1159 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.

The disclosed embodiments include computer-implemented systems and processes that generate and provision, in real time, directed digital content based on decomposed structured messaging data. For example, an apparatus may receive a plurality of messages that characterize first data exchanges initiated between a first counterparty and second counterparties during a first temporal interval. Each of the messages includes elements of message data associated with a real-time payment requested from the first counterparty by a corresponding one of the second counterparties. Based on the elements of message data, that apparatus may predict an occurrence of a second exchange of data that involves the first counterparty during a second temporal interval, and may transmit notification data that includes product data characterizing an available product associated with the predicted occurrence of the second data exchange to a device operable by the first counterparty for presentation within a digital interface.

Apparatuses, systems, and techniques are presented to generate digital content. In at least one embodiment, one or more neural networks are used to generate a three-dimensional voxel representation of a scene based, at least in part, upon a plurality of two-dimensional images of the scene.

A digital content management system may include a set of one or more memory components and a set of one or more processing devices coupled to the set of one or more memory components. The set of one or more processing devices to perform operations comprising: obtaining one or more electronic signatures on a digital document to yield an electronically signed digital document, storing the electronically signed digital document at an off-chain address, using a trained machine learning model to create a smart contract, the smart contract comprising a reference to the off-chain address; and deploying the smart contract to a blockchain.

Systems, devices, and methods are provided for determining contextually relevant user-based product recommendations based on scene information. In at least one embodiment, techniques described herein may be used to determine, using a first machine-learning model, first information associated with a first object within a first image of digital content, determine, using a second machine-learning model, similarity scores between the first object and a first plurality of products of an online purchasing system, detect, in association with the first image of the digital content, performance of a first computer-based action by a user, determine, using a third machine-learning model and based on contextual data of the user, one or more affinity scores for the user, select a first product based on the one or more affinity scores, and present a recommendation to the user to perform a second computer-based action in association with the first product.

Computer-implemented techniques for multimodal content relevance prediction using neural networks involves processing multimodal content comprising a digital image and text. Initially, dense embeddings are obtained: an image embedding from a pretrained convolutional neural network, and a text embedding from a pretrained transformer network. These embeddings encapsulate the features of the image and text respectively. Two pretrained dense neural sub-networks then reduce the dimensionality of these embeddings. A third dense neural sub-network determines a numerical score for the multimodal content using the reduced embeddings and an additional feature embedding. This score reflects various aspects of the multimodal content, leading to an action taken based on this numerical evaluation, providing a comprehensive and nuanced understanding and management of multimodal digital content.

This invention introduces an adaptive system for multi-modal media processing and delivery, addressing challenges in modern digital content distribution. The technology dynamically analyzes and processes media content in real-time, optimizing delivery across diverse devices, networks, and content types. Key features include adaptive processing that adjusts compression, encoding, and delivery protocols based on content characteristics and delivery constraints. The system incorporates artificial intelligence for continuous improvement, learning from historical data and user feedback. It addresses network variability and device diversity, adapting to changing conditions and optimizing content for different platforms. Security and personalization features enable protected content distribution and tailored user experiences. The invention's cross-media optimization approach allows efficient handling of various media formats within a unified framework. Its scalable, modular design suits applications from consumer streaming to enterprise-level distribution. This comprehensive solution aims to enhance content distribution efficiency and user experience in the complex, evolving digital media landscape.

A computer-implemented method includes generating to a display device a graphical user interface (GUI), the display device being coupled to at least one processing device. A file-icon reception field is generated within the GUI. A command to move into the reception field an icon representing a digital text file containing a data set is received from a user. The digital file is accessed. A table of contents characterizing the data set is generated, one or more portions of the data set based on the table of contents are displayed in the GUI.

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.

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 present disclosure relates to systems, non-transitory computer-readable media, and methods for using a large language model and digital content items (e.g., digital files) stored in a content management system to generate workflow outputs for external computing platforms. For instance, in some embodiments, the disclosed systems receive a workflow request that includes a natural language description of an objective to be accomplished via a workflow. The disclosed systems determine an action plan for completing the workflow and executes the action plan by accessing digital content items (e.g., digital files) stored in a content management system and extracting or generating data from the contents of the digital content items. Further, the disclosed systems use a large language model to generate a workflow output that is provided to an external computing platform for use.

The invention discloses a digital content tracing system based on a block chain technology, and particularly relates to the technical field of block chains. The tampering risk of the digital content in the distributed storage process is evaluated by analyzing the multi-node data synchronization behavior; the transaction overstock risk judgment module evaluates the transaction overstock and local congestion hidden danger according to the transaction verification rate in the propagation process; and the reliability evaluation module judges the reliability of the digital content tracing chain in combination with the tampering risk and the local congestion hidden danger. The abnormal risk assessment module analyzes operation behavior abnormity through the intelligent contract log, and the conflict possibility assessment module detects consistency deviation of circulation records; and the potential risk assessment module synthesizes the multi-dimensional risk, generates a potential risk assessment result of the tracing chain, and determines whether to trigger an on-chain responsibility node tracing verification mechanism, so that potential hidden dangers in the tracing chain can be comprehensively identified, and an efficient and reliable solution is provided for block chain digital content tracing.

The disclosed computer-implemented methods and systems cause a second screen device to display custom-built layouts once the second screen device has been converted to a video game controller. For example, the disclosed methods and system convert the second screen device to a video game controller for a video game being supported by a digital content system and displayed on a separate first screen device. In response to detecting a trigger event, the disclosed methods and systems replace a standard layout on the second screen device with a custom-built layout. Where the standard layout is limited to standard video game controls at standard positions, the custom-built layout includes any type combination of customized video game controls, colors, graphics, animations, and so forth. Various other methods, systems, and computer-readable media are also disclosed.

The invention discloses an AI generated face image authenticity discrimination method and device and a storage medium, and belongs to the technical field of computer vision and artificial intelligence security. The invention provides a detection framework based on composite scaling optimization, dynamic enhancement and incremental learning collaboration, depth, width and resolution collaboration expansion of a network architecture is optimized through a composite scaling strategy, and the training intensity is dynamically adjusted in combination with an adaptive data enhancement technology. And an incremental learning mechanism is introduced to realize continuous adaptation and knowledge accumulation of the model to a novel generation technology, so that the detection robustness of a diversified generation technology is remarkably improved while the calculation complexity is reduced. Based on verification of an authoritative test set, the method is superior to an existing method in the aspects of detection precision, generalization ability and continuous adaptability, has a better model efficiency balance characteristic, and provides reliable technical support for digital content security verification.

In various embodiments, a computer-implemented method for generating digital content comprises generating a multiparty interface that communicates with at least a trained machine learning (ML) model, a first client device, and a second client device; combining at least a first input from the first client device and a second input from the second client device to generate a composite prompt, transmitting the composite prompt to the trained ML model for execution, receiving a digital content item from the trained ML model that was generated in response to the composite prompt, and displaying the digital content item in the multiparty interface.

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 for player modeling using captured multimedia data. A system can include one or more processors to receive, via an interface, digital content including at least one captured media segment corresponding with at least one player. The one or more processors can apply the digital content as input to at least one artificial intelligence (AI) model to generate a player profile including a sport-specific textual representation of the player corresponding to a plurality of performance parameters, and the AI model can be configured based at least on sport vernacular and historical labeled digital content of a sport of the player. The one or more processors can generate a structured data package including the player profile based on organizing at least a portion of the sport-specific textual representation and provide the structured data package to a digital application or a scouting computing system.

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.

The invention relates to the field of digital content security and artificial intelligence, and discloses an advertisement video badness-oriented fine-grained detection method based on a knowledge graph and a large language model, and the method comprises the steps: 1, collecting an advertisement video, and obtaining a knowledge graph of a badness-oriented advertisement video in a triple form through processing; 2, carrying out semantic segmentation on an advertisement video to be detected; step 3, extracting and processing audio emotion, vision and text information, and retrieving an associated violation mode sub-graph from the knowledge graph according to the advertisement type of the video to be detected; 4, the extracted information features and the knowledge graph sub-graphs in the step 3 are input into a large model, the large model outputs a bad video fine-grained analysis result, and a detection report is generated. According to the method, a specific bad guiding fragment is positioned to support fine-grained traceability, an evidence chain for omnibearing bad guiding detection is generated, and the detection accuracy is improved. The automatic detection of the bad guidance illegal advertisement is realized, the bad guidance illegal clues in the advertisement are accurately positioned, and the manual interpretation is assisted.

The invention discloses a multistage video watermark embedding and extracting method and system, and the method comprises the steps: carrying out the perception analysis of an original video through a space-time attention neural network, and dynamically dividing a time domain stable region and a space domain sensitive region; a three-level watermark system is constructed, the three-level watermark system comprises a basic-level watermark, a first-level enhanced watermark and a second-level enhanced watermark, the basic-level watermark adopts an anti-rotation binary matrix generated by a Fourier descriptor, the first-level enhanced watermark is a DCT frequency domain Hash sequence, and the second-level enhanced watermark is a semantic fingerprint generated based on a motion vector. According to the method, through multi-dimensional collaborative optimization of the anti-attack capability, the dynamic security mechanism and the verification efficiency, the core defects that erasing is easy due to insufficient reversibility in a traditional video watermarking technology and counterfeit affiliation chaos is caused by a static key are overcome, and reliable technical guarantee is provided for digital content copyright protection.

The invention provides an automatic content typesetting method based on AI recognition, and relates to the technical field of artificial intelligence and digital content process.The method comprises the steps that text data and image data are received, and the overall category, the element type and the spatial position in an image are recognized through a computer vision model; meanwhile, the unit type, importance and semantic association of the text are analyzed through a natural language processing model, a content relation graph fusing semantics and the spatial relation is constructed, constraint solving is conducted through a dynamic layout optimization engine on the basis of the content relation graph in combination with semantic association constraint, environment constraint and aesthetic index constraint, and the content relation graph is obtained. And an automatic typesetting scheme considering content relevance and visual aesthetic feeling is generated. According to the method, the problems of content relevance splitting and aesthetic defects in traditional typesetting are solved, and intelligent collaborative layout of multi-modal content is realized.

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 present disclosure relates to systems, non-transitory computer-readable media, and methods for capturing snapshots of digital content displayed on a client device and searching through the captured content. The disclosed systems provide a search function for effectively traveling back in time to identify digital content previously displayed on a client device. The disclosed systems provide options for capturing snapshots of content displayed on a display screen, extracting data from the snapshots, and storing the snapshots for use when populating search results. The disclosed systems utilize machine learning models to extract text and / or to generate text versions of snapshots including extracted text, descriptions of images, transcripts of videos, and / or textual summaries from displayed documents or webpages. In response to a search query, the disclosed systems can produce search results that include digital videos including captured snapshots of content displayed by a client device over time.

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.

In other embodiments, a method to perform image-based searching may include receiving a group of images from a content owner; creating an index using the group of images; receiving a further image from a mobile computing device of a user; determining a correlation between the further image and the index; and / or directing the mobile computing device of the user to content of the content owner, responsive to the determining of the correlation.

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.