiment analysis, and anomaly detection to refine the identification process.

[0187] Real-time tracking of user behaviors and performance analytics are facilitated by Al algorithms, which can also offer predictive insights into future user activities.

[0188] This comprehensive approach ensures unparalleled accuracy, robust security, and contextually relevant attribution. It is versatile enough to cater to a wide range of user needs, including organizations, businesses, personal influencers, and individual users, across various digital platforms and scenarios. A preferred embodiment according to the present invention is; a method for managing a social media account wherein the social media account is used by a plurality of content creators from various electronic devices, comprising; allowing the content creators to select a different attribute to represent their online identity, associating the selected attribute with the content creator, extracting the relevant data from the digital content shared from the social media account, analyzing the relevant data in order to at least determine the creator of the digital content based on the attribute included in the digital content.

[0189] In another embodiment of the present invention, said method, further comprises providing an interface for receiving digital content to be shared through the social media account and, allowing a plurality of content creators to use the same interface for sharing digital content through the social media account.

[0190] In a further embodiment, the attribute identifying the content creator is included in the post as text, each time while sharing the digital content.

[0191] According to a different embodiment, the attribute identifying the content creator is automatically included in the digital content.

[0192] In a different embodiment, the attribute to be selected by each content creator is an emoji, symbol, letter or word. In addition to traditional identifiers like emojis, symbols, letters, tags, or words, the system supports advanced options such as digital signatures, hashtags, mentions, multimedia elements, QR codes, barcodes, timestamps, geolocation tags, contextual keywords, and custom tokens. Hash data is also incorporated as an Al-augmented identifier, adding an extra layer of data integrity and verification to the attribution process. This is supplemented by other technical data identifiers like IP address, device ID, location IP, contact information, and user ID. Al- powered features like predictive user matching, sentiment analysis, and anomaly detection further refine the identification process. Additionally, Al algorithms bolster real-time tracking of user behaviors, performance analytics, and even offer predictive analytics to forecast future user actions. This expansive set of Al-enhanced identifiers and functionalities ensures unparalleled accuracy, robust security, and contextually relevant attribution. The system is versatile, catering to a wide range of user needs — from organizations and businesses to personal influencers and individual users — across various digital platforms and scenarios.

[0193] According to a different embodiment the attribute selected by the content creator while sharing the digital content is verified from the electronic device associated with the content creator prior to sharing the digital content.

[0194] In an embodiment the digital content is social media posts, emails, text messages or documents.

[0195] In a preferred embodiment of the present invention, digital content is shared across social media and e-commerce platforms that exist currently and any future platform that allows sharing of information of any kind. In another embodiment the new user of an electronic device is notified in order to change the attribute selected by the prior content creator.

[0196] A different embodiment of the present invention relates to a system for managing a social media account wherein the same account is used by a plurality of content creators, comprising: an attribution module configured to allow a content creator to select an attribute so that the application can read the caption and determine who shared the post; an analyzing module configured to extract relevant data from the digital content, a data storage module configured to store the extracted data and a data analyzing module configured to analyze the stored data.

[0197] According to a preferred embodiment the system further comprises, an interface configured to receive digital content to be shared through the social media account, and to be used by a plurality of content creators for sharing digital content.

[0198] According to one embodiment, when a content creator logs into the system, the attribute associated with this creator is locked in the system until the digital content is shared. This ensures that the content is accurately attributed to the correct creator. In a further embodiment, the attribute to be selected by the content creator goes beyond traditional choices like emojis, symbols, letters, or words. The system also allows for advanced attribute options, including but not limited to digital signatures, hashtags, mentions, multimedia elements, QR codes, barcodes, timestamps, geolocation tags, contextual keywords, and custom tokens.

[0199] The system also leverages Al-powered features like predictive user matching, sentiment analysis, and anomaly detection to further refine the identification process. In addition, Al algorithms are used for real-time tracking of user behaviors, performance analytics, and even offer predictive analytics to forecast future user actions. This expansive set of Al-enhanced identifiers and functionalities ensures unparalleled accuracy, robust security, and contextually relevant attribution. The system is versatile and is designed to cater to a wide range of user needs — from organizations and businesses to personal influencers and individual users — across various digital platforms and scenarios Hash data is included as an Al-enhanced identifier, providing an additional layer of data integrity and verification. This is further supported by other technical data identifiers such as IP address, device ID, location IP, contact information, and user ID. Moreover, the system employs Al-powered functionalities like predictive user matching, sentiment analysis, and anomaly detection to refine the identification and attribution process. Real-time tracking of user behaviors and performance analytics are also facilitated by Al algorithms, which can even offer predictive insights into future user actions.

[0200] This comprehensive suite of identifiers and Al-powered features ensures that the system provides unparalleled accuracy, robust security, and contextually relevant attribution. It is designed to be versatile, catering to a wide array of user needs, from organizations and businesses to personal influencers and individual users, across various digital platforms and scenarios."

[0201] By adding these details, you'll give a more complete picture of the system's capabilities and flexibility, particularly in terms of the advanced identifiers it can use and the Al functionalities that enhance its performance.

[0202] In another embodiment, the data analyzing module is configured to analyze the stored data in order to at least determine the origin of digital content by tracking the attribute included in the digital content.

[0203] In a different embodiment, the data analyzing module is configured to analyze the stored data in order to determine the attribute associated with the digital content, track the content creators’ posts, content creator’s performance and social media account performance.

[0204] An embodiment of the present invention relates to use of the system, for providing valuable insights and metrics for business and marketing purposes.

[0205] In a further embodiment, the social media account is used by a plurality of content creators from more than one electronic device. Especially in this embodiment the method of the present invention comprises one or more advanced options selected from the list comprising digital signatures, hashtags, mentions, multimedia elements, QR codes, barcodes, timestamps, geolocation tags, contextual keywords and custom tokens. Hash data is incorporated as an Al-augmented identifier to add an extra layer of data integrity and verification to the attribution process. This is supplemented by other technical data identifiers like IP address, device ID, location IP, contact information, and user ID.

[0206] The present invention is a comprehensive system and method for automatically analyzing and attributing digital content, such as social media posts, across various social media and e-commerce platforms. The system allows for the tracking and analysis of user posts, user performance, store performance, and social media account performance, providing valuable insights and metrics for business and marketing purposes. The invention as described throughout this specification is a significant improvement over the prior art.

[0207] A number of embodiments have been described. Nevertheless, it will be understood that various modifications can be made without departing from the spirit and scope of the processes and techniques described herein. In addition, the logic flows depicted in the figures do not require the particular order shown, or sequential order, to achieve desirable results. In addition, other steps can be provided, or steps can be eliminated, from the described flows, and other components can be added to, or removed from, the described systems. Accordingly, other embodiments are within the scope of the following claims.

[0208] In an embodiment, a system for managing a social media account utilized by a plurality of content creators, comprises the following modules: an attribution module configured to allow a content creator to select an attribute in order to enable identification of the source of shared digital content within the social media platform, a data extraction module configured to extract relevant data including metadata and attribute from the digital content of content creators, wherein the module employs advanced algorithms to assess various metrics associated with the digital content, thereby automating the content extracting process, a data storage module configured to securely store the extracted relevant data, facilitating efficient data retrieval and management, wherein the module is optimized to ensure data integrity while providing fast access and secure storage capabilities, a data analysis module configured to analyze the stored relevant data using algorithms, wherein the module enables the use of natural language processing techniques to read and interpret captions associated with posts, thereby accurately determining the specific creator responsible for sharing each piece of content.

[0209] In a further embodiment, the data extraction module further comprises an interface configured to receive digital content to be shared through the social media account and to be used by a plurality of content creators for sharing digital content.

[0210] In a further embodiment, the attribute to be selected by the content creator is an emoji, symbol, letter or word.

[0211] In a further embodiment, the data analysis module is configured to analyze the stored data to determine at least the origin of digital content by tracking the attributes included in the digital content.

[0212] In a further embodiment, the data analysis module is configured to analyze the stored data to determine the attribute associated with the digital content, track the content creators’ posts, content creator’s performance and social media account performance.

[0213] In a further embodiment, the system comprises one or more Al-powered features selected from the list comprising predictive user matching, sentiment analysis and anomaly detection to further refine the identification process.

[0214] In a further embodiment, the Al-powered features are used for real-time tracking of user behaviors, performance analytics, and even offer predictive analytics to forecast future user actions.

[0215] In a different embodiment, a computer implemented method performed by a system is disclosed for managing a social media account utilized by a plurality of content creators across one or more electronic devices.

[0216] In a further embodiment, a computer implemented method comprises:

[0217] - allowing content creators to select a unique attribute to represent their online identity, wherein the selected attribute is associated with the respective content creator,

[0218] - extracting relevant data from digital content shared through a social media account, - analyzing the extracted data to determine at least the creator of the digital content based on the included attribute.

[0219] In a further embodiment, a computer implemented method comprises

[0220] - providing an interface for receiving digital content to be shared through the social media account, and

[0221] - allowing a plurality of content creators to utilize the same interface for sharing digital content through the social media account.

[0222] In a further embodiment, a computer implemented method comprises

[0223] - automatically including the identifying attribute in the digital content when shared,

[0224] - enabling the attribute to be an emoji, symbol, letter or word and / or incorporating advanced options such as digital signatures, hashtags, mentions, multimedia elements, QR codes, barcodes, timestamps, geolocation tags, contextual keywords, and custom tokens.

[0225] In a further embodiment, a computer implemented method further comprises

[0226] - verifying the selected attribute from the electronic device associated with the content creator prior to sharing the digital content.

[0227] In a further embodiment, a computer implemented method further comprises

[0228] - allowing the shared digital content to encompass social media posts, emails, text messages, or documents, and enabling sharing across current and future social media and e-commerce platforms.

[0229] In a further embodiment, a computer implemented method further comprises

[0230] - notifying a new user of the electronic device to change the attribute selected by a previous content creator.

[0231] In an embodiment, use of a system is disclosed for providing valuable insights and metrics for business and marketing purposes.

Claims

Claims1 . A system for managing a social media account utilized by a plurality of content creators, comprising: an attribution module configured to allow a content creator to select an attribute in order to enable identification of the source of shared digital content within the social media platform, a data extraction module configured to extract relevant data including metadata and attribute from the digital content of content creators, wherein the module employs advanced algorithms to assess various metrics associated with the digital content, thereby automating the content extracting process, a data storage module configured to securely store the extracted relevant data, facilitating efficient data retrieval and management, wherein the module is optimized to ensure data integrity while providing fast access and secure storage capabilities, a data analysis module configured to analyze the stored relevant data using algorithms, wherein the module enables the use of natural language processing techniques to read and interpret captions associated with posts, thereby accurately determining the specific creator responsible for sharing each piece of content.

2. A system for managing a social media account utilized by a plurality of content creators according to claim 1 , wherein the data extraction module further comprises an interface configured to receive digital content to be shared through the social media account and to be used by a plurality of content creators for sharing digital content.

3. A system for managing a social media account utilized by a plurality of content creators according to claim 1 or 2, wherein the attribute to be selected by the content creator is an emoji, symbol, letter or word.

4. A system for managing a social media account utilized by a plurality of content creators according to any one of preceding claims, wherein the data analysis module is configured to analyze the stored data to determine at least the origin of digital content by tracking the attributes included in the digital content.

5. A system for managing a social media account utilized by a plurality of content creators according to any one of preceding claims, wherein the data analysis moduleis configured to analyze the stored data to determine the attribute associated with the digital content, track the content creators’ posts, content creator’s performance and social media account performance.

6. A system for managing a social media account utilized by a plurality of content creators according to any one of preceding claims, wherein it comprises one or more Al-powered features selected from the list comprising predictive user matching, sentiment analysis and anomaly detection to further refine the identification process.

7. A system for managing a social media account utilized by a plurality of content creators according to claim 6, wherein the Al-powered features are used for real-time tracking of user behaviors, performance analytics, and even offer predictive analytics to forecast future user actions.

8. A computer implemented method performed by a system according to any claims 1 to 7, for managing a social media account utilized by a plurality of content creators across one or more electronic devices.

9. A computer implemented method according to claim 8, comprising:- allowing content creators to select a unique attribute to represent their online identity, wherein the selected attribute is associated with the respective content creator,- extracting relevant data from digital content shared through a social media account,- analyzing the extracted data to determine at least the creator of the digital content based on the included attribute.

10. A computer implemented method according to claim 9 further comprising,- providing an interface for receiving digital content to be shared through the social media account, and- allowing a plurality of content creators to utilize the same interface for sharing digital content through the social media account.1 1 . A computer implemented method according to claims 9 to 10, comprising- automatically including the identifying attribute in the digital content when shared,- enabling the attribute to be an emoji, symbol, letter or word and / or incorporating advanced options such as digital signatures, hashtags, mentions, multimediaelements, QR codes, barcodes, timestamps, geolocation tags, contextual keywords, and custom tokens.

12. A computer implemented method according to claims 9 to 1 1 , further comprising- verifying the selected attribute from the electronic device associated with the content creator prior to sharing the digital content.

13. A computer implemented method according to claims 9 to 12, further comprising- allowing the shared digital content to encompass social media posts, emails, text messages, or documents, and enabling sharing across current and future social media and e-commerce platforms.

14. A computer implemented method according to claims 9 to 13, further comprising- notifying a new user of the electronic device to change the attribute selected by a previous content creator.

15. Use of a system according to anyone of claims 1 to 7, for providing valuable insights and metrics for business and marketing purposes.