2880 results about "Identicon" patented technology

The invention discloses a high-precision instrument assembly fault backtracking method and system, belongs to the field of precision manufacturing, and aims to solve the problems that in a traditional backtracking method, assembly data are scattered and unreliable, fault root positioning is fuzzy, and new scene adaptation depends on a large amount of data. The method comprises the following steps: collecting assembly structured data, video images and environment data in a multi-source manner, filtering out low-quality images, and distributing unique identifiers for products; fusing the multi-modal data to generate a depth feature matrix; constructing an anomaly detection model to output a risk score and a label; hashing the data and then storing the data into a product exclusive private block chain; when a fault occurs, extracting data on the chain through a unique identifier, reconstructing an assembly process by using a graph neural network, and comparing a standard positioning root; and based on the fault report incremental training model, parameters are optimized in combination with meta-reinforcement learning. According to the method, the data authenticity is guaranteed, the fault backtracking precision and efficiency are improved, a new scene is quickly adapted, the production rework rate is reduced, and the stable assembly quality is maintained.

HyperText Markup Language (HTML) content analysis (HCA) using machine learning is described. A feature vector schema may be generated based on domain names corresponding to HTML webpages and corresponding indications of a status of the HTML webpage. The schema may map each position in a feature vector of a given HTML webpage to a resource identifier. Information may be processed using the schema to generate respective feature vectors. The feature vectors may be used to train a model to generate risk indicators for HTML webpages. A potentially parked domain webpage or a potentially malicious domain webpage may be received. A feature vector for the webpage may be generated and inputted to the model. The model may generate a risk indicator for the webpage. The risk indicator may be output and may cause responsive actions. The model may be updated based on a determination indicating whether the webpage was a parked domain webpage or a malicious domain webpage.

Data platforms described herein are configured to monitor a compute environment and facilitate interactive analysis of multifaceted security threats within the compute environment. Such a data platform may determine that one or more assets within the compute environment are possibly being targeted by a multifaceted security threat and present an interactive user interface. The user interface may be configured to display an identifier indicative of the multifaceted security threat, a set of selectable evidence items each associated with a different facet of the multifaceted security threat and assessed based on the monitoring of the compute environment, and a presentation pane for displaying information. As such, the data platform may detect a selection of a particular evidence item from the set of selectable evidence items and, in response to the selection, populate the presentation pane with information related to the particular evidence item. Corresponding methods, systems, and products are also disclosed.

A method of automatic pre-prompt generation includes receiving at least one user preference from a user device, modifying a system prompt for a machine-learning language model based on the received at least one user preference to generate a modified system prompt, providing the modified system prompt as an initial input to the machine-learning language model, receiving a natural-language text prompt provided by the user to a chat application on the user device, receiving a user identifier from the user device, querying a first database with the user identifier to retrieve first information, generating a representation of the first information and the natural-language prompt, querying a second database using the representation to retrieve second information, and generating a modified text prompt based on the natural-language prompt, the first information, and the second information.

An end-to-end rendering system enforces policy-constrained, symbolic presentation of artificial-intelligence (AI) outputs prior to any pixel emission. An AI accelerator emits inference tokens as (token identifier, confidence, domain tag) tuples into an output FIFO. A graphics processor with a command processor and SIMD co-processor executes a policy engine that runs a deterministic finite automaton (DFA) stored in non-transitory memory. The DFA consumes the tuples and produces, before any frame-buffer writes, a permit / deny decision and a visibility mask that constrain presentation attributes. A glyph selector maps permitted tuples to entries in a constrained glyph dictionary specifying a glyph identifier, semantic class, and allowed substitutions. A provenance tagger computes a cryptographic hash over at least the glyph identifier, a DFA-state policy identifier, a session nonce, and a checksum of the tuples to create an evidence capsule bound to the glyph. A device-aware renderer, consulting a device profile registry and a rendering grammar, emits a renderable asset for the glyph only when permitted by the visibility mask and grammar. A justification ledger records, for each displayed glyph, the evidence capsule and a monotonic timestamp. Executed entirely within the graphics processor, the pipeline prevents unauthorized tokens from entering the display path, enables cryptographically verifiable traceability of visible content, and reduces bandwidth by representing outputs as glyph identifiers rather than text or raster imagery, while allowing runtime policy updates without modifying the underlying inference model.

Systems and methods disclosed herein are enabled to dynamically generate structured documents using one or more artificial intelligence models. A computing device receives an output generation request and uses a first AI model to retrieve data chunks from source documents and applicable templates. A second AI model ranks the retrieved chunks based on one or more metrics, such as vector similarity, keyword density, and temporal relevance. A third AI model subsequently generates a response using the ranked chunks, templates, and predefined operational boundaries for each chunk. The generated response is tagged with source identifiers to enable the traceability of the response back to corresponding chunks. The system transmits, via the computing device, the response, the retrieved chunks, and / or the source identifiers.

The invention discloses a vehicle knowledge question-answering method based on a GraphRAG method and computer equipment, and the method comprises the steps: building a document tree structure based on the title hierarchy of a vehicle knowledge document, distributing a text identifier, and adding metadata information; converting the stored text units into text unit vectors by adopting a vector embedding model, and establishing a text vector index; calculating a similarity score between the query vector and the text unit vector, and determining a traditional RAG path text unit based on the similarity score; identifying entities and entity relationships of the text units according to the entity types and the relationship types by adopting a large language model so as to generate a vehicle knowledge graph; performing multi-level community division on the vehicle knowledge graph by adopting a Leiton community detection algorithm, and generating a structured community abstract according to each level of community; determining a GraphRAG path community abstract by adopting a large language model; and fusing the community abstract of the GraphRAG path with the text unit of the traditional RAG path and generating an answer.

Systems and methods are provided for managing AI (Artificial Intelligence) agents interactions with electronic communications that are displayed at an electronic communications interface, such as an email interface. The systems parse electronic communications to determine whether they correspond to new or existing requests and actions to be performed when responding to the request(s). The systems also generate notifications identifying the set of actions with visual identifiers that visually distinguish the set of actions based on whether they have been completed or not and whether they need authorization to be completed. The systems also display selectable controls for controlling how the AI agent performs the actions.

An apparatus and system for privacy protection for Ambient Internet-of-Things (A-IoT) devices are disclosed. A-IoT devices transmit obfuscated identifiers (OIDs) instead of actual identifiers, which are de-obfuscated by the network to retrieve the original identifiers. The device identifiers are obfuscated using shared secret parameters and periodically updated configurations. Hash-based lightweight privacy mechanisms, temporary identifiers (TempIDs), and pseudonym generation may be used to ensure secure communication and prevent replay attacks.

Techniques and systems are described that perform automated identification and retrieval of contextual information for quick and accurate processing of user queries by artificial intelligence generative models. The techniques include receiving a natural language (NL) query associated with a user identifier (ID) and obtaining, using a first NL generative model, contextual data that is pertinent to the NL query and is associated with the user ID. The techniques further include generating an augmented NL query that is based on the NL query and the contextual data. The techniques include communicating the augmented NL query to a recipient that includes the first NL generative model, a second NL generative model, or a user session associated with the user ID.

The invention discloses a multi-agent interaction TOKEN compression strategy method and system based on LLMLINGUA, and belongs to the technical field of artificial intelligence and natural language processing. Aiming at the problems of high communication load and difficulty in semantic recovery caused by high-frequency interaction in a multi-agent system, the invention adopts the following technical scheme: inputting text data, and performing word segmentation and semantic analysis to form a low-dimensional semantic vector; dynamically selecting a compression strategy type according to semantic density and serial number density characteristics; the LLMlingua module is used for carrying out depth coding to generate a low-dimensional semantic identifier; storing the identifier in a dynamic reference library and distributing a unique reference code; reference codes and context information are transmitted among the intelligent agents; self-adaptive grouping is achieved through cosine similarity calculation, similar group debate is skipped, and debate between different groups is triggered; the modules are integrated to a multi-agent system. According to the method, the communication load and calculation overhead are remarkably reduced, semantic high-fidelity restoration is guaranteed, and the system response efficiency is improved.

In various examples, systems and methods are disclosed relating to displaying images in chatbot / NPC / virtual agent / digital avatar / etc. responses. A system can identify text corresponding to an image in an electronic document and can store a representation of the text in association with an identifier of the image. The system can receive an input prompt for a machine-learning model. The system can generate a response to the input prompt using the machine-learning model. The response can include the image responsive to identifying the representation of the text using a searching function and an output of the machine-learning model.

The invention discloses a large language model illusion detection method and system based on an adaptive sampling strategy, and the method comprises the steps: carrying out the word segmentation and coding of a to-be-detected text and each sampling text, obtaining the respective token identifiers, carrying out the splicing, obtaining a plurality of text pair sequences, and carrying out the coding processing, and obtaining the coding results of a plurality of text pairs; the coding results of the text pairs are input into a consistency evaluation model to be processed, and consistency scores of the multiple text pairs are obtained; and obtaining an embedded vector of a to-be-detected text, calculating statistical information of consistency scores of the plurality of text pairs, inputting the embedded vector and the statistical information into the sampling decision model for decision making, determining whether to perform sampling again, if so, performing sampling again, and if not, obtaining a hallucination detection result based on the consistency scores. A sampling decision is innovatively designed after consistency scoring, and the sampling decision realizes adaptive sampling and scoring in the illusion detection process by means of a near-end strategy gradient optimization training sampling decision model in reinforcement learning.

The invention relates to the field of risk data visual tracing, and provides a visual track tracing method and system for a risk checking task, and the method comprises the steps: analyzing a heterogeneous data stream, separating a source metadata set, calculating an original structure entropy fingerprint, intercepting an end feature, embedding a global tracking identifier, and generating a packaging data package; generating transformed service data by using dynamic byte code instrumentation, constructing a runtime execution context and a differential snapshot, and transmitting an instantiated track node object according to a shunt delivery strategy; mapping and instantiating the logical topology execution graph based on the Hash fragments, and marking a pollution state and generating a total element execution link graph when the logic topology execution graph is abnormal; the method comprises the steps of generating a topology summary data packet, rendering a real-time state view, responding to physical interaction operation to construct a retrieval request data packet, reconstructing a full-amount service load through topology backtracking and reverse data evolution, and generating an attribution view in combination with static codes and rule description. According to the method, a dynamic trajectory tracing and full-link risk visualization mechanism of massive heterogeneous data is constructed.

The invention relates to the technical field of computers, and discloses a multi-mode audio and video synchronous processing method and system based on a distributed architecture. The method comprises the following steps: generating a unique logic sequence identifier consisting of a device code, a media type and a frame number for each frame of audio and video data; distributing the frame carrying the identifier to a distributed node for processing and adding a local timestamp; and the sink node reconstructs an original frame sequence according to the identifier, calculates a synchronous offset by combining high-precision clock calibration, and realizes dynamic reordering and output through a double-buffer structure and a self-adaptive drift compensation algorithm. The system comprises a source end acquisition module, an identifier generation module, a task scheduling module, a distributed processing cluster module, a convergence synchronization module and a synchronous output module. According to the scheme, the system effectively guarantees the audio and video synchronization precision in a high-concurrency heterogeneous environment, the synchronization error is controlled within 20 milliseconds, and the playing quality and the user experience of scenes such as live broadcast and cloud games are remarkably improved.

The invention relates to the technical field of artificial intelligence, and provides a large model cue word construction method and device, equipment, a medium and a program product, and the method comprises the steps: extracting query content and a business scene identifier from a user request; based on the business scene identifier, matching a target cue word template from a cue word template database; and based on the intention category identifier associated with the query content and the target cue word template, constructing a large model cue word. According to the large model cue word construction method provided by the invention, the target cue word template is matched from the cue word template database based on the business scene identifier, and the large model cue word is constructed in combination with the intention category identifier associated with the query content, so that the adaptive large model cue word can be dynamically constructed according to the real-time user intention and the business scene, and the user experience is improved. The method has high flexibility, and meanwhile, through scene adaptation and intention accurate guidance, the prompt words can be constructed in a unified and dynamic manner by multiple services, and the expansibility and adaptability of service processing are improved.

The invention discloses a home page multi-component parallel prefetching and rendering first screen acceleration method and device and a storage medium, and the method comprises the following steps: constructing a global dependency relationship model based on the metadata and dependency relationship of each component of a home page, and calculating the priority for each component according to a predefined priority evaluation strategy, generating a component scheduling sequence constrained by the priority and the dependency relationship; controlling a concurrency control strategy of the component; preferentially rendering a visual placeholder structure, and smoothly replacing the real content to a user visible area through a progressive rendering replacement strategy; when the variable quantity of the component data exceeds a preset threshold value, triggering an increment updating strategy based on the difference content; generating a cache identifier according to component granularity, reading and writing data in a multi-level cache structure based on the cache identifier, and ensuring data consistency by adopting a background asynchronous verification strategy; and collecting performance indexes in a page rendering process, and adjusting the priority evaluation strategy, the concurrency control strategy and the increment updating strategy according to the performance indexes.

The invention provides a data privacy protection method and system for a large model knowledge base, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining original privacy data of each participant, carrying out the cleaning and feature extraction of the original privacy data, and recognizing sensitive information fields; performing desensitization processing on the fields containing the sensitive information by utilizing a generative adversarial network to obtain desensitized data; and performing k-anonymization processing on the desensitized data, dividing the data into a plurality of equivalence classes based on the quasi-identifier, and performing generalization processing on the classes with the record number less than k to obtain an anonymized data set. According to the method, the privacy leakage problem of multi-participant data collaboration in large model training is solved.

A smart shopping cart identifies items using cameras and sensors. The cart captures images of items within its storage area and applies machine-learning models, such as a barcode detection model, an OCR model, and an image embedding model, to generate identifier predictions. These predictions are processed using an efficient selection algorithm, which may involve majority voting, weighted voting, or linear regression, to select the most accurate identifier. The cart updates its display and user interface with the identified item. The process may be performed primarily by the CPU to enhance computational efficiency, avoiding the latency associated with GPU data transfer. Additional techniques, such as circular buffers and frame skipping, are employed to further optimize resource usage.

The disclosed technology includes a computer-implemented technique for generating images of physically producible products in response to user input describing a conceptual product. The system receives user input—such as natural language text, speech, or images—via a user interface, configures a prompt for a generative artificial intelligence (AI) system, and generates an image representing a version of the conceptual product with distinct physical attributes. Each version is associated with a unique identifier, enabling selection, modification, and purchase of the conceptual product. The system supports multiple product categories, including jewelry, home décor, and fashion, and extracts physical attributes to determine manufacturability and pricing. User feedback is incorporated to improve AI performance. The invention enables presentation of selectable product versions and initiates manufacturing processes based on user selections, supporting unstructured user input and multiple data modalities.

Techniques for simulating multi-agent interactions within a virtual world in response to generated hypotheses using dynamically retrieved external data are disclosed herein. A computing system can receive instructions including a user input with a seed phrase and provide the user input to a configuration generation model to generate agent traits associated with agents in a virtual world and / or data-source identifiers associated with external data sources. The system can retrieve external data from the external data sources and provide the agent traits and external data to a hypothesis generation model to generate a hypothesis that includes a constraint associated with the external data. The system can instantiate agents that have the agent traits and execute a simulation session consistent with the constraint (e.g., by causing the agents to generate an output set using the hypothesis and question). The system can generate a representation of the output set for analysis.

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.

Retrieval of structured tabular data in a retrieval-augmented generation (RAG) platform is described. An example method includes receiving a tabular file including structured data, extracting short and long metadata from the file, and generating a dataset agent object using the extracted metadata. A human-readable description is associated with the structured data, and the dataset agent object and metadata are stored in a metadata index. A natural language query is received through a query agent object rendered within a canvas including interconnected agent objects. A dataset agent object is identified based at least in part on metadata filtering using the natural language query and short metadata. A structured query conforming to structured query language syntax is generated using a large language model (LLM) and executed on the structured data. A response including the result set and dataset agent identifier is generated and transmitted to a downstream agent object for further processing.

The invention discloses a retrieval enhancement generation method and system based on context awareness, and relates to the technical field of information retrieval, and the method comprises the following steps: carrying out block processing on a knowledge base document, distributing a unique identifier and a sorting sequence number, and establishing an association relationship between text blocks to save a document structure; constructing a structure index of the document; according to a query request of a user, performing initial retrieval on query content based on the vector similarity to obtain an initial related text block set with the highest relevancy; performing context extension retrieval on the initial related text block set to obtain an extended preamble text block set; and carrying out duplicate removal, merging and reordering processing on the preamble text block set, and adopting a smooth transition technology to obtain a natural text as a retrieval result. Through the technical scheme of the invention, not only is the correlation ensured, but also the context integrity is ensured, the semantic fragmentation problem of a traditional RAG system can be effectively solved, and the retrieval quality is remarkably improved.

The invention provides a universal visual perception method for an open world based on language guidance, and belongs to the crossing field of computer vision and natural language processing, and the method comprises the steps: generating an initial multi-modal fusion representation, and obtaining the initial multi-modal fusion representation through the collection of a video frame sequence and a language instruction through processing and fusion; then determining a target candidate region, and carrying out matching, screening and optimization based on semantic keywords and visual region features; then generating a target identifier, and allocating a unique identifier to the high-confidence region; a continuous tracking trajectory sequence is formed, and a bounding box is updated and the trajectory is smoothed in combination with an algorithm; when the target disappears, the state vector of the target is temporarily stored, and the tracking identifier is recovered when a similar region appears; optimizing the tracking sequence, and adjusting a bounding box to generate an optimized sequence; and finally, outputting target motion trail, position and state information. Through multi-modal fusion, an optimization algorithm and a recovery mechanism, the open world target identification and tracking effect is effectively improved, and the practical value is high.

The invention discloses a mandatory access control method and device based on a process function context, and the method comprises the steps: collecting a security context associated with a system call initiated by a target process, so as to generate a standardized object description; mapping the object description into a target function classification identifier, so as to obtain a process function context view of the target process according to the target function classification identifier; constructing a target decision key for access decision based on the current policy era, the qualifier, the function classification identifier, the view identifier of the process function context view and the isolation domain abstract; and querying the multi-level cache according to the target decision key to determine a matched target access decision. Therefore, context-sensitive judgment and cross-component consistency taking the functional context as the center are realized.

The invention belongs to the technical field of semantic recognition, and discloses an extraction analysis method and system based on semantic expression understanding. The method comprises the following steps: performing semantic analysis and feature extraction on an original query statement input by a user in an interactive interface to generate a structured semantic unit; performing interaction scene judgment on the structured semantic unit based on a preset multi-dimensional judgment condition and then outputting a scene identifier; calling a corresponding target intention recognition strategy according to the scene identifier; inputting the structured semantic unit into a domain classification model, and outputting a plurality of candidate intentions and corresponding initial confidence; and determining a target user intention from the candidate intentions based on an interaction clarification result of the user and an intention query rule by using the initial confidence and a preset confidence threshold, and generating a standardized intention recognition result. According to the mode, the user semantics can be deeply understood, the recognition strategy is dynamically adjusted according to the context, and accurate, efficient and sustainable extraction analysis is carried out on the user intention through man-machine cooperation and closed-loop feedback.

The invention belongs to the technical field of natural language processing and knowledge engineering, and discloses a method, a system, equipment and a medium for automatically constructing a multi-field scientific knowledge base, and the method comprises the following steps: based on an input identifier list or a field search word, retrieving a full text of a literature, analyzing and converting the full text into a structured text; combining a pre-configured large language model with a field cue word, extracting key information in the structured text, and outputting structured data; the automatic script filters irrelevant, repeated or incomplete structured data according to a pre-configured filtering rule; performing standardization processing on the filtered structured data so as to realize the consistency and comparability of the data; and inserting the standardized structured data into an interactive knowledge base or database, establishing data association, and verifying association logic. The method supports cross-field rapid adaptation, solves the problems of low efficiency and high error rate of a traditional method, and can be widely applied to the fields of biomedicine, material science, synthetic biology and the like.

This application discloses a parameter advertisement method and apparatus. An unpacking end advertises a packing end of a feature of a data flow that needs to be packed and an identifier of a compression algorithm, so that the feature of the data flow and the identifier of the compression algorithm are automatically transferred from the unpacking end to the packing end. Therefore, the packing end can determine packing for some data flows and a to-be-used compression algorithm based on the received feature of the data flow and the received identifier of the compression algorithm, so that the packing end can pack a specified data flow by using a specified compression algorithm, and a process in which the feature of the data flow and the identifier of the compression algorithm are manually configured at the packing end is avoided. Therefore, configuration complexity at the packing end is reduced.

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.