System and method for AI-driven email-based social media management

Abstract

A system for AI-driven email-based social media management. The system receives emails containing post content from users 110, processes the content with an AI engine 122 to generate draft social medi

Description

FIELD OF INVENTION

[0001] The present disclosure relates to the field of digital communication and social media management, and more particularly, to the use of artificial intelligence for email-based content creation, scheduling, and multi-platform social media posting. BACKGROUND

[0002] Social media management has become an integral part of modern business operations, with companies of all sizes seeking to establish and maintain a strong online presence across multiple platforms. The proliferation of social media platforms, each with its unique features and audience demographics, has created both opportunities and challenges for businesses aiming to effectively engage with their target markets.

[0003] Traditionally, social media management has involved manual content creation, scheduling, and posting across various platforms. This process can be time-consuming and prone to errors, particularly for businesses managing multiple accounts or targeting diverse audiences. Moreover, the need for consistent brand messaging and timely responses to customer interactions has further complicated the task of effective social media management.

[0004] While several social media management tools have emerged to address these challenges, many of these solutions require users to learn new interfaces and workflows, which can be daunting for small businesses or individuals with limited technical expertise. Additionally, existing tools often lack sophisticated Al-driven features for content optimization, translation, and cross-platform adaptation, leaving users to manually tailor their content for each platform's specific requirements.

[0005] The increasing importance of data-driven decision making in social media strategy has also highlighted the need for more advanced analytics and insights. Current solutions often provide basic metrics but may fall short in offering actionable recommendations based on comprehensive analysis of user engagement patterns and content performance across multiple platforms. SUMMARY

[0006] Embodiments of the invention are set out in the appended claims.

[0007] 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.

[0008] The present disclosure provides a system for Al-driven email-based social media management. The system includes a computation unit comprising data storage configured to store user account information and social media platform data, processing circuitry configured to execute an Al engine, and an input output path configured to receive emails from users and send emails to users. The processing circuitry is configured to receive an email containing post content from a user, process the email content using the Al engine to generate a draft social media post, send a preview of the draft social media post to the user, receive approval or revision requests from the user, schedule the approved post for publication on one or more social media platforms, and publish the approved post on the one or more social media platforms at the scheduled time.

[0009] The system may include various features to enhance its functionality. For example, the Al engine may be configured to perform natural language processing on the email content to extract key information for generating the draft social media post. The system may also be capable of translating the draft social media post into one or more languages based on user preferences or target audience location. Additionally, the system may automatically add relevant hashtags to the draft social media post based on the content.

[0010] The system may further include advanced scheduling capabilities, such as resolving scheduling conflicts between multiple posts based on contextual factors including post urgency, content relevance, and historical engagement patterns. The system may also automatically edit the approved post for different social media platforms based on platformspecific requirements and analyze the content of the post to ensure compliance with platformspecific guidelines and legal regulations.

[0011] In some aspects, the system may employ a multi-layered approach to ensure compliance with platform-specific guidelines and legal regulations across different platforms and jurisdictions. The Al engine may maintain a comprehensive database of guidelines and regulations for various social media platforms and jurisdictions, which may be regularly updated to reflect the latest changes.

[0012] The compliance analysis process may begin with content categorization. The Al engine may use natural language processing and image recognition techniques to classify the content of the post into various categories such as text, images, videos, links, or advertisements. This categorization may help in applying the relevant set of guidelines and regulations.

[0013] For platform-specific compliance, the system may analyze the content against each target platform's unique requirements. This may include checking for prohibited content, adherence to character limits, image dimensions, video length, and format compatibility. The system may also verify the appropriate use of hashtags, mentions, and other platform-specific features.

[0014] In terms of legal compliance, the Al engine may incorporate a jurisdiction-based analysis. Based on the user's location and target audience, the system may apply relevant local, national, and international laws. This may include checking for potential copyright infringements, adherence to data protection regulations, and compliance with advertising standards.

[0015] The system may also implement a keyword and phrase analysis to identify potentially sensitive or restricted content. This may involve scanning the post for words or phrases related to politics, health claims, financial advice, or other regulated topics. When such content is detected, the system may flag it for additional review or provide warnings to the user.

[0016] In some cases, the Al engine may utilize machine learning algorithms to improve its compliance checking capabilities over time. By analyzing user feedback and platform responses to posted content, the system may refine its understanding of compliance requirements and improve its ability to detect potential issues.

[0017] The system may provide users with detailed compliance reports, highlighting any potential issues and suggesting modifications to ensure compliance. These reports may include explanations of the relevant guidelines or regulations, allowing users to make informed decisions about their content.

[0018] For posts targeting multiple platforms or jurisdictions, the system may generate platform-specific versions of the content, each tailored to meet the respective compliance requirements. This may involve adjusting text, resizing images, or modifying hashtags to ensure optimal compliance across all target platforms and regions.

[0019] In some implementations, the system may include an escalation process for complex compliance issues. When the Al engine encounters ambiguous situations or high-risk content, it may flag these for human review, ensuring an additional layer of compliance verification for sensitive or potentially controversial posts. BRIEF DESCRIPTION OF FIGURES

[0020] The present disclosure also provides a method for Al-driven email-based social media management. The method includes receiving an email containing post content from a user, processing the email content using an Al engine to generate a draft social media post, sending a preview of the draft social media post to the user, receiving approval or revision requests from the user, and if revisions are requested, rewriting the post using the Al engine and repeating the sending and receiving steps. The method further includes scheduling the approved post for publication on one or more social media platforms, resolving any scheduling conflicts using Al-based contextual analysis, and publishing the approved post on the one or more social media platforms at the scheduled time.

[0021] The method may include additional steps such as performing natural language processing on the email content to extract information, generating the draft social media post based on the extracted information, and automatically adding relevant hashtags to the draft social media post based on the content. The method may also involve translating the draft social media post into one or more languages and automatically editing the approved post for different social media platforms.

[0022] The present disclosure further provides a non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform the method for Al-driven email-based social media management as described above.

[0023] These systems, methods, and computer-readable media provide a comprehensive solution for managing social media content through an email-based interface, leveraging Al technology to streamline the process of content creation, translation, scheduling, and publication across multiple social media platforms.

[0024] The foregoing general description of the illustrative examples and the following detailed description thereof are merely exemplary aspects of the teachings of this disclosure and are not restrictive. BRIEF DESCRIPTION OF FIGURES

[0025] Non-limiting and non-exhaustive examples are described with reference to the following figures. The above and other objects and advantages of the disclosure will be apparent upon consideration of the following detailed description, taken in conjunction with the accompanying drawings, which: FIG. 1 illustrates a block diagram of an Al-driven email-based social media management system, in accordance with some examples of the present disclosure. FIG. 2 depicts a flowchart for an automated social media content management process, in accordance with some examples of the present disclosure. FIG. 3 shows a block diagram of a computation unit for a computing device, in accordance with some examples of the present disclosure. DETAILED DESCRIPTION

[0026] The following description sets forth exemplary aspects of the present disclosure. It should be recognised, however, that such a description is not intended as a limitation on the scope of the present disclosure. Rather, the description also encompasses combinations and modifications to those exemplary aspects described herein.

[0027] The present disclosure provides a system and method for Al-driven email-based social media management. The system comprises a computation unit with data storage, processing circuitry, and an input output path. The processing circuitry executes an Al engine configured to receive emails containing post content from users, process the content to generate draft social media posts, send previews to users for approval, and publish approved posts on multiple social media platforms. The Al engine performs natural language processing, content translation, hashtag generation, and scheduling conflict resolution based on contextual factors. The system automatically edits posts for platform-specific requirements and ensures compliance with guidelines and regulations. This disclosure streamlines social media management through an intuitive email-based interface, leveraging Al capabilities for content optimization and multi-platform publishing.

[0028] The present disclosure provides a system and method for managing social media content through an email-based interface, leveraging artificial intelligence (Al) capabilities for content creation, translation, scheduling, and multi-platform posting. The system includes a computation unit with data storage, processing circuitry, and an input output path. The processing circuitry executes an Al engine that is configured to receive emails containing post content from users, process the content to generate draft social media posts, send previews to users for approval, and publish approved posts on multiple social media platforms. The Al engine performs natural language processing, content translation, hashtag generation, and scheduling conflict resolution based on contextual factors. The system automatically edits posts for platform-specific requirements and ensures compliance with guidelines and regulations. This system and method streamline social media management, providing a user-friendly, efficient, and effective solution for businesses and individuals. The subsequent sections of this detailed description will further elaborate on the components and functionality of the system, the method of operation, and the various examples and aspects of the present disclosure.

[0029] Referring to FIG. 1, in some examples, the system for Al-driven email-based social media management includes a user 1 10, a second user content creator 112, a user content creator 114, a content forwarder 116, a platform 120, an Al composer 122A, an Al content reviewer 122B, an Al content editor 122C, an Al scheduling assistant 122D, an Al translation engine 122E, an approval module 126, and a posting module 128.

[0030] The user 110 interacts with the system primarily through email. The user 110 can send an email containing post content to a dedicated Product-Mx email address. The email may contain text, images, videos, or any other form of content that the user wishes to post on social media. The email may also specify the target social media platform(s) and the desired posting schedule.

[0031] The second user content creator 112 and the user content creator 114 can provide additional content to the user 110. This content can be incorporated into the user's social media posts, enhancing the variety and richness of the content.

[0032] The content forwarder 116 is responsible for forwarding or sending content to the platform 120. The platform 120 is the central component of the system, which processes and manages the content.

[0033] Within the platform 120, there are several Al-powered modules. The Al composer 122A is responsible for composing content. The Al composer 122A may utilize natural language processing techniques to understand the content of the email and generate a draft post that is relevant, engaging, and optimized for the target social media platform. The Al composer 122A may also use computer vision techniques to analyze any images or videos attached to the email and incorporate them into the draft post.

[0034] The Al content reviewer 122B reviews the content for appropriateness and quality. The Al content reviewer 122B may utilize machine learning techniques to identify potentially inappropriate or non-compliant content, such as explicit language, sensitive topics, or copyrighted material. The system may then alert the user or suggest modifications to the content to ensure compliance.

[0035] The Al content editor 122C edits the content as needed. The Al content editor 122C may adjust the format, size, or aspect ratio of images or videos to meet the specifications of each platform. The Al content editor 122C may also modify the text of the post to fit the character limits or style guidelines of each platform.

[0036] The Al scheduling assistant 122D helps in scheduling posts. The Al scheduling assistant 122D may analyze past post performance data, such as likes, shares, comments, and views, to identify the times when user engagement is highest. The Al scheduling assistant 122D may then recommend these times to the user for scheduling future posts.

[0037] The Al translation engine 122E translates content into different languages as required. The Al translation engine 122E may utilize machine translation techniques, such as statistical machine translation or neural machine translation, to produce translations that are accurate, fluent, and natural-sounding.

[0038] The approval module 126 is connected to the platform 120 and is responsible for obtaining approval of created content. Once approved, the posting module 128 handles the actual posting of content to various social media platforms according to the user's approval and schedule.

[0039] In some examples, the system supports specific social media platforms including Facebook, Instagram, Threads, X, LINE, TikTok, Youtube, WeChat and Xiaohongshu. The system may interact with the APIs of these platforms to post content directly from the system, retrieve performance data, and manage the user's social media accounts.

[0040] In some examples, the input output path 330 is further configured to receive user feedback on post performance and the Al engine is configured to use this feedback to improve future post generation. The processing circuitry 320 is further configured to generate analytics reports based on post performance across multiple social media platforms. These features help to maximize the reach and impact of each post, leading to improved social media engagement and growth. [0041 ] In some examples, the system may generate comprehensive analytics reports that provide detailed insights into post performance across multiple social media platforms. These reports may include a wide range of metrics and key performance indicators (KPIs) to help users understand the effectiveness of their social media strategy and make data-driven decisions.

[0042] The analytics reports may include engagement metrics such as likes, shares, comments, and click-through rates for each post across different platforms. The system may also track reach and impressions, providing users with information on how many unique users saw their content and how many times it was displayed.

[0043] In some cases, the analytics reports may include audience demographics and psychographics, offering insights into the age, gender, location, interests, and behaviors of the users engaging with the content. This information may help users tailor their content strategy to better resonate with their target audience.

[0044] The system may also provide sentiment analysis, using natural language processing techniques to categorize user comments and reactions as positive, negative, or neutral. This analysis may help users gauge the overall reception of their content and identify potential areas for improvement.

[0045] In some implementations, the analytics reports may include comparative analysis, allowing users to benchmark their performance against industry averages or competitors. The system may also provide trend analysis, highlighting patterns in engagement rates, optimal posting times, and content types that perform well over time.

[0046] The Al engine may compile these analytics by integrating data from various social media platform APIs, aggregating the information into a unified dashboard. The system may normalize metrics across different platforms to provide consistent and comparable data, accounting for platform-specific nuances in how engagement is measured.

[0047] In some aspects, the analytics reports may include predictive analytics, using machine learning algorithms to forecast future post performance based on historical data and current trends. This feature may help users optimize their content strategy and scheduling for maximum impact.

[0048] The system may also provide real-time analytics, allowing users to monitor post performance as it happens and make quick adjustments to their social media strategy if needed. In some cases, the Al engine may generate automated insights and recommendations based on the analytics data, suggesting ways to improve content performance or identify opportunities for increased engagement.

[0049] These comprehensive analytics reports may be accessible through the email interface, with users able to request specific reports or receive scheduled updates. The system may also provide visualization tools, such as graphs and charts, to help users easily interpret the data and identify key trends or patterns in their social media performance.

[0050] Referring to FIG. 2, in some examples, the processing circuitry 320 is configured to schedule the approved post for publication on one or more social media platforms at the scheduled time (step 214). The scheduling may be based on the user's preferences, the optimal posting times for each platform, or any other relevant factors. The scheduling process ensures that the post is published at the right time to maximize its reach and engagement.

[0051] Referring to FIG. 2, in some examples, the automated social media content management process 200 begins with step 202, where the processing circuitry 320 of the computation unit 300 is configured to receive an email containing post content from a user via the input output path 330. The email may contain text, images, videos, or any other form of content that the user wishes to post on social media. The email may also specify the target social media platform(s) and the desired posting schedule.

[0052] In step 204, the processing circuitry 320 processes the email content using an Al engine. The Al engine may utilize natural language processing techniques to understand the content of the email and generate a draft social media post. The Al engine may also use computer vision techniques to analyze any images or videos attached to the email and incorporate them into the draft post. The Al engine may further add relevant hashtags or metadata to the draft post based on the content.

[0053] Once the draft post is ready, the process 200 proceeds to step 208, where the processing circuitry 320 sends a preview of the draft social media post to the user via the input output path 330. The preview may be formatted to resemble how the post would appear on the target social media platform(s). The user can review the preview and provide feedback or approval.

[0054] In step 210, the processing circuitry 320 receives approval or revision requests from the user via the input output path 330. If the user approves the draft post, the process 200 proceeds to the scheduling step. If the user requests revisions, the Al engine may rewrite the post based on the user's feedback and the process returns to step 208 for another round of preview and approval.

[0055] In some examples, the processing circuitry 320 is configured to schedule the approved post for publication on one or more social media platforms. The scheduling may be based on the user's preferences, the optimal posting times for each platform, or any other relevant factors. The scheduling process ensures that the post is published at the right time to maximize its reach and engagement.

[0056] Referring to FIG. 2, in some examples, the processing circuitry 320 is configured to schedule the approved post for publication on one or more social media platforms at the scheduled time (step 214). The scheduling may be based on the user's preferences, the optimal posting times for each platform, or any other relevant factors. The scheduling process ensures that the post is published at the right time to maximize its reach and engagement.

[0057] In some examples, the processing circuitry 320 is further configured to resolve scheduling conflicts between multiple posts based on contextual factors including post urgency, content relevance, and historical engagement patterns (step 216). For instance, if a user schedules two posts for the same time, the system may prioritize the post with higher urgency or relevance. The system may also consider the historical engagement patterns of similar posts to determine the optimal posting order. This Al-driven conflict resolution enhances the effectiveness of the social media strategy by ensuring that each post is published at the most appropriate time.

[0058] In some examples, the Al engine may employ a multi-faceted approach to determine post urgency and content relevance when resolving scheduling conflicts. The system may analyze various factors to assess the urgency of a post, including: 1. Time-sensitive content: The Al engine may identify keywords or phrases that indicate time-sensitive information, such as "breaking news," "limited time offer," or specific dates and times mentioned in the post content. 2. User-specified priority: The system may allow users to assign priority levels to their posts when submitting content via email, which the Al engine can factor into its urgency assessment. 3. Event-related content: The Al engine may cross-reference post content with a database of upcoming events, holidays, or trending topics to determine if the post is tied to a specific timely occurrence. 4. Seasonal relevance: The system may consider the current season or time of year when assessing the urgency of posts related to seasonal products, services, or events.

[0059] To determine content relevance, the Al engine may consider: 1. Keyword analysis: The system may analyze the frequency and importance of keywords in the post content, comparing them to trending topics and the user's historical post performance. 2. User engagement history: The Al engine may evaluate the performance of similar posts in the past, considering metrics such as likes, shares, comments, and click-through rates. 3. Target audience alignment: The system may assess how well the post content aligns with the interests and demographics of the user's target audience on each social media platform. 4. Content freshness: The Al engine may consider how recently similar content has been posted, prioritizing posts that offer new or updated information. 5. Cross-platform relevance: The system may evaluate the suitability of the content for different social media platforms, considering platform-specific trends and user behavior.

[0060] When resolving scheduling conflicts, the Al engine may use a weighted scoring system that combines these urgency and relevance factors. The system may assign numerical values to each factor and calculate an overall priority score for each post. Posts with higher priority scores may be given precedence in the scheduling process. [0061 ] In some cases, the Al engine may also consider the optimal posting times for each platform when resolving conflicts. If two posts have similar priority scores but one aligns better with the optimal posting time for its target platform, the system may prioritize that post for the conflicting time slot and reschedule the other post to the next best available time.

[0062] The Al engine may continuously learn and adapt its conflict resolution strategies based on the performance of posted content. By analyzing the engagement metrics of posts that were prioritized in conflict situations, the system may refine its weighting of different factors to improve future scheduling decisions.

[0063] In some implementations, the system may provide users with transparency into the conflict resolution process by including explanations of scheduling decisions in the preview emails sent for approval. This may allow users to understand why certain posts were prioritized and provide feedback to further improve the Al engine's decision-making process.

[0064] In some examples, the Al engine is configured to perform natural language processing on the email content to extract key information for generating the draft social media post. The Al engine may analyze the text of the email to identify the main topics, keywords, sentiments, and entities. This information is then used to generate a draft post that is relevant, engaging, and optimized for the target social media platform.

[0065] In some examples, the Al engine is further configured to translate the draft social media post into one or more languages based on user preferences or target audience location. The Al engine may utilize machine translation techniques, such as statistical machine translation or neural machine translation, to produce translations that are accurate, fluent, and natural-sounding. This feature allows users to reach a global audience by posting content in multiple languages.

[0066] In some examples, the Al engine may consider various factors when translating the draft social media post into one or more languages based on user preferences or target audience location. The system may store and analyze user profile information, including language preferences, geographic location, and target markets, to determine which languages may be most relevant for translation.

[0067] The Al engine may access a database of user preferences that includes language settings for different social media platforms. For instance, a user may prefer to post in English on Linkedln but in Spanish on Facebook. The system may automatically apply these preferences when generating translations for each platform.

[0068] In some cases, the Al engine may analyze the content of the post to determine its relevance to specific geographic regions or cultural contexts. Based on this analysis, the system may suggest or automatically generate translations for languages commonly spoken in those regions.

[0069] The system may also consider the target audience location by analyzing engagement data from previous posts. If the user's content consistently receives high engagement from users in certain countries or regions, the Al engine may prioritize translations into the languages spoken in those areas.

[0070] In some implementations, the Al engine may use geolocation data associated with the user's social media accounts to infer the most relevant languages for translation. For example, if a user frequently posts from multiple countries, the system may automatically generate translations for the primary languages spoken in those locations.

[0071] The Al engine may also take into account current events, trending topics, or seasonal factors in different regions when determining which languages to prioritize for translation. This contextual awareness may help ensure that translated content remains relevant and timely for diverse audiences.

[0072] In some aspects, the system may allow users to specify target audience demographics, including language preferences, for specific campaigns or post series. The Al engine may then use this information to generate appropriate translations for each post within the campaign.

[0073] The Al translation engine may also consider platform-specific language trends and user behaviors. For instance, if certain languages are more commonly used or have higher engagement rates on particular social media platforms, the system may prioritize those languages for translation when posting to those platforms.

[0074] In some implementations, the system may use machine learning algorithms to continuously refine its language selection and translation processes based on user feedback and post performance metrics. This adaptive approach may help improve the relevance and effectiveness of translated content over time.

[0075] In some examples, the processing circuitry 320 is configured to automatically add relevant hashtags to the draft social media post based on the content. The Al engine may analyze the content of the post to identify relevant topics, trends, or keywords, and generate corresponding hashtags. These hashtags can help to increase the visibility and reach of the post on social media platforms.

[0076] In some examples, the processing circuitry 320 is configured to publish the approved post on the one or more social media platforms at the scheduled time (step 218). The system may interact with the APIs of the target social media platforms to post the content directly from the system. This automated posting process saves users the time and effort of manually posting content on each platform.

[0077] In some examples, the processing circuitry 320 is further configured to automatically edit the approved post for different social media platforms based on platformspecific requirements (step 220). For instance, the system may adjust the format, size, or aspect ratio of images or videos to meet the specifications of each platform. The system may also modify the text of the post to fit the character limits or style guidelines of each platform. This automatic editing feature ensures that each post is optimized for its target platform, enhancing its appearance and performance.

[0078] Referring to FIG. 2, in some examples, the processing circuitry 320 is configured to automatically edit the approved post for different social media platforms based on platformspecific requirements (step 220). For instance, the system may adjust the format, size, or aspect ratio of images or videos to meet the specifications of each platform. The system may also modify the text of the post to fit the character limits or style guidelines of each platform. This automatic editing feature ensures that each post is optimized for its target platform, enhancing its appearance and performance.

[0079] In some examples, the Al engine is configured to analyze the content of the post to ensure compliance with platform-specific guidelines and legal regulations. The Al engine may utilize machine learning techniques to identify potentially inappropriate or non-compliant content, such as explicit language, sensitive topics, or copyrighted material. The system may then alert the user or suggest modifications to the content to ensure compliance. This feature helps to protect users from potential legal issues and maintain a positive and respectful social media presence.

[0080] In some examples, the Al engine is configured to recommend optimal posting times based on historical engagement data for each social media platform. The Al engine may analyze past post performance data, such as likes, shares, comments, and views, to identify the times when user engagement is highest. The system may then recommend these times to the user for scheduling future posts. This feature helps to maximize the reach and impact of each post, leading to improved social media engagement and growth.

[0081] In some examples, the process 200 begins with step 202, where the processing circuitry 320 of the computation unit 300 is configured to receive an email containing post content from a user via the input output path 330. The email may contain text, images, videos, or any other form of content that the user wishes to post on social media. The email may also specify the target social media platform(s) and the desired posting schedule.

[0082] In step 204, the processing circuitry 320 processes the email content using an Al engine to generate a draft social media post. The Al engine may utilize natural language processing techniques to understand the content of the email and generate a draft post that is relevant, engaging, and optimized for the target social media platform. The Al engine may also use computer vision techniques to analyze any images or videos attached to the email and incorporate them into the draft post. The Al engine may further add relevant hashtags or metadata to the draft post based on the content.

[0083] Referring to FIG. 2, in some examples, once the draft post is ready, the processing circuitry 320 sends a preview of the draft social media post to the user via the input output path 330 (step 208). The preview may be formatted to resemble how the post would appear on the target social media platform(s). The user can review the preview and provide feedback or approval. This step allows the user to ensure that the post meets their expectations and adheres to their desired messaging and branding.

[0084] In some examples, the processing circuitry 320 receives approval or revision requests from the user via the input output path 330 (step 210). If the user approves the draft post, the process 200 proceeds to the scheduling step. If the user requests revisions, the Al engine may rewrite the post based on the user's feedback and the process returns to step 208 for another round of preview and approval. This iterative process ensures that the final post aligns with the user's preferences and objectives.

[0085] In some examples, the processing circuitry 320 is configured to schedule the approved post for publication on one or more social media platforms (step 214). The scheduling may be based on the user's preferences, the optimal posting times for each platform, or any other relevant factors. The scheduling process ensures that the post is published at the right time to maximize its reach and engagement.

[0086] In some examples, the processing circuitry 320 is further configured to resolve scheduling conflicts between multiple posts based on contextual factors including post urgency, content relevance, and historical engagement patterns (step 216). For instance, if a user schedules two posts for the same time, the system may prioritize the post with higher urgency or relevance. The system may also consider the historical engagement patterns of similar posts to determine the optimal posting order. This Al-driven conflict resolution enhances the effectiveness of the social media strategy by ensuring that each post is published at the most appropriate time.

[0087] In some examples, the processing circuitry 320 is configured to publish the approved post on the one or more social media platforms at the scheduled time (step 218). The system may interact with the APIs of the target social media platforms to post the content directly from the system. This automated posting process saves users the time and effort of manually posting content on each platform.

[0088] In some examples, the processing circuitry 320 is further configured to automatically edit the approved post for different social media platforms based on platformspecific requirements (step 220). For instance, the system may adjust the format, size, or aspect ratio of images or videos to meet the specifications of each platform. The system may also modify the text of the post to fit the character limits or style guidelines of each platform. This automatic editing feature ensures that each post is optimized for its target platform, enhancing its appearance and performance.

[0089] Referring to FIG. 2, in some examples, the processing circuitry 320 is configured to publish the approved post on the one or more social media platforms at the scheduled time (step 218). The system may interact with the APIs of the target social media platforms to post the content directly from the system. This automated posting process saves users the time and effort of manually posting content on each platform, thereby enhancing the efficiency of social media management.

[0090] In some examples, the Al engine is configured to perform natural language processing on the email content to extract key information for generating the draft social media post (step 204). The Al engine may analyze the text of the email to identify the main topics, keywords, sentiments, and entities. This information is then used to generate a draft post that is relevant, engaging, and optimized for the target social media platform. The Al engine may also use computer vision techniques to analyze any images or videos attached to the email and incorporate them into the draft post. This feature allows the system to create high-quality social media content that is tailored to the user's needs and preferences. [0091 ] In some examples, the processing circuitry 320 is configured to automatically add relevant hashtags to the draft social media post based on the content (step 204). The Al engine may analyze the content of the post to identify relevant topics, trends, or keywords, and generate corresponding hashtags. These hashtags can help to increase the visibility and reach of the post on social media platforms, thereby enhancing the effectiveness of the user's social media strategy.

[0092] In some examples, the Al engine is further configured to translate the draft social media post into one or more languages based on user preferences or target audience location (step 206). The Al engine may utilize machine translation techniques, such as statistical machine translation or neural machine translation, to produce translations that are accurate, fluent, and natural-sounding. This feature allows users to reach a global audience by posting content in multiple languages, thereby expanding the user's social media presence.

[0093] In some examples, the processing circuitry 320 is further configured to automatically edit the approved post for different social media platforms based on platformspecific requirements (step 220). For instance, the system may adjust the format, size, or aspect ratio of images or videos to meet the specifications of each platform. The system may also modify the text of the post to fit the character limits or style guidelines of each platform. This automatic editing feature ensures that each post is optimized for its target platform, enhancing its appearance and performance.

[0094] Referring to FIG. 2, in some examples, the processing circuitry 320 is further configured to automatically edit the approved post for different social media platforms based on platform-specific requirements (step 220). For instance, the system may adjust the format, size, or aspect ratio of images or videos to meet the specifications of each platform. The system may also modify the text of the post to fit the character limits or style guidelines of each platform. This automatic editing feature ensures that each post is optimized for its target platform, enhancing its appearance and performance.

[0095] In some examples, the processing circuitry 320 is configured to resolve scheduling conflicts between multiple posts based on contextual factors including post urgency, content relevance, and historical engagement patterns (step 216). For instance, if a user schedules two posts for the same time, the system may prioritize the post with higher urgency or relevance. The system may also consider the historical engagement patterns of similar posts to determine the optimal posting order. This Al-driven conflict resolution enhances the effectiveness of the social media strategy by ensuring that each post is published at the most appropriate time.

[0096] In some examples, the processing circuitry 320 is configured to analyze the content of the post to ensure compliance with platform-specific guidelines and legal regulations. The Al engine may utilize machine learning techniques to identify potentially inappropriate or non-compliant content, such as explicit language, sensitive topics, or copyrighted material. The system may then alert the user or suggest modifications to the content to ensure compliance. This feature helps to protect users from potential legal issues and maintain a positive and respectful social media presence.

[0097] In some examples, the Al engine is configured to recommend optimal posting times based on historical engagement data for each social media platform. The Al engine may analyze past post performance data, such as likes, shares, comments, and views, to identify the times when user engagement is highest. The system may then recommend these times to the user for scheduling future posts. This feature helps to maximize the reach and impact of each post, leading to improved social media engagement and growth.

[0098] Referring to FIG. 3, in some examples, the system for Al-driven email-based social media management includes a computation unit 300. The computation unit 300 comprises data storage 310, processing circuitry 320, and an input output path 330. These components work together to enable the system's functionality and support the Al-driven email-based social media management process.

[0099] In some examples, the data storage 310 is configured to store user account information and social media platform data. The user account information may include the user's email address, social media account credentials, preferences, and history. The social media platform data may include the APIs, guidelines, and specifications of the supported social media platforms. The data storage 310 may also store the emails received from users, the draft and approved posts generated by the Al engine, and the scheduling and performance data of the posts.

[0100] In some examples, the processing circuitry 320 is configured to execute an Al engine. The Al engine is responsible for processing the emails received from users, generating draft social media posts, sending previews to users for approval, scheduling the approved posts for publication, and publishing the approved posts on the social media platforms. The Al engine may utilize various Al techniques, such as natural language processing, machine learning, and computer vision, to perform these tasks. The processing circuitry 320 may also handle the interactions with the social media platforms, such as posting the content, retrieving the performance data, and managing the user's social media accounts.

[0101] In some examples, the input output path 330 is configured to receive emails from users and send emails to users. The input output path 330 may support various email protocols, such as SMTP, IMAP, and POP3, and may be compatible with various email clients and services. The input output path 330 may receive emails containing post content, user feedback, and approval or revision requests from users. The input output path 330 may also send emails containing previews, notifications, and reports to users.

[0102] In some examples, the system may further include a non-transitory computer-readable medium storing instructions that, when executed by the processing circuitry 320, cause the computation unit 300 to perform the Al-driven email-based social media management process. The instructions may include the steps of receiving an email, processing the email content, generating a draft post, sending a preview, receiving approval or revision requests, scheduling the approved post, resolving scheduling conflicts, and publishing the approved post. The non-transitory computer-readable medium may be a part of the data storage 310 or may be a separate component of the computation unit 300.

[0103] In some examples, the computation unit 300 may be implemented in a server, a cloud-based system, a personal computer, a mobile device, or any other suitable computing device. The computation unit 300 may be connected to the internet or a local network to receive and send emails, interact with the social media platforms, and access the Al engine and other resources. The computation unit 300 may also include other components, such as a power supply, a cooling system, and a user interface, to support its operation and functionality.

[0104] In some examples, the Al engine executed by the processing circuitry 320 may be configured to perform advanced content creation tasks. These tasks may involve the use of natural language generation (NLG) and computer vision techniques. NLG is a process of creating natural language text from structured or unstructured data, such as keywords, topics, or images. Computer vision is a process of analyzing and understanding visual information, such as faces, objects, scenes, or actions. The Al engine may use these techniques to create content that is relevant, engaging, and optimized for each social media platform, using the appropriate formats, dimensions, and features. For example, the Al engine may create captions, hashtags, and mentions for images or videos, or generate text or audio scripts for stories or reels.

[0105] In some examples, the Al engine may also provide content recommendations based on user preferences, interests, goals, and behavior. The Al engine may use natural language processing (NLP) and machine learning (ML) techniques to analyze the user's profile, history, and feedback, and to learn from their patterns and trends. NLP is a process of understanding and manipulating natural language text or speech, such as sentiment, topics, keywords, or entities. ML is a process of learning from data and making predictions or decisions, using algorithms and models. The Al engine may use these techniques to suggest content ideas, topics, formats, or styles that can match the user's needs and expectations. For example, the Al engine may suggest what type of content to post, when to post it, or how to edit it.

[0106] In some examples, the processing circuitry 320 may be further configured to automatically edit the content provided by the client for different platforms. This may involve horizontal and vertical editing, or creating short-form clips from a longer provided video. The automatic editing feature ensures that each post is optimized for its target platform, enhancing its appearance and performance.

[0107] In some examples, the Al engine may also check that the content is appropriate for the platform. If the content is not appropriate for a particular jurisdiction, the system may not post and suggest that it should not be posted. Examples include tobacco or alcohol advertising in some countries is restricted or banned, and other examples include criticism of local governments or the like which may incur fines or penalties. This feature helps to protect users from potential legal issues and maintain a positive and respectful social media presence.

[0108] In some examples, the system for Al-driven email-based social media management is implemented using specific technologies and frameworks. The front-end of the system may be developed using React.js, a JavaScript library for building user interfaces. React.js allows for the creation of reusable UI components, which can enhance the efficiency of development and maintainability of the system. The back-end of the system may be developed using Node.js, a JavaScript runtime built on Chrome's V8 JavaScript engine. Node.js is known for its event-driven, non-blocking I / O model, which makes it lightweight and efficient, perfect for data-intensive real-time applications that run across distributed devices.

[0109] In some examples, the system may use MongoDB as the database management system. MongoDB is a source-available cross-platform document-oriented database program. Classified as a NoSQL database program, MongoDB uses JSON-like documents with optional schemas. MongoDB is developed by MongoDB Inc. This choice of database can provide flexibility in storing various types of data, including user account information, social media platform data, email content, draft and approved posts, and scheduling and performance data.

[0110] In some examples, the system may integrate with various third-party APIs to support its functionality. For instance, the system may interact with the APIs of the supported social media platforms, such as Facebook Graph API, Twitter API, Instagram API, Linkedln API, and YouTube Data API, to post content directly from the system, retrieve performance data, and manage the user's social media accounts. The system may also integrate with Al APIs, such as OpenAI API and ChatGPT API, to leverage advanced Al capabilities for content creation, translation, scheduling, and multi-platform posting.

[0111] In some examples, the system includes specific security measures to protect the user's data and ensure the integrity of the system. The system may use the HTTPS protocol for all the communication between the client and the server, providing a secure channel for data transmission. The system may also encrypt the user's passwords and the social media access tokens using crypt, a one-way hash function, before storing them in the database. This can prevent unauthorized access to the user's data even if the database is compromised.

[0112] In some examples, the system may use OAuth 2.0 protocol for authenticating the user and obtaining the access tokens from the third-party APIs. OAuth 2.0 is an authorization framework that enables applications to obtain limited access to user accounts on an HTTP service. It works by delegating user authentication to the service that hosts the user account and authorizing third-party applications to access the user account. OAuth 2.0 provides several grant types for different use examples, including authorization code for apps running on a web server, implicit for browser-based or mobile apps, password for logging in with a username and password, and client credentials for application access.

[0113] In some examples, the system may use JWT (JSON Web Token) for authorizing the user and verifying the requests from the client. JWT is an open standard that defines a compact and self-contained way for securely transmitting information between parties as a JSON object. This information can be verified and trusted because it is digitally signed. JWTs can be signed using a secret (with the HMAC algorithm) or a public / private key pair using RSA or ECDSA.

[0114] In some examples, the system may use CORS (Cross-Origin Resource Sharing) for preventing cross-site request forgery (CSRF) attacks. CORS is a mechanism that allows many resources (e.g., fonts, JavaScript, etc.) on a web page to be requested from another domain outside the domain from which the resource originated. By allowing selective crossorigin access to your resources, you can prevent CSRF attacks that trick the victim into loading a page that contains a malicious request.

[0115] In some examples, the system may use helmet.js for setting various HTTP headers to enhance the security of the server. Helmet.js is a collection of middleware functions that help secure Express.js applications by setting various HTTP headers. It's not a silver bullet, but it can help protect the application from some well-known web vulnerabilities by default.

[0116] The system for Al-driven email-based social media management incorporates cloud integration to enhance accessibility and scalability. The social monitoring tool is located on a remote server outside the organization's network, allowing for seamless integration with the marketing tool in a cloud computing environment. This architecture enables employees to access both tools using single sign-on credentials, streamlining the user experience and improving security.

[0117] The social monitoring functionality of the system is designed to monitor social media data from both external public networks and internal private networks. The internal network contains content pertaining to employees within the organization, providing a comprehensive view of social media activity. The system performs a first monitoring at the social monitoring tool to gather data from these diverse sources.

[0118] Data analysis is a component of the system. It generates trends for products and services by analyzing both external and internal social data. The Al engine performs semantic and sentiment analysis on the social media data, extracting valuable insights from the content. For internal content, the system associates profile data, including professional information, with the originators of the content, providing context for the analysis.

[0119] The system is capable of identifying actionable messages based on the results of the semantic and sentiment analysis. It tags social media data with one or more tags based on the analysis results, categorizing the content for further processing. The system then associates these actionable messages with criteria of the organization's products or services, enabling targeted responses and marketing efforts.

[0120] Message transmission is handled efficiently by the system. It transmits actionable messages to enterprise tools based on the assigned tags. This process is governed by a rule engine with predefined rules identifying how to direct the actionable messages, ensuring that the right information reaches the appropriate departments or individuals within the organization.

[0121] The Al engine facilitates the creation of marketing programs using the results of the semantic and sentiment analysis. These programs are designed to capitalize on identified trends and insights. The system is capable of distributing marketing messages on social media sites in response to these identified trends, allowing for timely and relevant engagement with the target audience.

[0122] To measure the effectiveness of these marketing efforts, the system performs a second monitoring to analyze the impact of the marketing programs. It tags marketing messages with unique marketing tracking codes, allowing for precise attribution and performance measurement. The system extracts information from responses to marketing messages, providing valuable feedback on campaign performance.

[0123] As part of this monitoring process, the system flags responses containing URLs or abusive language, helping to maintain brand safety and identify potential issues. It correlates these responses with marketing programs using the tracking codes, providing a comprehensive view of campaign performance. The system also allows for the deletion of marketing messages in response to flagged responses, enabling quick action in case of problematic content.

[0124] The Al engine's scheduling capabilities are sophisticated, identifying multiple message delivery schedules spanning across a period of delivery time. This feature allows for strategic timing of content distribution to maximize engagement and reach. The system then distributes marketing messages according to these optimized schedules.

[0125] Additional features of the system include a dashboard visualization of analysis results, providing users with an intuitive interface to understand social media performance and trends. The system also performs semantic filtering on social media data, allowing for more refined analysis and targeting.

[0126] The Al engine supports the implementation of storefronts on social media sites, enabling direct e-commerce integration with social media marketing efforts. It also facilitates the distribution of electronic coupons and marketing emails, providing multiple channels for customer engagement and promotion.

[0127] Finally, the system captures social statistical data to analyze the effects of marketing programs. This comprehensive data collection and analysis capability allows organizations to continuously refine their social media strategies based on empirical evidence of performance and engagement.

[0128] A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the disclosure. Accordingly, other implementations are within the scope of the following claims.

Claims

03 251. A system for Al-driven email-based social media management, comprising: a computation unit (300) comprising:data storage (310) configured to store user account information and social media platform data;processing circuitry (320) configured to execute an Al engine; andan input output path (330) configured to receive emails from users and send emails to users;wherein the processing circuitry (320) is configured to:receive an email containing post content from a user via the input output path (330);process the email content using the Al engine to generate a draft social media post;send a preview of the draft social media post to the user via the input output path (330);receive approval or revision requests from the user via the input output path (330);schedule the approved post for publication on one or more social media platforms;authenticate with the one or more social media platforms using the stored user account information;determine platform-specific requirements using the stored social media platform data; andpublish the approved post on the one or more social media platforms at the scheduled time in accordance with the platform-specific requirements.

2. The system of claim 1, wherein the Al engine is configured to perform natural language processing on the email content to extract key information for generatingthe draft social media post.

3. The system of claim 1, wherein the Al engine is further configured to translate the draft social media post into one or more languages based on user preferences or target audience location.

4. The system of claim 1, wherein the processing circuitry (320) is further configured to automatically add relevant hashtags to the draft social media post based on the content.

5. The system of claim 1, wherein the processing circuitry (320) is further configured to resolve scheduling conflicts between multiple posts based on11 03 25contextual factors including post urgency, content relevance, and historical engagement patterns.

6. The system of claim 1, wherein the processing circuitry (320) is further configured to automatically edit the approved post for different social media platforms based on platform specific requirements.

7. The system of claim 1, wherein the Al engine is further configured to analyze the content of the post to ensure compliance with platform-specific guidelines and legal regulations.

8. The system of any one of claims 1-7, wherein the input output path (330) is further configured to receive user feedback on post performance and the Al engine is configured to use this feedback to improve future post generation.

9. The system of claim 1, wherein the processing circuitry (320) is further configured to generate analytics reports based on post performance across multiple social media platforms.

10. The system of claim 1, wherein the Al engine is further configured to recommend optimal posting times based on historical engagement data for each social media platform.

11. A method for Al-driven email-based social media management, comprising: receiving (202) an email containing post content from a user;processing (204) the email content using an Al engine to generate a draft social media post;sending (208) a preview of the draft social media post to the user;receiving (210) approval or revision requests from the user;if revisions are requested, rewriting (212) the post using the Al engine and repeatingthe sending and receiving steps;scheduling (214) the approved post for publication on one or more social media platforms;authenticating with the one or more social media platforms using the stored user account information;determining platform-specific requirements using the stored social media platform data;resolving (216) any scheduling conflicts using Al-based contextual analysis; and publishing (218) the approved post on the one or more social media platforms at the scheduled time in accordance with the platformspecific requirements.11 03 2512. The method of claim 11, wherein processing (204) the email content using an Al engine comprises:performing natural language processing on the email content to extract key information;generating the draft social media post based on the extracted key information; andautomatically adding relevant hashtags to the draft social media post based on the content.

13. The method of claim 11 or 12, further comprising:translating (206) the draft social media post into one or more languages based on user preferences or target audience location; andautomatically editing (220) the approved post for different social media platforms based on platform-specific requirements.

14. The method of any one of claims 11 to 13, wherein resolving (216) any scheduling conflicts using Al-based contextual analysis comprises: analyzing contextual factors including post urgency, content relevance, and historical engagement patterns;determining a priority order for multiple posts based on the analyzed contextual factors; andadjustingthe scheduling (214) of the posts according to the determined priority order.

15. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform a method for Al-driven email-based social media management, the method comprising: receiving an email containing post content from a user;processing the email content using an Al engine to generate a draft social media post;sending a preview of the draft social media post to the user; receiving approval or revision requests from the user;if revisions are requested, rewriting the post usingthe Al engine and repeating the sending and receiving steps;schedulingthe approved post for publication on one or more social media platforms;authenticating with the one or more social media platforms usingthe stored user account information;determining platform-specific requirements usingthe stored social media platform data;resolving any scheduling conflicts using Al-based contextual analysis; andpublishing the approved post on the one or more social media platforms at the scheduled time in accordance with the platform-specific requirements.LDCM

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