331 results about "Autocomplete" patented technology

This disclosure describes a natural language question (NLQ) query service within a service provider network that provides row level security (RLS) for autocomplete during entry of NLQs and fuzzy matching in NLQ answering. The rules take the form of per-user predicates such as Tim can only see rows with region=US. In configurations a complex extraction and preprocessing pipeline to extract distinct combinations of values against RLS predicate “rule keys” is used. Those distinct values are indexed along with grouped rule keys to enable pushing down predicates at auto-complete time. This enables pushing part of RLS rule handling to ingestion time of a dataset rather than handling all RLS rule handling at query time, enabling meeting of latency goals. In configurations, a single logical document of unique cell values is split into multiple documents with a subset of rule keys to handle scalability limits.

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.

The present disclosure presents a generative model configured to receive input regarding an item in two different modalities, such as text data and non-text data (including, for example, image or audio data), in order to generate output regarding the item that is determined based on a combination of both modalities' input. Specific relative positional and token type embeddings may be employed in an encoder portion of an encoder-decoder arrangement. An associated decoder may be trained to generate new text corresponding to diverse tasks based on the encoded representation of the two inputs as generated within the encoder. For example, the decoder may be utilized to generate attributes regarding the input item, auto-complete or auto-correct a title or description of the item, among other uses.

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 relates to the technical field of table information updating, in particular to a table information updating method and system based on an intelligent recognition text. The method comprises the following steps: associating at least one table to be updated, and preprocessing an input table image; identifying the pre-processed table image by using an OCR identification technology and a deep learning convolutional neural network, and extracting table content in the table image; a semantic relation in the table content is understood through a pre-trained language model, and relation association is conducted on the table content; according to the method, the content of the table is input into the to-be-updated table according to the semantic relation and correspondingly updated, the whole process can be automated by combining OCR, CNN, a pre-training language model and an image processing technology, boundary detection, content extraction, semantic analysis and data association of the table are automatically completed through algorithms, and the processing efficiency is greatly improved.

The invention relates to the technical field of chip design, in particular to a timing constraint automation generation method. According to the method, the actual demand of the clock needing to be created is determined based on the design document, and then the clock list is generated based on the actual demand; the content of the clock list comprises a PLL source, a clock type, a clock name, a source path, a main clock name, a main clock source path, a frequency, a main clock duty ratio, a derivative clock duty ratio, a frequency division coefficient, whether the clock has a cross-clock requirement or not, and the mutual relation between the created clocks; executing the script in a clock list which needs to be read by the configuration script and the specified script; and traversing all options in the clock list by the script, generating a time sequence constraint, and automatically finishing GCA check. According to the method, timing constraint compiling and checking can be automatically achieved through the script only by completing output of the clock list, and the method for compiling the timing constraint is accurate and easy to maintain.

The invention relates to an intelligent contract auditing method and device, a block chain system, computer equipment, a computer readable storage medium and a computer program product, and the method comprises the steps: obtaining a to-be-audited intelligent contract, and extracting a source code and annotation information in the intelligent contract; performing semantic analysis on the source code and the annotation information to obtain code semantic features and annotation semantic features of the smart contract; based on the similarity between the code semantic feature and the annotation semantic feature, determining a semantic audit result of the smart contract; under the condition that the similarity meets a correction condition, performing correction processing on the source code based on the annotation semantic feature and the code semantic feature to obtain an updated code; and determining an audit result of the smart contract in combination with the semantic audit result and a vulnerability audit result obtained by performing vulnerability analysis on the update code. By adopting the method, the audit of the intelligent contract can be automatically completed, and the working efficiency of the intelligent contract audit process is improved.

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.

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 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.

The invention relates to the technical field of software testing, and provides a code atomic power testing method and device, electronic equipment and a computer program product. The method comprises the following steps: acquiring a code atomic power list and a document atomic power list; wherein the code atomic energy list records all atomic energy of the project code, and the document atomic energy list records all atomic energy of the project demand document corresponding to the project code; and comparing the code atomic power list with the document atomic power list, and determining the atomic power problem existing in the project code according to the comparison result. In the process, the atomic power test of the project code is automatically completed in a list comparison mode, and compared with a traditional test mode of manual checking, the test efficiency and accuracy can be greatly improved.

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.

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.

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 provides a real-time warehouse counting assembly line intelligent arrangement method, device and equipment, a storage medium and a program product, and relates to the field of warehouse counting intelligent arrangement. The arrangement method comprises the following steps: constructing an ETL platform; collecting a knowledge descriptive corpus and an assembly line arrangement translation case corpus, and combining the two corpora according to a predetermined proportion to form a training corpus; taking chatglm-6B as a pre-training model, training the pre-training model by using the training corpus, and outputting an intelligent arrangement model; an interface is integrated on an ETL platform to call an intelligent arrangement model, a user inputs natural language description and / or SQL statements and / or PromQL statements, and a system automatically generates a pipeline arrangement instruction, completes arrangement and displays the pipeline arrangement instruction on a page. According to the method, under high-quality training data, the assembly line arrangement instruction can be accurately generated according to the descriptive natural language, the SQL language or the PromQL language provided by the user, and the assembly line arrangement work is automatically completed.

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 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.

Methods, systems, and computer-readable storage media for receiving a set of descriptions provided as unstructured data, each description associated with one or more entities in a set of entities that can be queried using the IR system, providing, from the set of descriptions, a first set of training data including at least a first set of entities including at least a portion of the set of entities, training a named-entity recognition (NER) model using at least a portion of the first set of training data, receiving, by the IR system, a portion of a search query, and providing a set of auto-complete suggestions based on the portion of the search query and the NER model.

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.