243 results about "Autocomplete" patented technology

The invention discloses a voice interaction method and device and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a target voice instruction of a user, and carrying out the analysis of the target voice instruction according to a preset target large language model, and obtaining a control intention; determining a position sequence of a target interface element corresponding to the control intention according to the control intention and a UI interface corresponding to a controlled device; determining a trigger action sequence according to the control intention; the controlled device is controlled to respond to the target voice instruction based on the position sequence and the trigger action sequence, and the quantity of component elements of the position sequence is equal to that of component elements of the trigger action sequence. Interaction with the user interface can be automatically completed according to the voice instruction input by the user, and the interaction efficiency is improved.

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.

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 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 current document is directed to an IaC cloud-infrastructure-management service or system that automatically generates schema for cloud-infrastructure-specification-and-configuration files used by integrated development environments (“IDEs”), associated with the IaC cloud-infrastructure-management service or system, for autocompletion and validation. The IaC cloud-infrastructure management service or system accesses cloud-provider plug-ins to collect information and then encodes collected information regarding resource types, resource-type-associated functions and function arguments, cloud-infrastructure-specification-and-configuration data-file syntax, and other relevant information into cloud-infrastructure-specification-and-configuration data-file schemas. The schemas are input to integrated IDEs to control autocompletion and cloud-infrastructure-specification-and-configuration data-file validation.

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.

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.

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 task processing method. For the to-be-processed task, determining a to-be-executed task user according to the initial position and a preset coverage area range; determining a target execution user according to the current execution information and the historical task execution attribute of the to-be-executed user and the task attribute information of the to-be-processed task; determining a to-be-selected path according to the initial position, the target position, the position data of the task in the current execution information and map data corresponding to the target area; and determining a target driving path from the at least one to-be-selected path according to the target cost. According to the technical scheme of the embodiment of the invention, the target execution user is determined according to the current execution information of the user, the historical task execution attribute and the attribute information of the task, and the target driving path is determined according to the target cost corresponding to the path, so that the matching work of the task and the execution user can be automatically completed, and the most economical path is determined; the actual distribution time is effectively shortened; and the logistics cost is reduced.

In some aspects, search functionality is provided in an observability analysis system. In some implementations, search results are obtained from prior search queries, the search results include a subset of event data generated in a computing environment. Metadata are generated by analyzing the event data to identify a plurality of fields and values for each of the plurality of fields. A partial search query is received through a user interface at a first computer node; the partial search query represents at least a portion of a request to search the event data. Autocomplete suggestions are generated at the first computer node based on the partial search query and the metadata. Each of the autocomplete suggestions includes a suggested field selected from the plurality of fields, a suggested value selected from the values, or both. At least one of the autocomplete suggestions is displayed in the user interface.

A method is provided. The method is executed by an autocomplete prediction engine implemented as a computer program within a computing environment. The autocomplete prediction engine executes automated communication mining on a communication. The method includes processing the communication to extract intents and entities related to each intent. The method includes providing the intents and the entities into forms using a language model to provide a conversational or natural language understanding of the communication.

Systems and methods for a dual-pathway model that includes a deterministic word graph that operates in parallel with a semantic autocomplete model. The system generates outputs first using the deterministic word graph, and the system then uses the output of the deterministic word graph to determine whether to invoke the functionality of the semantic autocomplete model. By doing so, the semantic autocomplete model is used only sparingly, thus reducing aggregate latency in the system, but still allowing for increased scalability and customizability in the overall system as the deterministic word graph and the semantic autocomplete model may be updated and trained in parallel in the dual-pathway model.

The invention discloses a multi-language general part-of-speech recognition method and system based on a large language model, and relates to the technical field of natural language processing and part-of-speech recognition, and the method comprises the steps: employing the large language model as a core base model, and carrying out the efficient fine tuning, language design and multi-field vocabulary and syntactic analysis through a low-rank adaptation technology, model distillation is used for migrating knowledge of a large model to a small model so as to reduce the size and calculation cost of the model, standardized cue words are designed to guide the model to generate standardized output, a part-of-speech recognition task is automatically completed from the standardized output, and finally JSON format data are generated through code analysis model output. And therefore, the front-end display and the back-end service logic processing can be efficiently supported. The output part-of-speech information can be used for various downstream tasks such as intelligent search, text classification, code analysis and grammar verification, and data support is provided for various service systems.

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.

To reduce the time and effort involved in creating BF materials during sales activities.SOLUTION: A system being the patent application target, comprises: means for coordination with external databases; information automatic complementation means; and sales activity reporting means.SELECTED DRAWING: Figure 1

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.

The invention discloses an efficient automatic testing method based on TCL and Python scripts, and relates to the technical field of testing, the method comprises the following steps: S1, setting an upper computer for controlling testing, and setting a data testing instrument, a signal source and a data acquisition board card which are respectively in signal connection with the upper computer for the upper computer; s2, the signal source is in signal connection with a data acquisition board card, and a signal output by the data acquisition board card is set as a target test signal; s3, calling a Python script by using a TCL script to control a data test instrument to start a signal test process; s4, calling a Python script to generate a final result file according to an analysis result of the test data; and S5, reading a data analysis result from the final result file. Automatic control is achieved through the preset TCL and Python scripts, starting, data collection and analysis of the whole testing process can be automatically completed, and the method has the advantages of reducing the manual error rate and improving the testing efficiency, precision and repeatability.

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.