75results about "Unstructured textual data retrieval" patented technology

The invention relates to the technical field of passenger service, and discloses a construction method and device of a dynamic extension word bank, equipment and a medium. The method comprises the following steps: acquiring multi-source heterogeneous original knowledge data related to civil aviation passenger service, and converting the multi-source heterogeneous original knowledge data into a standard format text; processing the unstructured data in the standard format text based on the adaptive word segmentation model and the stop word list to obtain a plurality of candidate words; performing multi-dimensional corpus feature quantitative analysis on each candidate word to determine candidate keywords; and performing similarity calculation based on the entity word segmentation and the candidate keywords, determining effective candidate keywords, and adding the effective candidate keywords into the dynamic expansion word bank. By means of the method and device, the technical problems that in the prior art, a word bank used by a civil aviation service system is usually based on a static vocabulary or depends on manual compiling and updating, the static word bank cannot be updated in real time, the characteristics of the civil aviation field cannot be flexibly handled, and the maintenance cost is high due to manual updating and maintenance are solved.

The invention discloses a data error correction method, a data error correction device and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: firstly, converting first text data in a first document to be subjected to error correction into structured second text data retaining a content association relationship, and identifying a target error type of the second text data through semantic analysis; and then, calling a target error correction engine integrated with the industry knowledge base to realize accurate identification and correction of industry exclusive errors. In this way, the problems that a general error correction tool is low in precision, not suitable for industry characteristics and free of compliance basis are solved. And finally, converting the error correction result into a revision mark supported by the first document, and mapping the revision mark to the first document, so that accurate positioning and display of the error correction result in the original first document can be realized, a user can be helped to quickly focus on a core risk, and the document checking efficiency can be improved while the error correction precision of the document is ensured.

A Multimodal Search System (MSS) is designed to enable accurate search and retrieval across different modalities of data, including images, videos, and text. The MSS leverages multimodal and domain-specific models to extract and represent features and information from multimodal data, enabling seamless querying using text-based search queries. The MSS integrates multiple pipelines, including feature vector-based and tag-based approaches, to ensure robust and scalable search capabilities. The MSS addresses current multimodal-search challenges by leveraging a hybrid approach that combines multimodal embeddings, text embeddings, and semantic tagging to enable cross-modal searching. Additionally, for visual data, both tagging and embedding techniques are integrated within a single modality, ensuring a comprehensive and accurate representation. By assembling multiple retrieval strategies, the solution harnesses the strengths of each, resulting in a balanced and effective search system.

A method, according to one approach, includes: causing a received query to be translated from a first language to a second language. The method also includes generating potential answers for the translated query, and extracting features from the translated query. The method also includes causing the extracted features to be converted into feature vectors. The method also includes causing the feature vectors to be compared against existing vectors in a knowledge base that correspond to past question-answer pairs. The method also includes causing the potential answers to be ranked based at least in part on an outcome of comparing the feature vectors against the existing vectors in the knowledge base. Furthermore, the method includes causing a final answer to be generated based at least in part on the ranked potential answers.

The application discloses a data error correction method and device and a storage medium, and relates to the technical field of data processing. First, the first text data in the first document to be corrected is converted into structured second text data that retains content association, and the target error type of the second text data is identified through semantic analysis. Then, a target error correction engine integrated with an industry knowledge base is called to realize accurate identification and correction of industry-specific errors. In this way, the problem of low accuracy of general error correction tools, inadaptation to industry characteristics and lack of compliance basis is solved. Finally, the error correction result is converted into revision marks supported by the first document, and the revision marks are mapped to the first document, accurate positioning and display of the error correction result in the original first document can be realized, and the user can be quickly focused on the core risk, the error correction accuracy of the document is ensured, and the auditing efficiency of the document is improved.

A system for searching contents within a database of textual contents is described. Said contents and the corresponding databases may be of any kind such as movie titles, music titles, song titles, scientific terms, medical titles / terms, titles formed of one or more sequences of symbols, a list of telephone numbers, a contact list, etc. Upon providing one or more keyword, the search system provides a list one or more of corresponding contents to a user. According to one aspect, after selecting a presented content, by the user, a process corresponding to selected content is executed by a processor.

A method includes receiving, via a processing system, unstructured data associated with one or more operations performed in a production system, extracting, via the processing system, information from the unstructured data based on one or more domain specific prompts associated with the production system. The method also includes integrating, via the processing system, the information with structured data to generate updated structured data, receiving, via the processing system, a request associated with the one or more operations, and generating, via the processing system, a response based on the updated structured data, wherein the response includes a visualization representative of the response.

Systems and methods access, from one or more data storage locations, a dataset; perform data analysis on the dataset to detect one or more data quality characteristics each corresponding to at least one data quality dimension including timeliness, uniqueness, accuracy, completeness, validity, or consistency; evaluate the one or more data quality characteristics present in the dataset to identify one or more common patterns; and generate one or more data quality rule recommendations based on the identified one or more common patterns.

A method (300) includes processing a first query (116) to classify the first query as being related to an existing topic (251) that corresponds to a respective topic summary (250) stored in a datastore (198). The respective topic summary associated with a respective summary of past query-response interactions between a user and an assistant interface (150). The method also includes retrieving the respective topic summary from the topic summary datastore that corresponds to the existing topic, processing the first query conditioned on the respective topic summary retrieved from the topic summary datastore to generate a first response (118), and providing presentation content (180) based on the first response for output.

The invention discloses an index calculation method and device based on model fusion and a medium. The method comprises the steps that mixed data used for calculating a target index and user calculation requirements are acquired; determining a data proportion corresponding to the mixed data, and determining a structural data calculation model and a non-structural data calculation model in a preset model library based on the data proportion and / or a user calculation demand; determining result weights corresponding to the structural data calculation model and the non-structural data calculation model based on the data proportion and / or user calculation requirements; and determining an index calculation result based on the result weight, the structural data calculation model and the non-structural data calculation model. Based on the proportion of the structured data to the unstructured data in the mixed data, the combination of a traditional algorithm and large model calculation can be optimized, so that the advantages of the traditional algorithm in the aspects of structured data processing and interpretability are fully utilized.

Automatically detects a performance degradation in document retrieval. When document data is appended to the database (300), the learning unit (260) updates the language model (61) using machine learning. The performance evaluation unit (270) calculates a first statistic related to the position of each specific document data in the multiple specific document data based on the document retrieval results where each of the first tags attached to the multiple specific document data is set as a retrieval query (TQRY). The performance evaluation unit (270) calculates a second statistic related to the position of each specific document data in the multiple specific document data based on the document retrieval results where each of the second tags attached to the multiple specific document data is set as a retrieval query (TQRY). If the change in the first statistic caused by the update of the language model (61) accompanying the appending of at least one document data is greater than a first threshold, and the change in the second statistic caused by the update is greater than a second threshold (Th2), the performance evaluation unit (270) detects a performance degradation in document retrieval.

This invention relates to the medical field. It provides a method, apparatus, device, and medium for structured processing of medical materials based on OCR. The method includes: acquiring an image of a medical material to be identified; performing text recognition on the image using OCR technology to obtain multiple target texts; sorting the multiple target texts to obtain a target text set; determining the medical material type corresponding to the target text set based on a pre-trained medical material model; determining multiple structured field names corresponding to the medical material type of the target text set, and multiple keywords corresponding to each structured field name, based on a pre-constructed structured dictionary; and performing structured processing on the target text set. This invention can greatly eliminate the differences between medical materials in hospitals across the country, has high support for medical materials in hospitals nationwide, provides comprehensive coverage, has high efficiency in extracting structured medical information, and can make reasonable use of photographed medical materials.

The invention relates to the technical field of contract processing, and particularly provides an intelligent purchase contract comparison method and device in a cloud environment, and the method comprises the steps: firstly carrying out the data acquisition in a data storage layer, carrying out the format conversion, text preprocessing, feature extraction and similarity calculation in a data processing layer, and finally carrying out the similar contract screening in an application layer. Compared with the prior art, the method can help enterprises to optimize purchasing strategies and reduce purchasing risks, and is of great significance to digital transformation and fine management of enterprise purchasing services.

The invention discloses a business opportunity report automatic generation method and system based on a large language model and a storage medium, and the method comprises the steps: obtaining a plurality of hierarchical business scenes after splitting a business demand, constructing a report template, automatically collecting internal and external data according to the business scenes representing the business demand, and carrying out the preprocessing, thereby achieving the automatic generation of a business opportunity report. The business opportunity report generation method comprises the following steps: preprocessing data, splitting the preprocessed data by adopting a Prompt engineering technology and a Dify-based modular instruction, analyzing a scenarized instruction through a large language model according to a plurality of sub-processes obtained by splitting the instruction to obtain a multi-scene analysis result, and finally, automatically generating a business opportunity report according to a report template and the multi-scene analysis result. The multi-source data is automatically collected for accurate analysis, the business opportunity report is generated, the generation efficiency of the business opportunity report is improved, the accuracy is ensured, the analysis difficulty is reduced, and the business opportunity report can quickly respond to market dynamics.

The disclosure relates to methods of providing a response to a user query. A query is derived from the user query. An embedded query is obtained by passing the query through a first portion of a trained large language model. A semantically relevant element is obtained from an embedded database. The embedded database was obtained by embedding an initial database using the first portion of the trained large language model. The semantically relevant element is combined with the embedded query to form an augmented query. A response is provided to the user query by passing the augmented query through a second portion of the trained large language model.

The application relates to a data repairing method and device, computer equipment and a storage medium. The method comprises the following steps: obtaining sample data to be repaired, internal feature items and a feature similarity matrix, determining a data repairing item according to the missing number and missing position of missing attribute data in the sample data to be repaired, fusing the sample data to be repaired, the internal feature items and the data repairing item to obtain a target difference item, determining a constraint condition based on the data repairing matrix, the internal feature items and the feature similarity matrix, fusing the target difference item and the constraint condition to obtain a target iteration item, finally, iteratively determining a target data repairing matrix for the target iteration item, and obtaining target repaired sample data according to the target data repairing matrix, a data repairing index matrix and the sample data to be repaired. The method can effectively improve the accuracy of data repairing.

The invention relates to the technical field of computer systems, in particular to a material research system and method based on a multi-agent game, and the system comprises a man-machine interaction module, a scheme generation module, a review module, an arbitration module, a scheme execution module and a knowledge base module: the man-machine interaction module generates a task instruction according to a user demand; the scheme generation module is used for generating a plurality of candidate schemes; the review module performs multi-view cross review and risk interception on the candidate schemes; the arbitration module is used for processing opinion conflicts among multiple agents; the scheme execution module is used for executing an experiment and collecting data; and the knowledge base module is used for providing static knowledge support and closed-loop backflow of dynamic experimental data for the system. According to the method, multi-target conflicts, knowledge uncertainty and theoretical and practical differences in material research and development are converted into a structured and computable dynamic game process, so that the comprehensiveness of scheme generation, the preciseness of review and the success rate of experimental conversion are systematically improved.

Systems and methods access, from one or more data storage locations, a dataset; perform data analysis on the dataset to detect one or more data quality characteristics each corresponding to at least one data quality dimension including timeliness, uniqueness, accuracy, completeness, validity, or consistency; evaluate the one or more data quality characteristics present in the dataset to identify one or more common patterns; and generate one or more data quality rule recommendations based on the identified one or more common patterns.

The invention relates to the technical field of artificial intelligence, and provides a customer data processing method and device and a storage medium, and the method comprises the steps: providing a user dialogue interaction interface at a front end, so as to obtain sales session data and data storage information of a seller and a customer; the front end transmits the sales session data and data storage information to a rear end, so that the rear end obtains a session text of the sales session data, content extraction based on semantic analysis processing is carried out on the session text through a large language model according to the extraction rule, and sales session analysis results generated by the extracted content are aggregated according to the prompt words; and associating the session text and the sales session analysis result thereof with the data storage information to complete storage so as to form a follow-up record. Therefore, the analysis result of accurate extraction of the sales session is obtained, the follow-up quality and the follow-up decision can be automatically and efficiently determined, and the sales data processing and analysis efficiency is effectively improved.

A market explanation event extraction method includes a step of acquiring, by a market explanation event extraction system, a stock price index data and unstructured data of a text type for predicting a stock price, a step of extracting, by the market explanation event extraction system, an event that influences a stock market based on the acquired unstructured data, a step of predicting, by the market explanation event extraction system, a future direction of a stock price by analyzing the extracted event, and a step of selecting, by the market explanation event extraction system, an event that matches a result of predicting the direction by comparing the result of predicting the direction and an actual stock price index, and extracting a market explanation event that explains a cause of a rise or fall of a stock price based on the selected event.

The invention discloses a brain-computer signal safety system and a use method thereof, and relates to the technical field of intelligent information processing. The system comprises an information acquisition module, an analysis module and a security module. The information acquisition module is used for acquiring brain-computer signals and related safety information; the analysis module analyzes the signals and the information by using a personalized brain-computer signal safety model, and the model performs personalized setting on at least one of crowd, heredity, physiology, psychology or body factors of an acting object, and obtains proper brain-computer signal safety measures accordingly; and the safety module carries out safety processing on the brain-computer signals according to the measures. According to the method, the personalized security model based on the individual characteristics of the user is introduced, so that accurate and adaptive security control of the brain-computer signal can be realized, the individual adaptability and the use security of the brain-computer interface are remarkably improved, and the injury to the user is effectively avoided.

Systems and Methods for Presenting Search Results and Computer Readable Media. A system identifies a document related to a search term, in which the document includes a set of structural elements. The system determines a distribution of occurrences of the search term in the document, identifies one of the structural elements, based on the distribution of occurrences of the search term in the document, and presents information associated with the identified structural element.

An information processing device and method for improving search expansion generation (RAG) accuracy are provided. [Solution] The information processing device includes a document data acquisition unit that acquires document data, a document processing unit that performs layout analysis of the document data to identify multiple areas included in the document data and recognizes the contents of the multiple areas to extract multiple elements, a structured data generation unit that generates structured data by structuring the multiple elements, a graph data generation unit that generates graph data in which the multiple elements are multiple nodes based on the relationships between the multiple elements included in the structured data, a determination unit that determines a suggested correction node from the multiple nodes included in the graph data and a suggested correction for the suggested correction node, a display control unit that controls the display of the graph data and the suggested correction for the suggested correction node, and a correction unit that corrects a portion of the structured data that corresponds to the suggested correction node based on a user instruction for the suggested correction.

A communication system which comprises at least one unstructured data storage function (UDSF) node, the UDSF comprising an Unstructured Data Storage Manager (UDSM) and an Unstructured Data Storage Rep

According to one aspect, a method includes obtaining, at an interface to a system that includes a large language model (LLM) arrangement, a first prompt, and identifying a plurality of candidate chunks of documents that substantially match the first prompt. The method also includes analyzing the plurality of candidate chunks to identify a chunk node associated with the plurality of candidate chunks, and generating a query arranged to solicit information associated with the plurality of candidate chunks. A second prompt is obtained in response to the query, and the plurality of candidate chunks is analyzed. Analyzing the plurality of candidate chunks using the second prompt includes identifying at least a first candidate chunk of the plurality of candidate chunks that is associated with the second prompt, wherein the first candidate chunk has a context. Finally, the method includes generating a response to the first prompt using the context.

A computing system is disclosed for augmenting semantic retrieval using structured knowledge constraints. The system obtains content segments from one or more source documents and processes the content through a dual-indexing pipeline that generates vector embeddings and constructs a graph representation of inter-segment relationships. A vector index and a graph-based structure are maintained in parallel to support retrieval workflows. Upon receiving a search query, the system initiates vector-based retrieval to identify semantically similar segments and concurrently traverses the graph to identify contextually connected segments. Retrieved segments are cross-referenced using constraint resolution logic that evaluates identifier overlap, relationship proximity, and graph alignment metrics. The system adjusts relevance scores using adaptive weighting and structural validation and produces a unified ranking of content segments. Validated results may be presented for review, integrated into external applications, or refined through feedback signals that update scoring parameters and edge weights over time.

A method for monitoring and / or controlling the production of at least one chemical product, the method comprising: providing chemical product data relating to the production and / or processing of the chemical product; providing a plurality of chemical product data templates indicative of structures associated with at least a portion of the chemical product data; selecting at least one chemical product data template by determining the at least one chemical product data template corresponding to at least a portion of the chemical product data; generating contextualized chemical product data by merging at least a portion of the chemical product data with the at least one selected chemical product data template; providing the contextualized chemical product data to a data-driven model for generating environmental characteristic data relating to environmental characteristics associated with processing and / or producing the chemical product, where the data-driven model is configured to provide the environmental characteristic data in response to being provided the contextualized chemical product data; the environmental characteristic data is provided for monitoring and / or controlling the processing and / or production of the at least one chemical product.

The technology disclosed relates to systems and methods for artificial intelligence (AI) assisted management of web services. Disclosed implementations can include receiving, at a user interface, a user input including unstructured natural language and sending an HTTP request including the user input via an API layer to a conversation logic. The technology disclosed can further include pre-processing the HTTP request using the conversation logic, wherein the pre-processing further includes one or more of: parsing the unstructured dialogue of the user input, filtering out filler words from the user input, labelling the user input, feature selecting, feature augmenting, and appending metadata to the HTTP request, and identifying a web service management task responsive to the pre-processed HTTP request. The identified web service management task can be routed to a best fit AI model trained to perform one or more web service management tasks.

Conventional models extract chemical data through querying tables by decomposing complex user queries. This disclosure relates generally to a method and system for extraction of chemical molecules and associated target properties from text and complex tables using Large Language Models (LLMs). The disclosed method extracts a plurality of molecular property values associated with the chemical molecules, from a plurality data sources, via generating a chemical composition schema utilizing LLMs. The chemical composition schema acts as a lens to view tabular information and a text. Relevant tables are identified and tabular information comprising chemical composition instances are extracted from a plurality of documents utilizing the chemical composition schema, along with LLMs and prompting techniques. The chemical composition instances are curated and reconciled into a unified knowledge graph, which is then used for querying. The disclosed method ensures precision in tabular information extraction without the need for extensive model training.