190 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 discloses an accurate formula calculation method and device based on a large language model, and the method comprises the steps: receiving a mathematical formula image inputted by a user and a calculation request described by a natural language, carrying out the recognition of the mathematical formula image, generating a standard LaTeX expression, carrying out the grammar and semantic verification, employing the large language model to analyze the calculation request, and carrying out the analysis of the calculation request. The method comprises the steps of identifying a calculation type, a target variable and a parameter, outputting a structured operation instruction, converting a standard LaTeX expression into a mathematical object compatible with a symbol calculation library according to the structured operation instruction, dynamically declaring a symbol variable according to the operation instruction, and executing symbol operation based on a parameter extraction result to obtain a symbol calculation result. A symbol calculation result is converted into a readable format to be output, LaTeX mathematical expressions and natural language description are supported, a large language model and a multi-modal model are used for accurately recognizing a formula and analyzing a user natural language problem, complex symbol calculation and equation solving are automatically completed, tasks can be automatically completed through natural language interaction, and the use threshold is lowered.

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.

A server may automatically determine a classification for document text of an electronic document and display a graphical indication of the classification of document text in a first graphical region of a first graphical user interface. In response to the server receiving an approval of the classification, the server may generate a label for the document text based on the classification and train a machine learning (ML) model using the label and the electronic document. Furthermore, the server may execute the trained ML model for a second electronic document. For at least one field of a form on a webpage, the server may automatically complete a widget embedded in the web page, using the trained ML model display a second graphical indication of text in the second electronic document providing data for the at least one field.

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 relates to the technical field of artificial intelligence, in particular to a man-machine interaction system based on an open source large model. The method comprises the following steps: a user request unit receives multi-round dialogue input of a user based on a context interception algorithm and an intention recognition mechanism, and generates a context prompt word and a target agent identifier; the response processing unit receives the standardized request generated by the platform scheduling unit based on an exponential backoff retry algorithm and a JSON path extraction function, extracts a target field and converts the target field into a structured answer; and the knowledge management and state maintenance unit updates each round of questions and answers to a historical context based on a context closed-loop updating mechanism and a binding relation mapping mechanism, and feeds back the updated context to the user request unit. Based on a context interception algorithm and a semantic embedding classification model, multiple rounds of conversations of a user are converted into semantic cues, target agent identifier mapping and intention affiliation reasoning are automatically completed, and high-precision identification of a request intention and dynamic binding of the agents are achieved.

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 embodiment of the invention discloses a building automation system, a control method, a device, equipment, a medium and a product. The system comprises a database used for storing equipment data of a building; the intelligent agent is used for receiving natural language statements related to the building; the large language model is used for executing semantic understanding on the natural language statement and generating an operation task based on a semantic understanding result; wherein the intelligent agent is also used for accessing the database to obtain the equipment data associated with the operation task from the equipment data of the building, and executing the operation task based on the equipment data associated with the operation task. And the large language model is cooperated with the intelligent agent, so that the building task is automatically completed, and the intelligence of building control is improved.

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 invention relates to the technical field of adjustment of cross-platform advertisement material putting, and discloses an adjustment method and device for cross-platform advertisement material putting, electronic equipment and a medium, and the method comprises the steps: generating creative variants in batches by using a multi-modal large language model, shortening the material creation time, greatly improving the production efficiency and diversity, and reducing the production cost. Cultural compliance filtering is automatically completed through a preset rule base, the over-auditing rate is greatly improved, the compliance risk is reduced, an innovative cross-platform adaptation mechanism can intelligently convert core creativity into a form conforming to different platform preferences, efficient reuse of materials is achieved, a large amount of repeated manufacturing cost is saved, and finally, the method is suitable for popularization and application. And performing dynamic strategy adjustment on the real-time feedback data based on the reinforcement learning model. The method has the beneficial effects that the continuous autonomous optimization of the putting effect is realized, the advertisement click rate, the conversion rate and the overall return on investment are remarkably improved, and the end-to-end intelligent advertisement operation capability is formed.

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.

The invention discloses a multi-dimensional vocabulary expansion query system and method for trademark registration risk assessment, and the method comprises the steps: constructing a multi-dimensional, regionalized and quantitative conflict risk assessment and registrability prediction model, employing an advanced large language model, and combining the regionalized language culture characteristics; a comprehensive variant set of a target trademark in multiple dimensions such as shape, sound, meaning, common name, network term and cross-language transcription is intelligently generated, and the whole process of vocabulary expansion, full-classification parallel retrieval and risk calculation and prediction is automatically completed. The system has good expandability and maintainability, the comprehensiveness of trademark retrieval and the accuracy of risk identification are remarkably improved, and the risk of missing report is effectively reduced; the trademark retrieval and risk assessment efficiency is improved, and one-key quick response is realized; quantitative, objective and regionalized adaptive risk assessment and regionability prediction are provided to assist the user in accurate decision making; the user experience is improved, and a visual report and a convenient export function are provided.

The invention discloses a multi-agent collaboration system for constructing a data set for vertical field large language model training. The system integrates four levels of data acquisition, data processing, data generation and data optimization, and can automatically complete full-process processing from multi-source corpus acquisition, structured analysis to instruction and preference data construction after receiving a field demand and a retrieval intention proposed by a user. Through the cooperative effect of picking, structured reconstruction, co-citation analysis, expert generation and screening optimization intelligent agents, automatic construction of a pre-training data set, an instruction fine tuning data set and human feedback reinforcement learning RLHF data is realized, and the consistency, traceability and high quality of the data are ensured; therefore, the training effect and the application performance of the vertical field large language model are effectively improved.

One or more characters are input into an auto-complete field. Text of a displayed list of one or more candidate items is retrieved. The text of the displayed list of the one or more candidate items is compared to a predefined candidate item. In response to the text of the displayed list of one or more candidate items not having the predefined candidate item or not being withing a defined ranking, test results are flagged where the predefined candidate item is missing from displayed list of the one or more candidate items or is not withing the defined ranking. This allows the for automated testing of the auto-complete fields.

The invention provides a performance testing method and device and a storage medium. The method comprises the steps that all executable programs of a software system submitted by a user and modified program files in the software system are received; based on the program file and all executable programs of the software system, performing a performance test to obtain a test report; and sending the test report to the user. According to the application, a user only needs to send all the executable programs of the software system and the currently modified program file to the server. And the server automatically completes the performance test of the software system and returns a test report to the user. Automatic testing of the whole process of the software system is achieved, the automation degree is high, and the testing efficiency is improved. And the test is performed only when the modified program file appears, so that the incremental performance test is realized, the performance test is more targeted, and the test efficiency and accuracy are improved. And tests in the aspects of off-line analysis, on-line analysis, integration test and the like can be carried out, and the performance test is more comprehensive, efficient and accurate.

The invention discloses an automatic wiring method for a gold-plated lead of a DRAM (Dynamic Random Access Memory) packaging substrate, and relates to the technical field of DRAM packaging substrate design. The method comprises the steps of structured data input, VBA intelligent coordinate calculation, network screening and duplicate removal, wiring instruction set generation, script automatic generation, automatic execution and graph generation. Basic data are input through a standardized Excel template, a VBA program automatically completes calculation of bonding wire angles and lead coordinates and duplicate network de-duplication, then the bonding wire angles and the lead coordinates are converted into Allegro scripts, and gold-plated leads are drawn in batches in Allegro software. According to the method, a traditional manual design mode is replaced, the wiring efficiency is greatly improved, the problem of lead omission or redundancy caused by human negligence is avoided, the design flexibility and the change response capability are enhanced, and the accuracy and reliability of gold-plated lead design are guaranteed.

The invention discloses a method, a system, a medium and equipment for generating a BI picture from a design manuscript, and the method comprises the steps: obtaining design manuscript file data, carrying out the classification recognition and labeling of the data, and converting the data into structured data; according to the structured data, generating a standardized query demand, and establishing a mapping table of the design annotation and the data demand; calling a Text2SQL (Structured Query Language) model to generate an initial query statement for verification according to a standardized query requirement, and generating structural data of design draft analysis; selecting a target technology stack according to the structural data analyzed by the design draft, and generating a front-end code; and according to the front-end code, compiling to generate a runnable static file, starting a local preview service, and according to feedback, automatically updating a design draft analysis algorithm and a Text2SQL (Structured Query Language) model. According to the method, data query, code generation and picture rendering are automatically completed through design draft input through automatic code generation and the Text2SQL technology, and the development efficiency of BI pictures is improved.

The invention relates to the technical field of artificial intelligence, in particular to a customer service reply method and system of a multi-modal fusion architecture and a storage medium, and the method comprises the following steps: obtaining initial input data of a user, preprocessing the initial input data, and generating a standard data source; performing decision compliance analysis on the standard data source to generate a decision judgment result; and generating a corresponding decision scheme according to the decision judgment result. According to the invention, through a space-semantic double-constraint graph network, the technical defects of fragmentation and low standardization degree of multi-modal data identification by traditional intelligent customer service are effectively solved, and the problem of insufficient adaptation of a general identification technology to a government affair format is overcome; in a semantic constraint dimension, spatial features and a government and enterprise professional semantic dictionary are deeply fused based on a node association mechanism of a graph network, conversion from multi-modal data to structured policy elements is automatically completed, and the recognition accuracy is improved by more than 40% compared with that of a traditional NLP technology.

The invention relates to the technical field of new energy power systems, in particular to a new energy power system operation scheme evaluation method, device and equipment based on a large model and a computer storage medium. The method supports a natural language input operation scheme, automatically completes modeling and simulation calling, avoids manual operation, greatly reduces an evaluation threshold, and is suitable for rapid comparison and selection of batch and multi-scene schemes; according to the method, a unified evaluation index system is constructed, key dimensions such as voltage / frequency / attack angle stability, economical efficiency, carbon emission and system flexibility are covered, transverse comparison and grading judgment are supported, and evaluation scientificity is enhanced; according to the method, a large model and an RAG technology are integrated, and an industry knowledge base and a template are combined, so that a specialized evaluation report is automatically generated, and the communication efficiency and the engineering landing capability are improved; the method supports the access of various simulation tools and data formats, is suitable for various types of users, and promotes the standardization, intellectualization and popularization of the evaluation method.

The application discloses a kind of structural member manufacturing feeding configuration data automatic generation method, comprising the following steps: S1: obtaining the process rule of structural member manufacturing;S2: import and parse structural three-dimensional model, extract the geometric feature information of component;S3: based on geometric feature, component is automatically identified and name mapping, and the interactive selection of component is carried out;S4: based on process rule and component structure relationship, execute component allocation and generate component allocation result;S5: based on component allocation result and tray loading capacity constraint, tray allocation planning is carried out;S6: according to component allocation result and tray allocation result, generate feeding group data and standardize export.This application compared with prior art has the advantages that: provide a kind of structural member manufacturing feeding configuration data automatic generation method and system for component allocation and feeding configuration data generation are conveniently and automatically completed.

The invention discloses a method and a device for automatically skipping repeated approvers based on a global monitor, and aims to solve the problems of low efficiency, poor experience and the like caused by repeated approval in an enterprise-level approval process. According to the method, a repeated approver detection monitor is automatically injected into all user tasks in a process definition analysis stage by inheriting a BPMN analysis monitor of a process engine, and a process definition file does not need to be modified. When the task is created, the monitor reads the repeated skipping configuration defined by the process, judges whether the scene is a multi-person approval or countersigning scene or not, queries a historical task table to detect whether approvers are repeated or not, and automatically completes the task and sets a skipping mark variable if the approvers are repeated. The method has the advantages of zero invasion, flexible configuration, scene compatibility, traceability, high reliability and the like, and the approval efficiency and experience are effectively improved.

This invention discloses an intelligent self-healing system and method for operation and maintenance based on an LLM Agent, belonging to the fields of information technology and operation and maintenance automation technology. The system includes a data acquisition and preprocessing module, a rule scheduling module, a large language model intelligent agent module, a work order generation module, and an approval and execution module. It collects and standardizes multi-source monitoring data through dual channels, and the rule scheduling module intelligently distributes data according to single alarm or multi-alarm aggregation conditions: simple scenarios directly trigger the automatic operation and maintenance mechanism; complex scenarios submit the alarm context and predefined operation types to the LLM Agent to output optimal execution parameters. The work order generation module automatically generates work orders; the execution engine automatically completes the repair operation. This invention solves the problems of rigidity in traditional rule engines and the uncontrollability and difficulty in integration of pure AI models, achieving an end-to-end self-healing closed loop that balances security and intelligence, significantly improving the automation coverage and decision-making accuracy in complex scenarios.