3303results about "Program/content distribution protection" patented technology

In embodiments, systems and methods for improving machine-learning systems are disclosed. In embodiments, a system includes a data pool system that is configured to receive data from a plurality of different data sources and maintain a training data set that is used to train a specific machine-learning model based on the data from the plurality of different data sources. In embodiments, the system further includes a data scoring system that determines a data reliability score corresponding to the new data based on a set of intrinsic features of the new data and a data scoring model, wherein the data pool system selectively adds the new data to the training data set based on the reliability score of the new data. The system also includes a machine learning system that trains the specific machine-learning model based on the training data set.

A method for structuring the latent space of an autoencoder is provided. The method includes analyzing natural language descriptions related to input data; creating language-guided libraries that categorize and abstract data features based on the analyzed descriptions; mapping input data into the categorized and abstracted features within the latent space of the autoencoder; and training the autoencoder to minimize reconstruction loss while adhering to the structure imposed by the language-guided libraries.

The present disclosure relates to configured artificial intelligence methods and systems and related transportation systems and methods, including software-defined vehicles, for transportation systems using sensor and other data, and the integration of a transportation system with an AI convergence system of systems, providing a multi-layered system for intelligent automation and data-driven decision making across operational aspects of a transportation system.

The present invention discloses a system and method for secure artificial intelligence-based financial technology governance and risk management, designed to provide real-time, autonomous, and verifiable compliance assurance within digital financial ecosystems. The invention integrates a secure artificial intelligence processing unit, a governance control processor, a cryptographically anchored storage unit, a federated learning coordination processor, and a quantum-resistant communication interface enclosed within a tamper-proof hardware structure. The system performs encrypted machine learning computations on financial transaction data using homomorphic encryption and trusted execution environments to preserve confidentiality during analysis. It computes a governance risk index based on probabilistic inference and anomaly detection to identify regulatory deviations, applies adaptive compliance reasoning across multi-jurisdictional frameworks, and automatically enforces governance actions through secure decision logic.

The invention provides a cross-platform education data privacy protection analysis system, and relates to the technical field of data privacy protection. The cross-platform education data privacy protection analysis system comprises a multi-modal data acquisition unit, a dynamic privacy analysis engine, a federal learning processing core module, a block chain enhanced access control system, a privacy watermark traceability module and a compliance autonomy unit. Dynamic coupling of scene sensitivity and data protection intensity is realized through a dynamic privacy risk scoring equation and an adaptive differential privacy noise equation; the problems of privacy disclosure, compliance verification and traceability and responsibility investigation in cross-platform education data sharing are solved by combining federal learning, block chain evidence storage and privacy watermarking technologies. According to the method, the privacy risk assessment precision is remarkably improved, the model precision loss is reduced, full-life-cycle data security management and control are supported, and the method is suitable for multiple scenes such as K12 education and college educational administration.

The invention discloses a non-perpetual culture immersive interactive experience device and system based on a virtual digital human, and relates to the technical field of intangible cultural heritage intelligent interaction, and the device comprises a multi-mode sensing module which collects data such as user actions and voices through a depth camera and the like; the non-perpetual knowledge base module stores a non-perpetual knowledge graph and a case library; the digital human modeling engine fuses the inheritor characteristics and the user data to generate a virtual digital human; the immersion interaction engine constructs a cross-platform rendering environment to realize five-sense fusion experience; and the adaptive learning module analyzes the interaction sequence to optimize the digital human behavior, and all the modules cooperate to realize the intelligent interaction experience of non-genetic culture. Through the multi-modal perception module, the digital human modeling module and the like, the immersive interactive experience of the non-perpetual culture is realized, the user behavior can be accurately captured, the characteristic virtual digital human is generated, the five-sense fusion experience is provided, the content can be optimized based on the user interaction, the inheritance and propagation of the non-perpetual culture are promoted, and the participation degree and the sense of identity of the user are improved.

The invention discloses a large model generation content traceability technology based on model copyright ID watermark embedding, which comprises the following steps that: firstly, a large model content generator compiles a dynamic watermark embedding process into an arithmetic circuit, and generates a watermark text and a proof by using a zero-knowledge proof algorithm; then the large model content generator publishes the text with the watermark and the proof together, and hides the copyright ID and the secret key; a large model content user verifies the proof and the watermarked text through a smart contract by adopting a zero-knowledge verification algorithm; after verification is passed, an adversarial sample corresponding to the text with the watermark is generated, an adversarial training method is used for optimizing the text with the watermark, and parameters of the Viterbi balance algorithm are dynamically adjusted and improved. According to the method, the concealment and the generation quality are balanced, high-capacity watermark embedding is realized, the tamper resistance and the accurate traceability of multi-model copyright disputes are improved, and the privacy protection intensity is improved.

The invention discloses an open source software risk detection method and device based on an SBOM list, equipment and a medium, and relates to the technical field of software detection. The method comprises the following steps: acquiring an SBOM of target software, and analyzing component information of the SBOM; generating a feature character string according to the component information, and calculating a feature hash value through a hash algorithm; based on a known open source component, analyzing component information and vulnerability information of the component, calculating a feature hash value of the component, and storing the feature hash value to form a feature database; searching a matching item in a feature database based on the feature hash value of the target software component, and if the matching item is found, obtaining a license and vulnerability information of the target software component; if not, marking, manually judging and supplementing the data to the feature database; constructing a license compatibility matrix, and checking the compatibility of the obtained license and the overall license of the project; and comparing the acquired vulnerability information with a feature database, calculating vulnerability severity by combining a CVSS scoring model, and generating a priority repair report. According to the method, the software security detection speed and accuracy are improved.

The invention discloses a vehicle-mounted controller firmware upgrading method, which relates to the technical field of intelligent networked automobiles, and comprises the following steps of: accurately describing a dependency relationship between versions by introducing a directed acyclic graph, providing a clear topological sorting basis for an upgrading process, and improving the upgrading efficiency. The system function conflict and disorder caused by disordered upgrading sequence are fundamentally avoided; a distributed consensus protocol is utilized to ensure that each controller achieves state coordination before upgrading, so that the overall robustness of a multi-controller cluster is effectively improved when the multi-controller cluster faces network delay or individual node abnormity, and upgrading failure or version bifurcation caused by the fact that part of nodes are not ready is avoided; through the combination of the group-level binding token and the atomic switching mechanism, the controller group with a strong function dependency relationship can be ensured to be effective and updated as a unified transaction unit, and the safety and reliability of cross-domain function linkage are remarkably enhanced.

In some aspects, a component may generate an identification for a personal account based at least in part on user-asset heuristics, wherein the personal account is heuristically distinguished from an administrative account based at least in part on a number of users associated with an account that is either the personal account or the administrative account. The component may determine an owner identity of the owner associated with the personal account by linking the identification with an identity in a data source based at least in part on one or more heuristically determined identity attributes.

The invention relates to the field of watermarking algorithms, and provides a watermarking method and system for generating a text by a large language model. The method comprises the steps that a prompt text and a generated text sequence are input into a generation model, and an original logic score of a next mark position is generated through probability distribution calculation; performing semantic feature extraction on the generated text sequence by using an embedding model to obtain semantic embedding representation; converting the semantic embedding representation into a watermark logic score based on a pre-trained watermark model; and performing weighted combination on the original logic score and the watermark logic score to generate a final logic score, and outputting a watermark mark based on the final logic score. According to the method, the high robustness of the watermark is improved, and the safety of the watermark is also improved.

Herein disclosed is receiving predetermined content, receiving a request to transform the predetermined content into a derivative work, receiving a requested theme for the derivative work, using generative artificial intelligence to create the derivative work generated as a function of the predetermined content and the requested theme, determining if the generated derivative work is approved, in response to determining the generated derivative work is approved, applying a digital watermark to the approved derivative work, configuring an authorization server to govern use of the approved derivative work based on the digital watermark and providing user access to the authorized derivative work. The requested theme may be determined using a Large Language Model (LLM) and a chatbot interview. The generative artificial intelligence may comprise a diffusion model. The content may comprise music.

A blockchain-based system for managing music rights and royalties is disclosed. The system enables artists and contributors to upload music, assign revenue percentages, and track usage via smart contracts on a distributed ledger. Play counts are immutably recorded across distributor platforms. A contributor dashboard provides real-time access to royalty data. The platform incorporates Majority Reports to validate metadata matches and calculate royalties, and Minority Reports to flag discrepancies without payments, thereby ensuring data integrity. A Golden Box smart contract module replicates song rankings and applies predictive classification models to identify features associated with musical success. These mechanisms improve transparency, accuracy, and fraud prevention in royalty distribution.

The invention relates to the technical field of data integration, in particular to a multi-source data integration system. According to the technical scheme, the system comprises a plurality of heterogeneous data acquisition devices, a data processing module, a data storage module and a data transmission module. Differentiation processing is carried out on data of different devices, accurate time synchronization and data alignment are achieved through an NTP protocol and dynamic calibration, intelligent noise processing and data cleaning are carried out through multiple algorithms, compression encryption is adopted in storage, and a double-queue priority and breakpoint resume mechanism is applied in transmission. And fault monitoring and automatic processing are realized by means of hierarchical log recording and intelligent analysis, so that the problems of data isomerism, time synchronization, noise interference, storage and transmission efficiency, system reliability and the like are efficiently solved, and efficient, stable and safe data support is provided for a real-time monitoring platform.

The invention is suitable for the field of digital watermarking, and provides an anti-compression coding robust video watermark generation method based on an adversarial neural network, and the method comprises the steps: constructing an MSCA-GAN model; the model comprises an encoder, a decoder, a distortion layer, a discriminator network and an opponent network, and the specific steps are as follows: step S1, the encoder extracts different scale features of a video frame by using a multi-scale convolution attention mechanism, calculates attention weights, efficiently hides and embeds binary watermark information into the video, generates a watermark-containing video, and transmits the watermark-containing video to the decoder; performing confrontation optimization on an embedding strategy with a discriminator in training; according to the method, for H.264 compression layer special training, the multi-scale convolution attention mechanism and the depth separable convolution are combined, and the anti-compression robustness of the watermark under the H.264 standard is effectively improved; through common attack training such as noise layer simulation cutting and zooming, the watermark can still keep high extraction accuracy and robustness in a complex environment.

The invention discloses a knowledge graph-based software material consistency automatic checking method, which comprises the following steps of: collecting and processing a software declaration material to form a standardized original material set; forming an initial entity set and a candidate relation set based on the normalized original material set; constructing a heterogeneous knowledge graph based on the initial entity set and the candidate relationship set; executing cross-modal semantic anchoring on the heterogeneous knowledge graph to generate a semantic anchoring mapping table and an anchoring confidence value set; generating an enhanced constraint graph based on the semantic anchoring mapping table and the anchoring confidence value set; outputting a consistency detection result set and a minimum conflict subgraph set based on the heterogeneous knowledge graph and the enhanced constraint graph; and based on the consistency detection result set and the minimum conflict subgraph set, generating a difference check report and a repair suggestion set. According to the method, heterogeneous knowledge graph construction and semantic anchoring are adopted, and automatic consistency checking of the soft material is realized.

Systems and methods are described for tool discovery and ingestion for artificial intelligence (“AI”) agents. An AI platform can discover a first tool specification that includes an action and a description. The first tool specification is ingested to create a first tool object. The first tool object includes the action and an endpoint. Tool labels are determined from the specification and applied to the first tool object. A user interface displays the tool object, and it is added to an AI agent. The AI agent includes a manifest file that is used to execute the AI agent. This includes determining whether the AI agent is authorized to perform the action, and providing the AI agent with access to a tool credential, wherein the tool credential is sent to the endpoint.

The invention discloses a privacy protection method, system and device for block chain network node audit management, and the method specifically comprises the steps: generating a node anonymous voucher for a candidate node, carrying out the privacy authentication, mapping the sensitive information of the authenticated candidate node into an irreversible cryptographic hash value, and storing the irreversible cryptographic hash value in a distributed key management node; constructing a dynamic credible auditing group, selecting verification nodes from the candidate nodes, and performing uplink evidence storage after BLS aggregation signature; performing privacy aggregation calculation on the node behavior data of the verification node to generate an audit score ciphertext; fine-grained access control is realized through attribute-based encryption, and a node reputation level is dynamically bound through an auditing strategy; adaptive noise is added to an audit result by adopting a differential privacy mechanism, and the epsilon-differential privacy constraint condition is met by dynamically adjusting the noise amount; in the reward and punishment execution stage, a ring signature and a stealth address technology are combined, so that reward and punishment records are unhooked from real identities of nodes, and meanwhile, the trade traceability is ensured.

The invention discloses a large model cue word intelligent design and verification system supporting multi-person cooperation, which designs cue words in a manner of cue word project management and multi-person cooperation to realize specialized design of the cue words. The difficulty of cue word design is simplified by using AI aided design; the operation stability and accuracy of cue words in different models and different computing power are verified through single-point testing and batch testing, so that cue word verification and iteration form a closed loop, and the cue word quality is improved; after the cue word is published, directly outputting an interface with an access key for external calling, and storing the interface as a cue word case into a cue word knowledge base; compared with an existing system and technology, the cue word version management difficulty is effectively reduced, the workload of cue word designers is reduced, the cue word quality is improved, and the risk of cue word asset loss is reduced.

Apparatus and associated methods relate to an artwork authenticity owner validation system. In an illustrative example, an artwork remote identification system (ARIS) may include an artwork companion chip (ACC) and a certificate of authenticity (COA). The ACC, for example, may be physically attached to an artwork. The ACC and the COA, for example, may each include a unique identifier. For example, a centralized server may be configured to automatically authenticate an ownership of the artwork attached to the ACC by validating the ACC and the COA. For example, the user may scan the COA and then the ACC within a predetermined time limit to gain access to modify an ownership record in an artwork profile of the artwork. Various embodiments may advantageously allow a modification to the ownership information only when the centralized authentication server validates the COA and the ACC within the predetermined time period.

Systems, methods, and computer-readable storage media to protect tokens using a protection architecture. One method includes identifying asset tokens including links to a plurality of asset metadata objects. Further, the method includes generating a container metadata object including metadata of the asset tokens. Further, the method includes generating a container token including a link with the container metadata object. Further, the method includes encapsulating the container token and the asset tokens within a container including a container control structure restricting outputs of the container metadata object and the plurality of asset metadata objects. Further, the method includes generating an allocation token compatible with a segmented allocation control structure restricting outputs by the container of a first segmented allocation of the asset tokens based on metadata of a subset of the plurality of asset metadata objects. Further, the method includes providing, using the segmented allocation control structure, the allocation token.

The invention discloses a private network content copyright monitoring and evidence obtaining system based on AI and a large model, which utilizes AI and large model technologies to carry out copyright monitoring and evidence obtaining on multimedia content in a private network environment, and carries out deep semantic understanding and cross-modal feature extraction through a large model processor to generate unified semantic representation. The method comprises the steps that a copyright content database is established and stored in a copyright content knowledge base, the copyright content knowledge base is used for storing metadata of original content protected by copyright, unified semantic representation and copyright declarations in a natural language form provided by a copyright party, and the copyright declarations are converted into query vectors through a semantic understanding technology; the method comprises the following steps: acquiring a semantic representation of a multimedia content, performing similarity calculation with the semantic representation of the multimedia content, identifying infringement content, when the infringement content is identified, recording an original source, publishing time and publisher information of the infringement content, performing differentiation analysis, generating an evidence chain, displaying a copyright monitoring result through a user interface, generating infringement alarm information, and presenting details of the evidence chain.

The invention provides a large language model watermark embedding and detecting method and system based on entropy adaptive adjustment, and belongs to the technical field of large language models. The problems that an existing method is low in low-entropy text detection reliability and affects the text quality are solved. The method comprises the following steps: S1, calculating an entropy value of a token to be generated in real time through an entropy analysis module, and dynamically generating a watermark intensity control parameter; s2, adaptively adjusting a watermark embedding process according to the watermark intensity control parameter, and outputting a watermark text; and S3, calling a weight calculation module, and quantifying the confidence of the watermark signal in combination with the watermark intensity control parameter. The method is mainly used in the field of text source recognition.

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.

Systems and methods for preventing blockchain functions associated with malicious entities are disclosed herein. A first blockchain status update may be received from a first authorizing entity. A first counter and a first plurality of authorizing entities can be determined. The first counter and the first plurality of authorizing entities can be updated. The first counter can be compared to a consensus threshold. A first instruction to block requests for communications to the first entity address can be generated. A confirmation of the first instruction can be generated for display.

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.

The present disclosure provides a system for generating artist-governed merchandise derivative works, comprising at least one processor and memory storing instructions that, when executed, cause the system to receive a request associated with source content and a transformation theme for creating a merchandise derivative work, evaluate the request against pre-generation preference data associated with a content authority using a knowledge agent maintaining brand guidelines, generate the merchandise derivative work from the source content according to the transformation theme by applying a generative artificial-intelligence model through an action agent, perform post-generation validation against the brand guidelines, generate a digital identifier based on the merchandise derivative work, and enable distribution through a physical merchandise path for tangible products or a virtual merchandise path for digital assets.

The invention discloses a watermark generation detection method based on a stochastic smoothing testible robust large language model. The method comprises the following steps: training a watermark generator according to a lexical sequence data set; according to the prompt word text data set, the watermark generator and the large language model, generating texts with embedded watermarks and texts without embedded watermarks, and training a watermark detector; and converting a text into which a preset watermark is to be embedded, inputting the converted text into the trained watermark generator, outputting the text into which the watermark is embedded, processing the text into which the watermark is embedded by adopting a random smoothing-based mode, and inputting the processed text into the trained watermark detector for detection, thereby realizing watermark generation detection. According to the method, the watermark can still be reliably detected after the text is tampered, the anti-attack ability of the watermark is improved by introducing the random smoothing technology, the watermark detector has the cross-model generalization ability through the code conversion and training strategy, and efficient watermark embedding and detection can be achieved while the text generation quality is kept.

A method for managing and monetizing performance data for a plurality of users is disclosed. The method involves receiving a first data set related to user performance and / or behavioral information and establishing a first communication link for secure data transfer. The received first data sets, along with additional data from various sources, are stored in a central repository. The method ensures data integrity by verifying and validating its origin and authenticity. Advanced analytics are applied to extract actionable insights, trends, and structured information, enriched with metadata such as events, locations, timestamps, and user identities. A dynamic ontology is created to contextualize and categorize the structured data. When third parties request access, the method employs smart contracts to validate requests, securely transfer data, and compensate users. This approach ensures transparency, control, and equitable monetization of user-generated performance data.

The invention discloses a controllable text watermark embedding method based on a reinforcement learning strategy model, and the method comprises the steps: constructing a reinforcement learning strategy model, and controlling the token output of a large language model through the reinforcement learning strategy model; under the strategy control of the reinforcement learning strategy model, watermark embedding is carried out on the output text based on a watermark embedding strategy, and a candidate token sequence with hidden information is formed. According to the method, a policy network based on a reward function is introduced to perform token-level dynamic intervention on a large language model generation process, multiple embedding modes such as vocabulary replacement, grammar disturbance and structure addition are fused, and a watermark detector is used for providing a feedback signal to realize end-to-end closed-loop optimization; therefore, the naturalness, robustness and detectability of the text watermark are remarkably improved on the premise that the main structure of the language model is not modified, and the method has good deployment flexibility and traceability.