Unconstrained social media based on artificial intelligence

Abstract

The invention discloses unconstrained social media based on artificial intelligence. Systems or methods disclosed herein may provide for artificial intelligence-based social media content aggregation across multiple platforms. In an example, a computing system may receive a content query and may retrieve related content from various social media platforms. The computing system may generate an integrated content feed that includes first-party content from a platform owned by the primary entity and third-party content from an external messaging platform. The integrated feed may be arranged into the content slots, and sponsored content is intelligently placed between the organic content slots based on user engagement metrics. The computing system may implement a machine learning model to optimize content selection or placement based on the user interaction pattern.

Description

Cross-references to related applications

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 723,155, filed November 21, 2024, and U.S. Non-Provisional Patent Application No. 19 / 354,186, filed October 9, 2025, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This disclosure relates to social media content delivery systems, and more specifically, to artificial intelligence-based systems and methods for dynamically aggregating and presenting social media content from multiple platforms. Background Technology

[0003] Social media platforms facilitate user interaction, content sharing, and network building through digital archives, multimedia publishing, and engagement tools such as likes, comments, and shares. These platforms employ algorithms to personalize content and optimize user experience, thereby enhancing connectivity, information exchange, and social engagement across a diverse global user base. Summary of the Invention

[0004] The system or method disclosed herein can provide AI-based aggregation of social media content across multiple platforms. In one example, the computational system can receive content queries and retrieve relevant content from various social media platforms. The computational system can generate an integrated content feed that includes first-party content from platforms owned by major entities and third-party content from external messaging platforms. The integrated feed can be arranged into content slots, and sponsored content can be intelligently placed among the organic content slots based on user engagement metrics. The computational system can implement machine learning models to optimize content selection or arrangement based on user interaction patterns.

[0005] Additional advantages will be set forth in part in the description that follows, or may be learned through practice. These advantages will be realized and obtained through the elements and combinations particularly pointed out in the appended claims. It should be understood that, as claimed, the foregoing general description and the following detailed description are exemplary and illustrative only, and not restrictive. Attached Figure Description

[0006] Figure 1A An example system for aggregating dynamic social media content is shown.

[0007] Figure 1B Example user devices associated with dynamic social media content aggregation are shown.

[0008] Figure 2 An example method for aggregating dynamic social media content as disclosed in this paper is shown.

[0009] Figure 3 An example method for aggregating dynamic social media content as disclosed in this paper is shown.

[0010] Figure 4 A framework associated with machine learning is shown.

[0011] Figure 5 An example block diagram of an exemplary computing device suitable for implementing various aspects of the disclosed subject matter is shown.

[0012] These accompanying drawings depict various embodiments for illustrative purposes only. Those skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods shown herein can be employed without departing from the principles described herein. Detailed Implementation

[0013] Some embodiments of the invention will now be described more fully below with reference to the accompanying drawings, which illustrate some, but not all, of the embodiments of the invention. Various embodiments of the invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Similar reference numerals throughout the drawings refer to similar elements.

[0014] It will be understood that the methods and systems described herein are not limited to any particular method, component, or implementation. It will also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.

[0015] Traditional social media platforms operate in silos, requiring users to access multiple apps separately to view content across different platforms. This leads to inefficiency, disrupts user experience, and reduces engagement as users switch between apps to access content from different sources. Content feeds often include irrelevant content and advertisements that lower user engagement. There is a need for intelligent mechanisms to integrate and organize cross-platform content, based on user preferences and intent. The topics presented can provide dynamic content integration and delivery.

[0016] Figure 1A An example system 100 associated with dynamic social media content aggregation is illustrated. System 100 may include a user device 102, a social media platform 104, or a social media platform 106 connected via a network 108. User device 102 may be a smartphone, tablet, laptop, wearable device, or other device.

[0017] Social media platform 104 may be operated by a first-party social media platform provider or other first-party entity with the ability to verify user-generated content. Social media platform 106 may be operated by a third-party social media platform provider or other third-party entity with the ability to verify user-generated content (e.g., the owner of a messaging application). Social media platform 104 or social media platform 106 may run on one or more devices (e.g., servers). User device 102 may include an authorized social media application 120, which may be operated by a first-party entity, which may also be referred to as the primary entity (e.g., a first-party social media platform provider). For simplicity, as an example, social media platform 104 may be associated with a first-party entity, and social media platform 106 may be associated with a third-party entity.

[0018] Figure 1B An example user device 102 associated with dynamic social media content aggregation is shown. As illustrated, user device 102 may include an application 120 that can be displayed. Content feeds 118 with different slots (e.g., electronic social media news feeds), such as content slots 121, 122, 123, or 124, may exist. Content feed 118 may be an integrated content feed structure that provides intelligent slot allocation. Content slots may include first-party slots containing content from platforms owned by a primary entity (which may include different messaging applications) or third-party slots containing content from external messaging platforms. First-party slots may include content from different messaging applications (e.g., different types of social media platforms). Content slots 121, 122, 123, or 124 may include first-party organic content, third-party organic content, or sponsored content, etc. Organic content in the social media context may include user-generated posts, personal stories, shared photos, or natural interactions between users that do not have a promotional intent.

[0019] In one example, content slots 121 and 122 can receive content from a first-party social media platform 104 owned by the primary entity. The first-party social media platform 104 can provide direct application programming interface (API) access, allowing for efficient content retrieval or real-time updates. Content slot 123 can be associated with a third-party social media platform 106 and can integrate content from external platforms via collaboration agreements or public APIs, which can extend the available content viewed by application 120 while maintaining performance. Application 120 can be a standalone application or integrated into a social media platform application.

[0020] First-party social media platform 104 can determine the optimal slot layout based on multiple factors associated with the user profile of user device 102. For example, first-party social media platform 104 can achieve comprehensive engagement metric collection and processing through its technical architecture. First-party social media platform 104 can deploy a distributed event collection mechanism on user device 102 or computing components. Client listeners can collect detailed interaction data, including viewport crossover events, scroll position changes, touch and click interactions, media playback status, focus and blur events, or network status changes. These events can be processed through a buffering system that enables event batching, local storage for offline operations, bandwidth-optimized compression, priority queuing of critical events, duplicate filtering, or timestamp normalization.

[0021] First-party social media platform 104 can apply processed metrics through a rating pipeline that determines engagement probability scores for content items. The rating system can consider the current context, including time of day, user device type, network conditions, recent interaction history, and platform performance. Rating weights can be dynamically adjusted based on content freshness, user relationship strength, historical performance patterns, or platform diversity requirements.

[0022] First-party social media platforms can adaptively implement either supervised or unsupervised learning methods to improve their content delivery strategies. Supervised learning can utilize explicitly tracked engagement signals, while unsupervised learning can identify urgent patterns in user behavior. This approach can be a dual approach, capable of optimizing known engagement patterns while remaining responsive to emerging trends in content consumption behavior.

[0023] Figure 2 An example method 300 for content aggregation and delivery disclosed herein is illustrated. In step 310, user device 102 may receive a user query. Natural language processing may extract intent parameters, including the requested content type, time constraints, source preferences, relational filters, or classification criteria, etc.

[0024] In step 320, a platform can be selected based on the extracted parameters. User device 102 (or other associated device) can identify relevant content sources, verify platform availability, determine retrieval strategies, allocate computing resources, or initialize platform connections, etc.

[0025] In step 330, content retrieval can be performed. In step 340, feed generation can be performed using an artificial intelligence engine. The artificial intelligence engine can process the retrieved content by calculating relevance scores for content items, applying user preference filters, determining optimal slot allocation, generating descriptive metadata (presentation metadata), preparing sponsored content insertion points, or creating content preview thumbnails.

[0026] In step 350, the content delivery can be prepared for display by the system. This system can format the integrated feed structure, optimize media resources for the target device, generate a progressive loading list, establish update monitoring channels, initialize client cache instructions, or create rollback content versions, etc.

[0027] Figure 3 An example method 360 for aggregating and presenting social media content disclosed herein is illustrated. In step 362, a query associated with the social media content may be received. This query may include various parameters, such as keywords, tags, user handles, content categories, or time constraints defining the desired content retrieval scope.

[0028] In step 364, social media content associated with multiple messaging platforms (e.g., social media platform 104 or social media platform 106) can be retrieved based on a query. The retrieval process may involve platform interaction protocols, whereby the system can authenticate with at least one of the multiple social media platforms using stored user credentials maintained in a secure credential repository. During this authentication process, the system can identify available content types on the respective platforms, which may include, but are not limited to, text posts, images, videos, stories, or interactive media elements. The system may also consider rate limiting constraints, application programming interface (API) requirements, or platform-specific content formatting specifications to determine content retrieval parameters for each platform.

[0029] In step 366, an integrated content feed can be determined based on social media content from multiple social media platforms. This determination process may include using machine learning algorithm 132 to analyze the content relevance scores of the retrieved social media content, filtering content based on user engagement metrics stored in engagement database 134, or organizing content based on temporal relationships to help ensure a coherent narrative. The system may employ natural language processing or computer vision techniques to understand the content context and help ensure appropriate content categorization.

[0030] In step 368, instructions may be sent to distribute the integrated content feed into multiple content slots. These content slots may include first-party content slots and third-party content slots. First-party content slots include first-party platform content from platforms owned by a first entity (e.g., social media platform 104), and third-party content slots include third-party platform content from platforms owned by one or more other entities (e.g., social media platform 104). The distribution process may involve allocating a first portion of the multiple content slots to first-party platform content and a second portion of the multiple content slots to third-party platform content. This allocation may be dynamically adjusted based on real-time engagement metrics or platform performance data.

[0031] In step 370, sponsored content can be strategically inserted between content slots in the integrated content feed. Sponsored content placement can be prioritized (e.g., optimized). The optimal placement within the integrated content feed can be based on historical engagement data or advertiser requirements. This system can distribute sponsored content across multiple content slots based on user engagement metrics, which can facilitate organic content flow while maximizing advertising effectiveness.

[0032] In step 372, an integrated content feed can be provided to user device 102 via a content delivery network (e.g., a social media network). The system can continuously track user engagement with the integrated content feed, update user preference data based on the tracked user engagement, and adjust future content retrieval based on the updated user preference data.

[0033] The system can use machine learning models trained on user interaction data to optimize content selection and placement. Technological improvements include reduced app switching overhead, enhanced content relevance, and optimized placement of sponsored content.

[0034] In one example embodiment, a method may include: determining a user identifier associated with a first application by one or more computer processors coupled to memory; determining, through the first application, a request for first content from a second application and second content from a third application; determining that the first user identifier is associated with a second user identifier and a third user identifier, wherein the second user identifier is associated with the second application and the third user identifier is associated with the third application; automatically retrieving the first content by interacting with the second application; automatically retrieving the second content by interacting with the third application; and causing the first and second content to be presented in the user interface of the first application. The one or more computer processors coupled to memory may be configured to execute an AI agent to interact with the second or third application. The first and second content may be presented without opening the second or third application. The request may be automatically determined when the user identifier opens the first application on a mobile device.

[0035] Figure 4 A framework 600 associated with machine learning and / or artificial intelligence (AI) is shown. Framework 600 can be hosted remotely. Alternatively, framework 600 can be located at... Figure 1A The system 100 shown is within the system and can be processed / implemented by a device. In some examples, the machine learning model 610 (also referred to herein as artificial intelligence model 610) can be implemented / executed by a network device (e.g., a server). In other examples, the machine learning model 610 can be implemented / executed by other devices (e.g., user device 102). The machine learning model 610 can be operatively coupled to training data stored in a training database 603 (e.g., a data repository). In some examples, the machine learning model 610 can be associated with other operations. The machine learning model 610 can be one or more machine learning models.

[0036] In another example, training data 620 may include attributes of thousands of objects. Attributes may include, but are not limited to, the size, shape, orientation, position, etc. of one or more objects. The training data 620 used by the machine learning model 610 may be static or periodically updated. Alternatively, the training data 620 may be updated in real time based on evaluations performed by the machine learning model 610 in non-training mode. This is illustrated by the double-sided arrows connecting the machine learning model 610 and the stored training data 620.

[0037] Machine learning model 610 can be designed in part to generate an integrated content feed associated with one or more received inputs by utilizing determined contextual information. This information includes fields such as descriptions, defined variables, data categories associated with the variables and outputs (e.g., the integrated content feed), and responses to generated prompts. Machine learning model 610 can be a large language model to generate representations (e.g., vector spaces) or embeddings of one or more of the received inputs. These machine learning models 610 can be trained (e.g., pre-trained and / or real-time trained) on: large amounts of textual data (e.g., data associated with one or more inputs); previous responses generated to one or more prompts; previously generated content feeds; or extensive data acquisition of language patterns and semantic meanings. Machine learning model 610 can understand and represent the context of words, terms, and / or phrases in a high-dimensional space, effectively acquiring / determining semantic similarities between different received inputs (including descriptions and responses to prompts), even if they are not identical.

[0038] Typically, such determination of some existing systems may require a large amount of manual annotation and / or brute-force computer-based annotation to obtain training data within a supervised training framework. However, the example aspects of this disclosure can deploy one or more machine learning models (e.g., machine learning model 610), which can be flexible, adaptive, automatic, ad hoc, fast-learning, and trainable. Due to the learning framework aspect of this disclosure, which can be implemented by machine learning model 610, manual or brute-force operation may be unnecessary for the examples of this disclosure. This makes one or more user inputs, one or more queries, or other aspects of the examples of this disclosure flexible and scalable for billions of users on network devices and their associated communication devices.

[0039] Figure 5 An example computer system 700 is illustrated. In the example, one or more computer systems 700 perform one or more steps of one or more methods described or illustrated herein. In a particular embodiment, one or more computer systems 700 (e.g., user device 102, social media platform 106, or social media platform 104) provide the functionality described or illustrated herein. In the example, software running on one or more computer systems 700 performs one or more steps of one or more methods described or illustrated herein, or provides the functionality described or illustrated herein. The example may include one or more portions of one or more computer systems 700. Throughout this document, references to computer systems may include computing devices, and vice versa, where appropriate. Furthermore, references to computer systems may include one or more computer systems, where appropriate.

[0040] This disclosure contemplates any suitable number of computer systems 700. This disclosure contemplates computer systems 700 employing any suitable physical form. By way of example and not limitation, computer system 700 may be an embedded computer system, a system-on-chip (SOC), a single-board computer system (SBC) (e.g., a computer-on-module (COM) or system-on-module (SOM)), a desktop computer system, a laptop or notebook computer system, an interactive self-service machine, a mainframe, a network of computer systems, a mobile phone, a personal digital assistant (PDA), a server, a tablet computer system, or a combination of two or more of these computer systems. Where appropriate, computer system 700 may include one or more computer systems 700; computer system 700 may be single or distributed; spanning multiple locations; spanning multiple machines; spanning multiple data centers; or located in the cloud (which may include one or more cloud components in one or more networks). Where appropriate, one or more computer systems 700 can perform one or more steps of the methods described or illustrated herein without significant space or time constraints. By way of example and not limitation, one or more computer systems 700 can perform one or more steps of the methods described or illustrated herein in real time or in batch processing mode. Where appropriate, one or more computer systems 700 can perform one or more steps of the methods described or illustrated herein at different times or in different locations.

[0041] In the example, computer system 700 includes processor 702, memory 704, storage device 706, input / output (I / O) interface 708, communication interface 710, and bus 712 (e.g., communication bus 103). Although this disclosure describes and illustrates a particular computer system having a particular number of particular components in a particular arrangement, this disclosure contemplates any suitable computer system having any suitable number of any suitable components in any suitable arrangement.

[0042] In the example, processor 702 includes hardware for executing instructions, such as those that constitute a computer program. By way of example, and not limitation, to execute instructions, processor 702 may retrieve (or read) these instructions from internal registers, internal cache, memory 704, or storage device 706; decode and execute these instructions; and subsequently write one or more results to internal registers, internal cache, memory 704, or storage device 706. In a particular embodiment, processor 702 may include one or more internal caches for data, instructions, or addresses. Where appropriate, this disclosure contemplates processor 702 including any suitable number of suitable internal caches. By way of example, and not limitation, processor 702 may include one or more instruction caches, one or more data caches, and one or more page table caches (TLBs). Instructions in the instruction cache may be copies of instructions in memory 704 or storage device 706, and the instruction cache may accelerate the retrieval of these instructions by processor 702. The data in the data cache may be a copy of the data in memory 704 or storage device 706 for operation by instructions executed at processor 702; it may be the result of a previous instruction executed at processor 702 for access by subsequent instructions executed at processor 702 or for writing to memory 704 or storage device 706; or it may be other suitable data. The data cache can accelerate read or write operations of processor 702. The TLB can accelerate virtual address translation of processor 702. In a particular embodiment, processor 702 may include one or more internal registers for data, instructions, or addresses. Where appropriate, this disclosure contemplates that processor 702 may include any suitable number of suitable internal registers. Where appropriate, processor 702 may include one or more arithmetic logic units (ALUs); processor 702 may be a multi-core processor, or may include one or more processors 702. Although this disclosure describes and illustrates specific processors, this disclosure contemplates any suitable processor.

[0043] In the example, memory 704 includes main memory for storing instructions to be executed by processor 702 or data to be operated by processor 702. By way of example and not limitation, computer system 700 may load instructions from storage device 706 or another source (e.g., another computer system 700) into memory 704. Processor 702 may then load these instructions from memory 704 into internal registers or internal cache. To execute these instructions, processor 702 may retrieve and decode these instructions from internal registers or internal cache. During or after the execution of these instructions, processor 702 may write one or more results (which may be intermediate or final) to internal registers or internal cache. Processor 702 may then write one or more of these results to memory 704. In a particular embodiment, processor 702 executes only the instructions in one or more internal registers or internal cache or memory 704 (not storage device 706 or elsewhere) and operates only on the data in one or more internal registers or internal cache or memory 704 (not storage device 706 or elsewhere). One or more memory buses (each of which may include an address bus and a data bus) couple processor 702 to memory 704. As described below, bus 712 may include one or more memory buses. In the example, one or more memory management units (MMUs) are located between processor 702 and memory 704 and facilitate access to memory 704 requested by processor 702. In a particular embodiment, memory 704 includes random access memory (RAM). Where appropriate, the RAM is volatile memory. Where appropriate, the RAM may be dynamic RAM (DRAM) or static RAM (SRAM). Furthermore, where appropriate, the RAM may be single-port RAM or multi-port RAM. This disclosure contemplates any suitable RAM. Where appropriate, memory 704 may include one or more memories 704. Although this disclosure describes and illustrates specific memories, this disclosure contemplates any suitable memory.

[0044] In the example, storage device 706 includes a mass storage device for data or instructions. By way of example and not limitation, storage device 706 may include a hard disk drive (HDD), a floppy disk drive (FDD), flash memory, optical disk, magneto-optical disk, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these storage devices. Where appropriate, storage device 706 may include removable or non-removable (or fixed) media. Where appropriate, storage device 706 may be internal or external to computer system 700. In the example, storage device 706 is a non-volatile solid-state memory. In a particular embodiment, storage device 706 includes read-only memory (ROM). Where appropriate, the ROM may be a mask-programmable ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM), or flash memory, or a combination of two or more of these ROMs. This disclosure contemplates a mass storage device 706 in any suitable physical form. Where appropriate, storage device 706 may include one or more storage device control units facilitating communication between processor 702 and storage device 706. Where appropriate, storage device 706 may include one or more storage devices 706. Although this disclosure describes and illustrates specific storage devices, this disclosure contemplates any suitable storage device.

[0045] In the example, I / O interface 708 includes hardware, software, or both, providing one or more interfaces for communication between computer system 700 and one or more I / O devices. Where appropriate, computer system 700 may include one or more of these I / O devices. One or more of these I / O devices may enable communication between a person and computer system 700. By way of example and not limitation, I / O devices may include a keyboard, keypad, microphone, monitor, mouse, printer, scanner, speaker, still camera, stylus, tablet computer, touchscreen, trackball, camera, another suitable I / O device, or a combination of two or more of these I / O devices. I / O devices may include one or more sensors. This disclosure contemplates any suitable I / O device and any suitable I / O interface 708 for that I / O device. Where appropriate, I / O interface 708 may include one or more devices or software drivers that enable processor 702 to drive one or more of these I / O devices. Where appropriate, I / O interface 708 may include one or more I / O interfaces 708. Although this disclosure describes and illustrates specific I / O interfaces, this disclosure considers any suitable I / O interface.

[0046] In the example, communication interface 710 includes hardware, software, or both that provide one or more interfaces for communication (e.g., packet-based communication) between computer system 700 and one or more other computer systems 700 or one or more networks. By way of example, and not limitation, communication interface 710 may include a network interface controller (NIC) or network adapter for communicating with Ethernet or other wire-based networks, or a wireless NIC (WNIC) or wireless adapter for communicating with wireless networks such as Wi-Fi networks. This disclosure contemplates any suitable network and any suitable communication interface 710 for that network. By way of example, and not limitation, computer system 700 may communicate with one or more portions of ad hoc networks, personal area networks (PANs), local area networks (LANs), wide area networks (WANs), metropolitan area networks (MANs), or the Internet, or a combination of two or more of these networks. One or more portions of one or more of these networks may be wired or wireless. As an example, computer system 700 may communicate with networks such as wireless PAN (WPAN) (e.g., Bluetooth WPAN), Wi-Fi networks, Wi-Fi Max networks, cellular telephone networks (e.g., Global System for Mobile Communications (GSM) networks), or other suitable wireless networks, or combinations of two or more of these networks. Where appropriate, computer system 700 may include any suitable communication interface 710 for any of these networks. Where appropriate, communication interface 710 may include one or more communication interfaces 710. Although specific communication interfaces are described and shown in this disclosure, any suitable communication interface is contemplated in this disclosure.

[0047] In a particular embodiment, bus 712 includes hardware, software, or both hardware and software that couple the components of computer system 700 to each other. By way of example and not limitation, bus 712 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a front-side bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infiniband interconnect, a low-pin-count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCIe) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association local (VLB) bus, or another suitable bus, or a combination of two or more of these buses. Where appropriate, bus 712 may include one or more buses 712. Although this disclosure describes and illustrates a particular bus, this disclosure considers any suitable bus or interconnect.

[0048] In this document, where appropriate, one or more computer-readable non-transitory storage media may include: one or more semiconductor-based integrated circuits (ICs) or other integrated circuits (e.g., field-programmable gate arrays (FPGAs) or application-specific ICs (ASICs)), hard disk drives (HDDs), hybrid hard disk drives (HHDs), optical disc drives (ODDs), magneto-optical disk drives (ODDs), floppy disk drives (FDDs), magnetic tape, solid-state drives (SSDs), RAM drives, secure digital cards or drives, any other suitable computer-readable non-transitory storage media, computer-readable media, or any suitable combination of two or more of these. Where appropriate, computer-readable non-transitory storage media may be volatile, non-volatile, or a combination of volatile and non-volatile.

[0049] In this document, unless otherwise expressly stated or indicated by the context, "or" is inclusive rather than exclusive. Therefore, in this document, unless otherwise expressly stated or indicated by the context, "A or B" means "A, B, or both." Furthermore, unless otherwise expressly stated or indicated by the context, "and" is both common and respective. Therefore, in this document, unless otherwise expressly stated or indicated by the context, "A and B" means "A and B, commonly or respective."

[0050] While the disclosed system has been described with reference to various examples in conjunction with the accompanying drawings, it should be understood that other similar implementations may be used in addition to those disclosed herein, or modifications and additions may be made to the described examples of AI-based unconstrained social media platforms. For example, those skilled in the art will recognize that AI-based unconstrained social media, in addition to what is disclosed herein, can be applied to any environment, whether wired or wireless, and can be applied to any number of such devices connected and interacting across communication networks. Therefore, the disclosed system described herein should not be limited to any single example, but should be interpreted in accordance with the breadth and scope of the appended claims.

[0051] In describing preferred methods, systems, or apparatuses for verifying the authenticity of media content, as shown in the figures, specific terminology is used for clarity. However, the claimed subject matter is not limited to the specific terminology chosen. Furthermore, as used in the specification including the appended claims, the singular forms “a,” “an,” and “the” include the plural, and references to a particular numerical value include at least that particular value, unless the context clearly indicates otherwise. The term “a plurality” as used herein means more than one. Another example of indicating a range of values ​​includes from one particular value and / or to another particular value. Similarly, when a value is expressed as an approximation, the use of the antecedent “about” will be understood to indicate that the particular value forms another embodiment. All ranges are inclusive and composable. It should be understood that the terminology used herein is for descriptive purposes only and is not intended to be limiting.

[0052] This written description uses examples to enable any person skilled in the art to practice the claimed subject matter, including making and using any device or system and performing any combined methods. Other variations of the examples are also considered herein. It should be appreciated that, for clarity, certain features of the disclosed subject matter described herein in the context of individual embodiments may also be provided in combination in a single embodiment. Conversely, for brevity, various features of the disclosed subject matter described herein in the context of a single embodiment may also be provided individually or in any sub-combination. Furthermore, any reference to values ​​described in the scope includes every value within that scope. Any document referenced herein for any and all purposes is incorporated herein by reference in its entirety.

[0053] The scope of this disclosure covers all changes, substitutions, variations, alterations, and modifications to the exemplary embodiments described or illustrated herein, which will be understood by those skilled in the art. The scope of this disclosure is not limited to the examples described or illustrated herein. Furthermore, although this disclosure describes and illustrates various embodiments including specific components, elements, features, functions, operations, or steps, any embodiment in these embodiments may include any combination or arrangement of any component, element, feature, function, operation, or step described or illustrated anywhere herein as will be understood by those skilled in the art. Furthermore, the device or system or its components mentioned in the appended claims being adapted, arranged, enabled, configured, activated, operable, or operable to perform a specific function includes the device, system, or component, whether or not it or the specific function is activated, turned on, or unlocked, provided that the device, system, or component is so adapted, arranged, enabled, configured, activated, operable, or operable. Moreover, although this disclosure describes or illustrates specific embodiments to provide specific advantages, specific embodiments may not provide these advantages, provide some of these advantages, or provide all of these advantages.

Claims

1. A method comprising: Queries related to social media content are received via one or more computer processors coupled to memory; Based on the query, retrieve social media content associated with multiple social media platforms; The integrated content feed is determined based on the social media content from the multiple social media platforms; as well as Instructions are sent to distribute the integrated content feed into a plurality of content slots, wherein the plurality of content slots include: a first-party content slot comprising first-party platform content from a platform owned by a first entity; and a third-party content slot comprising third-party platform content from one or more other platforms owned by other entities.

2. The method according to claim 1 further includes: Provide the integrated content feed to the user's computing device.

3. The method according to claim 1 further includes: Sponsored content is inserted between the multiple content slots in the integrated content feed; as well as Provide the integrated content feed to the user's computing device.

4. The method according to claim 1, wherein, The retrieval of the social media content is based on access to the plurality of social media platforms, wherein the access includes: Use stored user credentials to verify with at least one of the plurality of social media platforms; Identify the content types available on at least one of the plurality of social media platforms; and Determine the content retrieval parameters for at least one of the plurality of social media platforms.

5. The method according to claim 1, wherein, Determining the integrated content feed includes: Analyze the content relevance scores of the retrieved social media content; Filtering content based on user engagement metrics; and The content is organized according to time relationships.

6. The method according to claim 1, wherein, The integrated content feed arrangement includes: The first portion of the plurality of content slots is assigned to the content of the first-party platform. The second portion of the plurality of content slots is allocated to the content of the third-party platform; and Sponsored content is distributed across the multiple content slots based on user engagement metrics.

7. The method according to claim 1 further includes: Track user engagement with the integrated content feed; Update user preference data based on the tracked user engagement; as well as Adjust future content retrieval based on the updated user preference data.

8. The method according to claim 1 further includes: Receive sponsored content from one or more advertisers; Determine the optimal placement position in the integrated content feed; as well as Insert the sponsored content into the determined optimal placement location.

9. The method according to claim 1, wherein, The first-party content slot includes content from a first subset of multiple social media platforms owned by the social media company, and the third-party content slot includes content from external social media platforms.

10. A method comprising: Querying for social media content is received through artificial intelligence (AI) computing devices; The AI ​​computing device responds to the query to access multiple social media platforms; Based on the query, retrieve social media content from the multiple social media platforms; The AI ​​computing device generates an integrated content feed, which includes content from the multiple social media platforms. The integrated content feed is distributed across multiple content slots, including: first-party content slots, which include content from a platform owned by a first entity; and third-party content slots, which include content from platforms owned by other entities. Sponsored content is inserted between the multiple content slots in the integrated content feed; Provide the integrated content feed to the user's computing device.

11. The method according to claim 10, wherein, Access to the aforementioned social media platforms includes: Verify each platform using stored user credentials; Identify the types of content available on each platform; and Determine the content retrieval parameters for each platform.

12. The method according to claim 10, wherein, Generating the integrated content feed includes: Analyze the content relevance scores of the retrieved social media content; Filtering content based on user engagement metrics; and The content is organized according to time relationships.

13. The method according to claim 10, wherein, The integrated content feed arrangement includes: The first portion of the plurality of content slots is assigned to content from a first-party platform. The second portion of the plurality of content slots is allocated to content from a third-party platform; and Sponsored content is distributed across the multiple content slots based on engagement metrics.

14. The method according to claim 10 further includes: Track user engagement with the integrated content feed; Update user preference data based on the aforementioned user engagement; as well as Adjust future content retrieval based on the updated user preference data.

15. The method according to claim 10 further includes: Receive sponsored content from multiple advertisers; Determine the optimal placement position in the integrated content feed; as well as Insert the sponsored content at the determined optimal placement location.

16. The method of claim 10, wherein, The first-party content slot contains content from social media platforms owned by social media companies, and the third-party content slot contains content from external social media platforms.

17. A method comprising: A user identifier associated with the first application is determined by one or more computer processors coupled to the memory; The first application determines the requests for first content from the second application and second content from the third application; The first user identifier is determined to be associated with a second user identifier and a third user identifier, wherein the second user identifier is associated with the second application and the third user identifier is associated with the third application; The first content is automatically retrieved by interacting with the second application; The second content is automatically retrieved by interacting with the third application; and This causes the first content and the second content to be displayed in the user interface of the first application.

18. The method according to claim 17, wherein, The one or more computer processors coupled to the memory are configured to execute an AI agent to interact with the second application or the third application.

19. The method of claim 17, wherein, The first and second content are presented without opening the second or third application.

20. The method of claim 17, wherein, The request is automatically determined when the user identifier is opened on the mobile device.

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