147 results about "Autocomplete" patented technology

A computerized platform enables secure, policy-compliant creation, governance, and monetization of digital artifacts produced by Al-driven “persona” agents in regulated domains (e.g., healthcare, finance). For each agent session, a federated compliance-token generator issues a cryptographically signed token embedding the full delegation chain, credential lineage, real-time policy snapshot, monetization rules, expiration and revocation data. As artifacts or workflows cross organizational or jurisdictional boundaries, a token inheritance module can subdivide, transfer, renew, or revoke the token while appending each event to an immutable, cryptographically linked audit trail. Every agent output is encapsulated as a digitally signed artifact that is indelibly linked to its originating compliance token and enriched with provenance, consent, and service metadata. A permissioned blockchain (or sharded distributed ledger) functions as an asset registry that immutably stores artifacts, tokens, and chronological records of creation, access, transfer, consent change, and policy updates. A smart-contract engine automates revenue allocation and settlement, ensuring continuous traceability, privacy protection, and financial compliance across institutional boundaries.

The invention relates to the technical field of artificial intelligence, in particular to a multi-modal intelligent compliance pre-auditing method and system, and the method comprises the following steps: receiving a qualification package uploaded by a supplier, and automatically completing the decompression operation; accurately marking the decompressed page as five types of a subject license, a financial report, industry qualification, a product catalog and environmental protection and safety evaluation by using a layout perception model, and executing fingerprint hash de-duplication processing on the homologous file to generate a unique document identifier DocID; the method has the beneficial effects that the authenticity verification, financial analysis and directory matching of all qualifications of a single supplier can be completed within about 30 seconds by adopting a one-package type automatic sorting link and parallel API aggregation verification; and compared with the traditional manual item-by-item check which merges the process of several hours, the whole check period is shortened by more than 90%, and the manpower of compliance specialists is greatly released.

The invention provides an end-to-end intelligent system development method and system based on an AI large model. The method comprises the following steps: S1, receiving a system demand described by a user through a natural language; s2, analyzing the requirements through an AI large model and converting the requirements into formalized specifications; s3, automatically generating a system architecture and dividing components; s4, generating each component code in a recursive manner; s5, dynamically detecting and repairing the generated code problem; s6, automatically integrating all components to form a complete system; and S7, continuously optimizing the system based on system feedback or user feedback. According to the method, work of multiple stages such as demand analysis, architecture design, module division, code writing and testing is automatically completed, distributed integration and smart contract are performed, distributed integration verification is performed through a block chain smart contract and a graph database, data consistency, interface matching and security among components are ensured, the integration process is optimized, and the integration efficiency is improved. Cross-domain cooperation is achieved, and the development efficiency is high.

The invention discloses an interactive terminal system for artificial intelligence recruitment, and relates to the technical field of artificial intelligence and human resources. The system comprises an intelligent interaction module, a demand analysis module, a data storage module, an intelligent matching engine, a post issuing module, an intelligent evaluation module, a process management module and a user feedback and model optimization module. The system receives an unstructured recruitment demand through a multi-modal interface, analyzes and generates structured post data based on a natural language processing technology, realizes accurate man-post matching through a multi-dimensional matching algorithm, and automatically completes post release, skill evaluation, video interview analysis and process management. According to the method, the problems of inaccurate demand understanding, low matching efficiency and tedious process in traditional recruitment are effectively solved, intelligent and automatic management of the whole recruitment process is realized, and the recruitment efficiency and talent matching accuracy are remarkably improved.

Systems and methods for generating dynamic conversational recommendations. Conversational recommendations include communications between a user and a system that may maintain and / or facilitate (e.g., via autocomplete functionality) a conversational tone, cadence, and / or speech pattern of a human during an interactive exchange between the user and the system. The system may use artificial intelligence applications to generate suggested dynamic conversational recommendations based on initial user inputs (e.g., such as autocomplete functionality).

Various embodiments of the present disclosure provide model-based domain-aware autocomplete techniques for generating autocomplete suggestions in a complex search domain. Example embodiments are configured to generate, using a domain-aware autocomplete model, a label for an autocomplete suggestion based on a set of keywords within an autocomplete suggestion training dataset associated with a target domain source. Example embodiments are also configured to generate, using a weak-labeling model, an updated label for the autocomplete suggestion by decorrelating the set of keywords from the label. Example embodiments are also configured to generate, using a sentence classification model, a category for the autocomplete suggestion based on the updated label. Example embodiments are also configured to, using the domain-aware autocomplete model, generate a suggestion-category pair (SCP) based on the autocomplete suggestion and the category for the autocomplete suggestion. Example embodiments are also configured for initiating performance of a search query resolution based on the SCP.

The embodiment of the invention provides a method and a device for generating training data of a graphical interface agent. The method and the device can determine high-quality training data according to a scheme that the graphical interface agent carries out training based on a thinking chain. Wherein the graphical interface agent can predict the operation used for completing the task described by the user instruction through the user graphical interface and the user instruction. For a data set comprising a user graphical interface, instruction information and truth value operation, an initial thinking chain is predicted based on a large language model, then a graphical interface agent obtains corresponding prediction operation based on the user graphical interface, the instruction information and the initial thinking chain, and the thinking chain is corrected under the condition that the prediction operation is inconsistent with the truth value operation. And forming a high-quality training sample for the GUI intelligent agent by using the corrected thinking chain and the data set. According to the scheme, high-quality training data acquisition can be automatically completed.

The invention discloses an interactive digital human generation method based on picture and audio synthesis, and belongs to the crossing field of artificial intelligence and computer graphics. According to the method, a full-link process of'feature extraction-model construction-emotion driving-real-time interaction-video output 'can be automatically completed only by uploading a character picture and a section of audio by a user: key point and semantic feature extraction is performed on the picture to obtain face / posture information; performing voice recognition, semantic analysis and emotion recognition on the audio to obtain a semantic tag and an emotion parameter; generating a personalized three-dimensional digital human based on the information, and driving the personalized three-dimensional digital human to generate facial expressions and limb actions which are synchronous with emotions; the user intention is analyzed in real time through natural language understanding and computer vision, and multi-modal interaction is achieved. According to the method, digital human creation can be completed without professional modeling and motion capture equipment, the generation cost is reduced, and the method can be widely applied to virtual anchors, online education, intelligent customer service and movie and television entertainment scenes.

The invention discloses a semi-supervised learning-based UI (user interface) element intelligent classification method, system and product, which are characterized in that on the basis of a Transform structure, a Mean Teacher consistency regularization mechanism is introduced to improve the generalization ability of a model under a small sample condition, and accurate classification and structured grouping of fragmented and overlapped UI elements are realized through multi-modal feature fusion. The method comprises the following steps: firstly, extracting multi-source features such as images, texts, colors, positions and categories from each UI element, and uniformly encoding the multi-source features into 256-dimensional embedded representation; inputting the embedded sequence into a multi-layer Transform encoder for context modeling, outputting three types of structure tags, and automatically finishing element grouping according to the three types of structure tags; according to the method, the accuracy and the stability of UI element classification can be effectively improved, the dependence on manual annotation is reduced, and the method is suitable for scenes such as automatic interface generation, low-code development, UI prototype analysis and interactive behavior modeling.

An interactive coding environment is provided via an integrated mobile application. The interactive coding environment includes multiple user interface input elements including, but not limited to, a text editor, a virtual keyboard, a virtual autocomplete toolbar, a quick actions toolbar, and a virtual joystick. A user interacts with the coding environment via their user device, such as a mobile device. While coding within the text editor, the user is provided with auto-complete code suggestions and / or code snippets. Suggested inputs are obtained from a predictive model based on the user's input. A virtual joystick is provided to enable easy navigation within a text document shown within the text editor.

Systems and methods for generating dynamic conversational recommendations. Conversational recommendations include communications between a user and a system that may maintain and / or facilitate (e.g., via autocomplete functionality) a conversational tone, cadence, and / or speech pattern of a human during an interactive exchange between the user and the system. The system may use artificial intelligence applications to generate suggested dynamic conversational recommendations based on initial user inputs (e.g., such as autocomplete functionality).

The invention discloses an experiment report marking method, device and equipment and a computer readable storage medium. The method comprises the following steps: receiving an experiment report file and storing the experiment report file into a reviewing queue; reading the experiment report file from a reading queue; analyzing the experiment report file to obtain text content and picture content; inputting a first prompt word and the text content into a text big language model, so that the text big language model generates a first evaluation for the text content; inputting a second prompt word and the picture content into a multi-modal model, so that the multi-modal model generates a second evaluation for the picture content; and integrating the first evaluation and the second evaluation to obtain a reviewing result of the experiment report file. According to the method and the device, the test report file is automatically read by utilizing the text large language model and the multi-modal model, so that the reading efficiency is improved, and the reading standard consistency is also ensured.

The embodiment of the invention provides a multi-language switching method and device.The method comprises the steps that under the condition that a multi-language item is compiled, all translated texts in the multi-language item are translated to obtain a multi-language resource file; under the condition of running the multi-language project, receiving a switching instruction carrying a target language, and obtaining a target language resource file corresponding to the target language from the multi-language resource file; and querying from the target language resource file to obtain a target translation text corresponding to each translated text, and replacing the translated text with the target translation text. According to the method, all the translated texts are automatically translated in the project compiling stage, the multi-language resource files can be generated after compiling is completed, language resource switching can be automatically completed during project running, existing service codes do not need to be manually modified, and therefore the labor cost of multi-language / internationalized project development is saved.