713 results about "Digital content" patented technology

A system and method for creating complex, immersive, and interactive digital content is disclosed. The system integrates advanced artificial intelligence, multi-modal input processing, cloud-based shared environments, and immersive hardware to generate, optimize, and deliver rich interactive experiences. The platform supports content mashups, custom scenario generation, and adaptive AI behaviors, enabling the creation of unique and engaging digital environments across various media formats.

Methods, systems, and non-transitory computer readable storage media are disclosed for controlling access to artificial intelligence systems based on determined intent of queries. The disclosed system utilizes one or more digital content analysis models to determine an intent of one or more queries to an artificial intelligence system. The disclosed system utilizes the one or more digital content analysis models to determine an intended use of the artificial intelligence system. Additionally, the disclosed system determines whether the intent of the one or more queries aligns with the intended use of the artificial intelligence system by generating a similarity score and comparing the similarity score to a similarity threshold. Based on whether the intent aligns with the intended use, the disclosed system executes computing instructions to enable or block the one or more queries from being processed by the artificial intelligence system.

An example may provide a prompt associated with a first state of a conversational system to a first machine learning model. The prompt may include at least one instruction to cause the first machine learning model to use at least the first state to generate at least one second state and reasoning, and use the reasoning to generate second output. The at least one second state may be generated by the first machine learning model using the first state. The reasoning may include an explanation of how the first machine learning model generated the at least one second state. The second output may be generated using the first machine learning model and the prompt. Options may be provided for presentation via the conversational system. The options may include digital content generated using the first machine learning model and the second output.

The invention belongs to the technical field of digital content security, and discloses a multi-mode AI content security risk traceability and identification detection system. Through obtaining multi-source heterogeneous data of content creation, editing, distribution and presentation of a full link, a content full link feature fingerprint is constructed, and cross-modal consistency analysis and link continuity verification are realized. According to the method, traceability map construction is introduced, potential tampering points are used as map nodes, content risks are quantified through tampering probability weights and risk transfer intensity, and tampering sources and propagation paths are accurately identified. The method has comprehensiveness and accuracy, deep analysis and risk assessment can be carried out on the multi-modal content, and the tampered content is effectively recognized and synthesized.

Systems and methods disclosed herein automatically authorize, audit, and manage usage of protected digital content via agentic artificial intelligence (AI) models. A data access / usage request is received (e.g., from a graphical user interface) that is associated with digital assets licensed from third parties. The system uses a first AI agent set to identify the digital content and retrieve corresponding access policies from a distributed database. The system uses a second AI agent set (same as or different from the first AI agent set) to evaluate the request against the retrieved policy to generate a permission set and / or settlement instructions. The system uses a third AI agent set (same as or different from the first and / or second AI agent sets) to embed digital watermarks and / or cryptographic signatures into the accessed content, and to record an audit trail of access, authorization, and / or settlement events in a distributed ledger or database.

Techniques are described, as implemented by computing devices, to control access to digital content through use of nonfungible tokens (NFTs). This is performed by leveraging a blockchain such that digital content associated with an item is made available to supplement use of the item (e.g., to supplement use of a physical item, digital content, and so forth) or make other functionality available based on a user's possession of the item.

A device may provide a prompt to a first machine learning model. The prompt may include at least one instruction to cause the first machine learning model to use at least first natural language input associated with a use of a conversational search system to rank data sources, generate a first search query and reasoning, and use the first search query and the reasoning to generate a second search query. The first search query may include data obtained from at least one of the ranked data sources. The reasoning may include an explanation of how the first machine learning model generated the first search query. A second machine learning model may synthesize a response determined via execution of the second search query. The synthesized response may be provided for presentation via the conversational search system.

The invention provides a distributed multi-source digital content tamper-proof Hash evidence storage method based on a block chain, and relates to the technical field of content tamper-proof, and the method comprises the steps: standardizing multi-source digital content, constructing an evolution pedigree tree, implanting a detector to capture change features, and tracking a propagation path; constructing a dynamic perceptual hash network to generate a dynamic hash value containing associated information; and verifying and generating an evidence storage record through a progressive consensus mechanism. According to the invention, efficient tamper-proof evidence storage of the multi-source digital content is realized, and the data traceability and verification efficiency are improved.

The invention relates to the technical field of text verification, in particular to an intelligent text verification method based on a hybrid model knowledge graph, which comprises the following steps of: analyzing a document, separating a text from a visual object, and generating semantics and visual vectors by using a bidirectional encoder and a hybrid visual model; performing form normalization verification by constructing a self-adaptive template matrix; judging the semantic homology of the image-text content by using a cross-modal gating arbiter; the text is converted into a semantic fact triple mapped to a unified space-time coordinate system, and logic irregularity is detected in a domain knowledge graph based on ontology constraint; and finally, summarizing all results to generate a structured verification report. According to the method, cross-modal semantic understanding and knowledge graph reasoning are effectively fused, full-dimension intelligent verification of content forms, image-text semantics and deep space-time causal logic is achieved, and the depth and accuracy of large-scale digital content verification are remarkably improved.

The invention discloses an AI-based digital advertisement material multi-modal adaptive generation method and system, and relates to the technical field of artificial intelligence and digital content, and the method comprises the steps: S1, obtaining historical operation records of a user in a material creation process, carrying out the classified storage of selection actions in each interaction, and obtaining a preliminary data set of the selection tendency of the user; s2, extracting style preference characteristics of the user in different creation scenes by adopting a behavior pattern analysis method according to the preliminary data set, and determining a dynamic change rule of the style preference of the user; s3, aiming at the dynamic change rule, constructing a dynamic capture mechanism, obtaining the latest selection tendency data, and judging the instant adjustment demand of the style preference of the user; according to the AI-based digital advertisement material multi-mode adaptive generation method and system, intelligence and individuation in the material creation process are realized, and a new technical scheme is provided for the field of creative design.

Embodiments of the disclosed technologies are capable of evaluating content recommendations. The embodiments describe creating a prompt using a search query and a content recommendation output by a machine learning model in response to the search query. The embodiments further describe causing a LLM to generate an evaluation of the content recommendation and the search query using the prompt. The evaluation includes a relevance score of the content recommendation and the search query. The embodiments further describe training the machine learning model to generate an updated content recommendation in response to the search query. The training includes using the relevance score of the content recommendation and the search query.

A device may provide a prompt to a first machine learning model. The prompt may include at least one instruction to cause the first machine learning model to use at least first natural language input associated with a use of a conversational search system to rank data sources, generate a first search query and reasoning, and use the first search query and the reasoning to generate a second search query. The first search query may include data obtained from at least one of the ranked data sources. The reasoning may include an explanation of how the first machine learning model generated the first search query. A second machine learning model may synthesize a response determined via execution of the second search query. The synthesized response may be provided for presentation via the conversational search system.

Embodiments of the described technologies determine input signals, where the input signals are specific to a user of the user network. The input signals are input to a set of artificial intelligence (AI) models. In response to the input signals, the first set of AI models output a first set of AI-derived signals relating to the input signals. At least one prompt template is applied to the first set of AI-derived signals to create at least one prompt. The at least one prompt is input to at least one generative AI model. In response to the at least one prompt, the at least one generative AI model outputs at least one thought starter machine-generated by the at least one generative AI model. The at least one thought starter includes digital content configured to be distributed via the user network.

A data processing system implements obtaining digital content as an output from the generative model; receiving a natural language prompt describing digital wrapping for the digital content, the digital wrapping to be presented to a recipient of the digital content; constructing a prompt based on the natural language prompt using the prompt construction unit; providing the second prompt to the generative model to cause the generative model to generate the digital wrapping; obtaining the digital wrapping as an output from the generative model; sending the digital content and the digital wrapping to a client device of a recipient; and causing the client device to present the digital wrapping on a second user interface of a client device and controls, which when activated, cause the client device to present an animation of the digital wrapping being removed and the digital content to be presented.

Systems and methods for real-time micro-licensing of digital content (e.g., real estate listings, real estate records, ads, articles) are provided that comprise evolving documents and which use blockchains of cryptographic technology to employ smart contracts for the automatic issuance of micro-licenses upon digital content distribution, aligning with user subscription criteria and dynamically adjusting upon digital content revisions. Instantaneous, secure transactions and access rights adjustments in real-time are facilitated as content updates occur, ensuring continuous alignment with user preferences and legal compliance through transparent, auditable, blockchain records.

The invention relates to a dynamic double-layer hidden watermark and encryption binding file protection method and system based on deep learning, and belongs to the technical field of digital content security. The problems of attack resistance, traceability obstruction and key-watermark unhooking in document cross-platform circulation are solved. According to the scheme, the method comprises the following steps of: extracting semantic fingerprints by using a sentence vector model Sentence-BERT; the fuzzy extractor generates a master key and derives a time key chain; the authentication encryption algorithm AEAD encrypts and binds the source and the timestamp load; container layer structure rearrangement and document layer zero-width character double embedding are carried out; the generative adversarial network or diffusion model adversarial training improves the optical character recognition and transcoding resistance; version binding and tracing are achieved through the watermark hash chain. The technical effects cover anti-counterfeiting migration, cross-layer fault-tolerant guarantee recoverability, rearrangement attack resistance, full-period accurate traceability and post-quantum security enhancement.

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support intelligent model selection for style-specific digital content generation. For example, a system that provides a digital content generation service may include a trained style detection model may receive reference digital content items from a user and extract a user style embedding that represents a style preference of the user. In some implementations, the reference digital content items may include text documents or images provided or selected by the user. The system may compare the user style embedding to a plurality of model style embeddings that each correspond to a respective generative artificial intelligence (AI) model to generate a ranked list of generative AI models. The system may access one or more highest ranked generative AI models from the ranked list to generate novel digital content based on a prompt from the user.

The present disclosure is directed toward systems, methods, and non-transitory computer readable media that provide a contextual content generation system that trains and implements a unique machine learning architecture to generate context-specific digital content items based on a digital guideline document. In particular, the disclosed systems select a content generation method from among prompt engineering and / or updating one or more machine learning models to generate digital content. For example, the disclosed systems utilize machine learning models to extract key elements from a digital guideline document comprising context-specific guidelines for digital content. Further, the disclosed systems generate an augmented prompt comprising indications of key elements from the digital guideline document. In addition, the disclosed systems select a content generation method from among prompt engineering and / or updating machine learning models to generate the digital content item which incorporates digital content corresponding to the context-specific guidelines based on the augmented prompt.

Embodiments of the disclosed technologies are capable of generating, using a machine learning model and a prompt, first content recommendations. The prompt comprises a search query and historic information associated with an entity. The first content recommendations are presented. The embodiments describe receiving a selection of a content recommendation of the first content recommendations. The embodiments describe generating, using the machine learning model and a second prompt, second content recommendations. The second prompt comprises a second search query and second historic information associated with the entity. The embodiments describe generating a ranked order of the second content recommendations using a history of entity interactions including the selection of the content recommendation of the first content recommendations. The embodiments describe determining context-aware recommendations by optimizing a permutation of the ranked order of the second content recommendations. The embodiments describe causing the context-aware recommendations to be presented.

A system and method for contextual three-dimensional messaging in augmented reality (AR) environments is disclosed. The system receives chat messages with specified real-world destinations and stores them associated with those locations. When a user wearing an AR device enters a destination location, the system detects their presence using techniques like GPS, Wi-Fi positioning, or computer vision. It then generates a 3D visual representation of the message and determines an appropriate spatial position within the physical environment based on environmental analysis and object detection. The 3D message is displayed at the determined position in the AR view. The system can analyze message content to identify topics and match them to detected real-world objects for contextual placement. Users can interact with displayed messages through gestures or voice commands to reply, forward, delete, or reposition messages. This enables immersive, location-aware messaging experiences that seamlessly blend digital content with the physical world.

Implementations are described herein for detecting deepfakes in digital media while preserving the privacy of the source computing device. In various implementations, sensor fingerprints and / or security tokens that signal software-introduced alterations, e.g., introduced by hardware abstraction layers (HALs) or virtual machines (VMs) may be utilized to detect such deepfakes. These signals may be used, separately and / or in combination, for various purposes, such as flagging digital content to a user as being a deepfake, preventing or blocking receipt and / or playback of digital content deemed to be a deepfake, allowing an end user to disable aspect(s) (e.g., layers) of digital content that are determined to be synthetic, etc.

The present invention generally relates to systems and methods for the efficient and secure creation of unique identifier associated with submitted URLs and the transmission and processing of encrypted signals by a receiving device, and in particular an audio transmission and processing of encrypted unique inaudible signals by a receiving device that may direct the receiving device to initiate an action, such as directing the receiving device to request an associated Uniform Resource Locator (URL) to then access a specific URL or other digital content through a browser application. The decoding and processing of the transmitted signal is operated on prior to the signal being played through the receiving devices speaker system(s). Consideration of the available signal bit rate is another aspect of the system's operational integrity.

A data processing system implements receiving a user prompt requesting a diagram representing digital content; constructing a prompt including the user prompt, the digital content, and instructions to a generative model to identify semantic context of the digital content, to identify a text data item, an audio data item, a video data item, and / or a structured file item embedded in the digital content to generate at least one of a text transcript of the audio / video / structure file item, and / or a text description of the audio / video / structure file item, to semantically analyze and extract diagram data from the text data item, the text transcripts, and / or the textual descriptions based on the semantic context, and to generate the diagram of the digital content based on the diagram data; providing the prompt to the generative model and receive the diagram; and providing the diagram to the client device for display.

A data processing system implements iteratively receiving a first prompt requesting a generative model to generate digital content, and subsequent prompt(s) requesting the model to further process the digital content; constructing a system prompt including the first prompt, the subsequent prompt(s), and instructions to the model to iteratively update the first prompt based on the subsequent prompt(s), and subsequently to generate the digital content based on a single updated first prompt; providing the system prompt to the model and receive the digital content; and providing the digital content to a client device. The system implements storing a prompt and a response generated by the model in a first application; causing the client device to present the prompt and the response in a read-only view; receiving a user selection to convert the response to editable; and converting the response to editable and inserting the editable response in a collaboration application.

The systems and methods disclosed herein embed and store traceable watermarks across multiple data modalities including text, image, audio, video, and structured data. The systems and methods disclosed herein receive an input dataset and generate unique watermark patterns for each data modality using cryptographic hash functions applied to dataset identifiers. The watermark patterns include parameters that provide instructions to watermark the input dataset. Watermarking includes modifying discrete data values, relationships, or arrangements within the original dataset at predetermined locations. The watermarked dataset is used to generate a hierarchical fingerprint structure including multiple levels of cryptographic hash identifiers that represent different portions of the data (e.g., at varying granularities). Both the watermark patterns and hierarchical fingerprints are stored in a distributed database accessible through a decentralized network, thus enabling authorized computing devices to verify data provenance and detect potential misappropriation across heterogeneous digital content types.

In implementations of systems for generating captions, a processing device implements a caption generation service to receive an input for caption generation that includes a text input indicating example language or content for the caption and an action input indicating a desired action. The processing device receives the text input via a user interface. The caption generation service generates a textual prompt for a machine-learning model based on the action input and text input. The machine-learning model uses the textual prompt to generate the caption in a specified structural format. The processing device then causes the generated caption to be presented to a user via the user interface.

Aspects of the disclosure are directed to text to image generative models fine-tuned to generate images that account for performance in addition to quality. For example, in a digital content domain, the generated images can be not only visually appealing but perform well as advertising assets, e.g., result in improved click through rate and / or conversion rate. Accounting for performance and quality can reduce processing cost and memory usage when generating images from text prompts, as the resolution of the image can be balanced with its function, allowing for reduced quality images that can still perform well.

Provided is a system and method for digital content generation and recommendation. A first plurality of stories are collected from various content sources over a network. A second plurality of stories are selected from the first plurality of stories such that a degree of similarity between any two stories of the second plurality of stories is below a determined threshold. A summary and a headline are generated for a first story of the second plurality of stories. A set of images are selected, from an image database, for the first story based on the generated summary and headline. A multimedia item including the summary, the headline, and a first image of the set of images is created. The multimedia item is presented on a lock screen interface of a user device, displaying the generated summary, the generated headline, and the first image on the lock screen interface.

The invention discloses a multi-dimensional collaborative counterfeit feature detection method for digital content, which comprises the following steps of: S1, receiving the digital content to be analyzed, performing format analysis and image extraction on the digital content, and performing standardization processing on the extracted image data; and S2, carrying out multi-dimensional atomic forgery feature extraction on the standardized image data, detecting potential forgery traces of each dimension, and outputting a preliminary analysis result of each dimension. The invention provides a multi-dimensional collaborative counterfeited feature detection method for digital contents, which integrates counterfeited indication information from various sources through multi-dimensional feature extraction and innovative collaborative analysis and context sensing mechanisms, and performs association analysis on the information in an innovative manner, so that the counterfeited counterfeited information is obtained. Therefore, the detection capability of digital content tampering and the interpretability of the result are effectively improved, the accuracy, robustness and interpretability of complex forgery detection are improved, and the feasibility of implementation is considered at the same time.

A system for data content feature extraction and evaluation using a hybrid quantum and classical neural network is disclosed. The system receives an input vector that represents digital content and generates a first quantum state vector for the input vector. The system generates a second quantum state vector by performing a quantum convolution operation on the first quantum state vector. The system initiates a second feature vector by mapping each quantum bit within the second quantum state vector to a respective numerical value. The system generates an output feature vector by performing a convolution operation on the second feature vector. The system evaluates the output feature vector by comparing the output feature vector with an expected vector. The system determines that the output feature vector corresponds to the expected vector. In response, the system determines that the output feature vector represents the digital content.