528 results about "Text file" patented technology

The invention provides a knowledge graph open domain construction and RAG question and answer method and device and a storage medium. The method comprises the steps of obtaining text content in a text file; performing text division on the text content to obtain text blocks; performing open domain information extraction on each text block by utilizing a large language model to obtain an initial triple; performing post-processing on the initial triad to obtain a post-processing triad for constructing the knowledge graph; constructing a knowledge graph according to the post-processing triple; the method for realizing RAG question answering based on the knowledge graph comprises the following steps: performing knowledge graph retrieval on a question to obtain a context related to the question, then constructing a question answering cue word by using the context and the question, and sorting out an answer to the question by a large language model based on the question answering cue word. According to the method, the problem that a complete knowledge graph cannot be obtained during defined domain knowledge graph construction in the face of diversified entities and relation types is solved, and the problems that RAG retrieval information is weak in correlation with problems and the multi-hop reasoning capability is limited are solved.

Systems and methods for semantic temporal segmentation based on topic recognition are disclosed. Text classification and segmentation may be used to index media content for subsequent searches. The method may include using a text classification model to semantically analyze sentences in a text file (such as a transcript) to determine topics with which sentences are associated. The output of the text classification model may be provided to a text segmentation model to enable more-accurate identification of text segments (e.g., paragraphs) within the text file. In some examples, the output of a text segmentation model is provided to a text classification model to enable the text classification model to perform more-accurate classification based on text segments rather than (or in addition to) performing classification on single sentences. The classification of the text segments may be used to assign labels to the text segments to enable subsequent searches based on the labels.

Systems and methods for implementing sticky tokens are illustrated. One embodiment includes a machine-readable medium containing bytecode stored within an immutable ledger. The bytecode encodes a review token, comprising a token identifier. The review token comprises a content element, wherein: the content element comprises a text document; and the text document corresponds to a written review. The review token comprises a recipient identifier, wherein the recipient identifier identifies a blockchain address of an intended recipient. The review token comprises access control information, comprising a right to transfer the review token. Execution of the bytecode occurs automatically when the recipient identifier matches a current owner of the review token. Execution of the bytecode renders of the content element. Execution of the bytecode blocks the blockchain address of the intended recipient from possessing the right to transfer.

A computer-implemented method includes generating to a display device a graphical user interface (GUI), the display device being coupled to at least one processing device. A file-icon reception field is generated within the GUI. A command to move into the reception field an icon representing a digital text file containing a data set is received from a user. The digital file is accessed. A table of contents characterizing the data set is generated, one or more portions of the data set based on the table of contents are displayed in the GUI.

Systems and methods are disclosed herein for generating updated descriptions of items based on analyzing candidate embeddings of semantic representations of item descriptions. The system may obtain a text file describing an item. The system may provide the text file to a generative language model to generate semantic representations of the text file. The system may generate, based on the text file, candidate embeddings in an embedding space. The system may obtain embeddings associated with existing items. The system may determine subsets of the embeddings within a threshold distance. The system may compare the subsets. The system may determine attributes associated with a candidate embedding based on the comparison. The system may generate an updated text file based on the attributes.

The invention discloses a hospital internal contract and project intelligent auditing method and system, and the method comprises the steps: inputting to-be-examined files into a multi-agent cooperation network model of an auditing vertical domain large model, carrying out the document format conversion of a plurality of to-be-examined files through a perception agent, and obtaining a plurality of text files; performing intention recognition according to the to-be-examined file, the audit scene and the audit type to obtain an audit rule list, and performing extraction, document slicing and assembly on the to-be-examined content according to the audit rule list to obtain an atomic file corresponding to each audit rule; performing risk judgment on the atomic file corresponding to each auditing rule by using the analysis agent to obtain a corresponding atomic result, performing summarization by using the summarization agent to obtain a risk prompt list, and performing summarization based on the risk prompt list to obtain auditing suggestions. According to the method, the files in different formats are processed through stepped task decomposition, and the processing results of the intelligent agents are integrated, so that the efficiency of the audit results is improved.

The invention discloses an SSD (Solid State Disk) testing method, which relates to the technical field of SSDs, and comprises the following steps: an SSD is connected to testing equipment, and the testing equipment comprises a controller, a data generation module, a data transmission module, a data storage module and a performance monitoring module; specific types of test data are generated through a data generation module, the specific types of test data comprise sequential read-write data, random read-write data and mixed read-write data, the file size of the sequential read-write data is within the range of 1GB-10GB, the operation block size of the random read-write data is within the range of 4KB-64KB, and the file size of the mixed read-write data is within the range of 4KB-64KB. The ratio of sequential read-write to random read-write in the mixed read-write data ranges from 1: 1 to 3: 1, the test data further comprises multiple file formats including but not limited to text files, image files, video files and audio files, and the data size of each file format accounts for 10%-30% of the total test data size; the SSD performance can be accurately evaluated, and the performance evaluation standard is perfected.

A system for preparing documents in a standards compliant electronic health record (EHR). A document intake device is configured to receive an image file transmitted to the device using an image file transport protocol. An artificial intelligence / machine learning (AI / ML) model is configured to extract patient demographic and medical information contained within the image file as a text file with the patient demographic and medical information, and format the text file for use as a EHR by a transformation engine. The transformation engine is configured to convert the text file formatted for use as the EHR into the standards compliant EHR and transmit the standards compliant EHR as a Direct Secure Message to a Health Information Service Provider (HISP). The HISP configured to send the standards compliant EHR as a Direct Secure Message for storage as a patient record if a patient match is found.

The invention discloses a data quality detection method and device for DataX and a medium, and relates to the technical field of data processing. The method comprises the following steps: reading a data source in a parallel fragmentation manner through a reader plug-in of DataX to obtain to-be-processed data; when the to-be-processed data is transmitted to the memory buffer area, triggering a quality detection engine, and loading a predefined detection rule base through the quality detection engine; if it is detected that the field is empty, the format deviates from a preset template or the necessary field is missing, generating an abnormal log containing a problem type, a field identifier and original data; and classifying and storing the abnormal logs into a distributed file system according to timestamps, generating a visual abstract report, analyzing problem types, field identifiers and original data, and calling a predefined repair script library for repair. By means of the method, data quality detection is executed in the data input and output process of the DataX, the detection result is output to the text file, and quality detection can be efficiently and accurately conducted on the data.

The invention belongs to the technical field of RAG questioning and answering, and particularly relates to an efficient and accurate RAG questioning and answering method and device based on a knowledge graph and a storage medium. The method comprises the following steps: reading contents in a text file, dividing the contents to obtain text blocks, and obtaining an initial triple by using a large language model; performing post-processing on the initial triad to obtain an optimized triad, and constructing a knowledge graph; building an index key value pair based on the optimized triad and the text block to which the triad belongs, and storing the index key value pair in a vector database; performing mixed knowledge retrieval based on the index key value pair to obtain mixed knowledge beneficial to question answering: unstructured knowledge from the text and structured knowledge from the knowledge graph; and taking the mixed knowledge as the context of question answering, and reasoning by using a large-scale language model. According to the method, the response time is remarkably shortened, the unstructured knowledge and the structured knowledge are effectively integrated, and the accuracy of low-level retrieval and high-level retrieval is remarkably improved.

A system and method are configured to commission and decommission an endpoint device using steganography. The system includes a server operatively connected to the endpoint device through a network. The server includes a steganographic module configured to receive configuration instructions, to generate a secret message corresponding to the configuration instructions, and to steganographically embed the secret message in a carrier file, including a media file selected from the group consisting of a text file, an image file, an audio file, and a video file. The carrier file is transmitted to the endpoint device which extracts the secret message from the carrier file, and which executes the configuration instructions in the secret message to reconfigure the endpoint device to commission or decommission the endpoint device or a component thereof. The method implements the system.

A computerized-method for providing personalized-content for an interaction of an agent with a customer during an outbound conversational-marketing-campaign of a tenant operated via a cloud-based contact-center-platform. The computerized-method includes: (i) receiving details of the customer and details of the outbound conversational-marketing-campaign of the tenant; (ii) creating a prompt-text based on the received details of the customer and the details of the outbound conversational-marketing-campaign; (iii) generating the personalized content for the interaction with the customer in text-format by executing an LLM AI engine with a trained model with the created prompt-text; (iv) storing a text-file with the generated personalized content in text-format in a contents-database; (v) initiating the interaction of the agent by dialing to the customer; (vi) retrieving the text-file for the interaction from the contents-database; and (vii) sending the text-file to a computerized-device of the agent to be presented via a display-unit that is associated to the computerized-device.

The invention discloses a code repository question and answer method based on a hierarchical code knowledge graph, which comprises the following steps: collecting all files of a target code repository, and preprocessing to obtain code files and text files; generating an abstract syntax tree from the code file, and extracting related entities; carrying out block segmentation on the text file, and extracting to obtain text blocks; constructing a code knowledge graph representing a code structure based on the extracted entities and text blocks; generating a hierarchical code knowledge graph by adopting a community detection algorithm and a hierarchical clustering method, and generating a functional abstract for each community in combination with a large language model; a Monte Carlo tree search-based code warehouse question and answer agent is used for performing question and answer path optimization, an optimal query track is dynamically planned through the steps of node selection, expansion, simulation, reward evaluation and the like, and a path with the highest accumulated reward is selected to generate a final question and answer result. The code knowledge modeling quality and the reasoning accuracy and efficiency of the question answering system can be effectively improved.

Examples of the present disclosure describe systems and methods for automating the identification of events in a text file. In examples, a computing system identifies a subset of a text file that comprises an unknown event using a set of rules. Each rule of the set of rules specifying a first pattern of characters is compared to the subset of the first text file. When the set of rules does not identify the unknown event, the subset of the text file is provided to a language model to generate a new rule with a second pattern of characters and an identifier of the new rule. The system then generates an updated set of rules by adding the new rule to the set of rules.

The invention provides a text file generation method and device, equipment, a medium and a product, and is applied to the technical field of data analysis. The method comprises the steps that in response to input of a query statement, an original text feature database and an abstract feature database are acquired; retrieving the query statement in the original text feature database to obtain a first retrieval result, and retrieving the query statement in the abstract feature database to obtain a second retrieval result; sorting the first retrieval result and the second retrieval result through a pre-trained correlation sorting model to obtain a candidate result; performing time sequence analysis and event aggregation on events in the candidate results through a time sequence index database to obtain a plurality of target events and a time sequence logic relationship of the plurality of target events, the target events being events associated with the query statement; and outputting the plurality of target events according to the time sequence and a preset text format. Therefore, the workload of manual file processing is greatly reduced.

The invention belongs to the technical field of intelligent manufacturing of aeronautical manufacturing enterprises, and relates to an assembly process procedure auxiliary compilation method based on a large model, which comprises the following steps: data preparation and processing: collecting and preprocessing an assembly process procedure text file; carrying out algorithm model selection and fine tuning, using an assembly process procedure text as a training data set and a test data set, and selecting different types of large models to carry out fine tuning; service publishing: deploying a plurality of large models with the best evaluation indexes as services, publishing the services, and externally providing available interface services; constructing an RAG knowledge base, vectorizing the assembly process procedure text, and constructing a vector knowledge base; intelligent question and answer: performing knowledge question and answer based on the RAG knowledge base; and assembly process procedure file generation: constructing the assembly process procedure file through text generation. According to the method, the functions of semantic retrieval intelligent question answering of the assembly technological procedure knowledge base, automatic assembly technological procedure file generation, information recording and the like are achieved.

An audio translation labeling and analysis method based on a multi-agent cooperation mechanism belongs to the field of information technology and artificial intelligence, and is characterized by comprising the following steps: S1, translating an audio file into a text file; s2, outputting a dialogue set of text structure arrangement for the text file through a model in which domain knowledge and language style learning are introduced; s3, extracting and structurally expressing a time expression, a place name and an abstract paragraph by adding a large language model of a prompt project, and associating an event background with a session context to obtain an information set; s4, splicing the dialogue set and the information set to obtain a document set; s5, forming a text-label pair data set by a series of texts and the corresponding labels with inclusion relations, and constructing the text-label pair data set into an instruction data set conforming to instruction fine tuning format specifications; s6, performing fine adjustment on the large language model base according to the instruction data set to obtain a fine-adjusted model; and S7, inputting the document set into the fine-tuned model, and labeling all levels of labels to obtain the document set with the labels, which is used for full-process automatic processing from audio transcription to text optimization, information extraction, labeling and analysis report generation.

The invention discloses an evidence optimization method and device based on a Bayesian model, computer equipment and a storage medium, and relates to the technical field of medical care informatics. The method comprises the following steps: acquiring a text file containing a variation classification result; calculating posterior probabilities corresponding to 17 evidence combinations given by the ACMG / AMP guide, and when selected evidence combination rules meet posterior probability classification, obtaining theoretical pathogenicity probabilities corresponding to the evidence intensity grades; finely classifying the clinical significance unknown variation VUS into six levels; determining thresholds corresponding to different evidence intensity grades by comparing the positive likelihood ratio or the local positive likelihood ratio with the theoretical pathogenicity probability corresponding to each evidence intensity grade; and the variation is marked with an ACMG / AMP evidence label again, and a pathogenicity classification result of the variation is given. According to the method, the evidence optimization difficulty can be reduced, and the accuracy and consistency of genetic variation interpretation in clinical gene detection can be improved.

The invention discloses an operation planning optimization modeling enhancement method, which comprises the following steps of: based on an operation planning optimization problem text file, constructing a structured problem representation containing a dependency relationship between constraints and original semantic information; according to the structured problem representation, executing logic consistency deduction by constructing and solving forward and reverse problem pairs, and generating a forward modeling result and a reverse verification set; performing multi-dimensional verification on the forward modeling result to identify potential modeling defects, and generating a structured counter-example test set for the potential modeling defects; and integrating the test performance of the reverse verification set and the structured counter-example test set, evaluating the credibility of the forward modeling result, and generating a final optimization model and a credibility evaluation report. According to the method, the logic preciseness and credibility of the automatic generation model can be improved, and quantitative reliability evaluation is provided for the model.

In some aspects, the techniques described herein relate to a method including: receiving, at a query platform, a query text string; tokenizing, by the query platform, the query text string, wherein the tokenizing generates a query vector embedding; determining, by the query platform, a document vector embedding, wherein the document vector embedding is above a similarity threshold with respect to the query vector embedding; retrieving, by the query platform, textual document data related to the document vector embedding; sending, by the query platform, the query text string and the textual document data to a generative model engine; receiving, by the query platform and from the generative model engine, a natural language response to the query text string; and displaying, by the query platform, the natural language response via an interface.

The invention discloses an unstructured file statistical analysis engine device based on AI semantic understanding. An uploaded file is processed through a knowledge base management module; the rule setting module is used for setting a reasoning triggering rule and controlling a reasoning output result; the knowledge reasoning module records and reasones all files and problems in the knowledge base one by one according to rules in the rule setting module and records results; the knowledge analysis module automatically analyzes the returned data and removes duplication; the knowledge storage module automatically creates a corresponding data service table according to format requirements; the statistical analysis module performs structured analysis on the data and generates a corresponding statistical list or content; and the knowledge display module displays the content generated by statistics according to different display modes and traces back to the original text file, and the unstructured text is creatively converted into the structured text file through combined use of the modules.

A system has a server having an internal data repository, an internal database storing data describing virtual objects, each having a unique SIO code, and an associated body of digital media providing object description by one or more of audio files, text files, photographs, and videos, object history and stories about the virtual objects, a user station with Internet access, a microphone, a display screen, a speaker system, and a coupled local data repository, and a software-enabled interface between the user station and the internal data repository. The user selects an SIO search, the system in response initiates a reactive natural language session with the user through the software-enabled interface, the user speaks queries and commands into the microphone, and the system interprets the queries and commands and endeavors to perform the commands and answer the queries by audio through the speaker system and display on the display screen.

The invention provides a method for quickly converting coordinate formats of mineral resource reserve databases in batches and related equipment. The method comprises the following steps: firstly, reading a mining area number and a registration classification number in a row coordinate record, and a character string in a mining area or mine range coordinate field and a character string in a calculation coordinate field from a coordinate management table; secondly, independently storing block coordinates in the two character strings by utilizing two one-dimensional arrays, and independently storing initial positions and inflection points of the block coordinates by utilizing two two-dimensional arrays; and finally, according to the information in the four arrays, in combination with an encoding rule of the character string, reading block coordinates from the corresponding arrays in sequence, forming a coordinate pair format, and writing the coordinate pair format into a corresponding text file, so that forward conversion from the coordinate string to the coordinate pair is realized. In addition, reverse conversion of coordinate pairs to coordinate strings is also provided. According to the invention, the coordinate pair data meeting the coordinate system transformation format requirement can be quickly realized and provided.

The invention relates to the field of hardware control, and provides a device and method for outputting a running log of a basic input / output system based on a virtual serial port, and the device comprises a substrate management controller which is provided with a first virtual serial port control module, the receiving module is used for receiving serial port data sent by a host through an enhanced serial peripheral interface bus; a first virtual serial port driving module and a second virtual serial port control module are arranged on a south bridge chip of the host, and the first virtual serial port driving module is used for sending operation log information to the first virtual serial port control module through an enhanced serial peripheral interface bus; the second virtual serial port control module is used for outputting the running log information to a text file under the universal serial bus flash disk when the existence of the universal serial bus flash disk is detected; and the enhanced serial peripheral interface bus is connected between the first virtual serial port control module and the first virtual serial port driving module and is used for mutually transmitting data between the first virtual serial port control module and the first virtual serial port driving module.

The invention relates to a multi-dimensional knowledge base construction and query method based on a multi-modal large model, and belongs to the technical field of AI large model application. The method comprises the following steps of: analyzing a text file containing text, table and picture contents; selecting a pre-trained large language model and a multi-modal large model, generating text abstracts and picture abstracts by utilizing the models, and enabling storage paths of the picture abstracts to correspond to storage paths of pictures one by one; selecting a text embedding model to construct a vector database and a retriever; and constructing a retrieval chain based on the multi-modal large model. According to the method, construction from a single text knowledge base to a multi-dimensional knowledge base is achieved, a multi-dimensional information retrieval method is provided, the problem of construction of the multi-dimensional knowledge base based on the vectorization technology is solved to a certain extent, and the large model output quality based on the retrieval enhancement generation technology is remarkably improved.

The invention discloses a material review processing method and system based on an AI model, and the method comprises the steps: obtaining a to-be-reviewed file inputted by a user, and dividing the to-be-reviewed file into a digital file, an image file and a text file according to the file type; generating target element tags according to the image file, and associating the text segments segmented from the text file and the corresponding data information in the digital file with the corresponding target element tags to form a plurality of to-be-reviewed information groups; and performing step-by-step auditing on the to-be-reviewed information groups according to corresponding review conditions in the review standard database, and generating an electronic review report according to an auditing result of each to-be-reviewed information group. According to the method, different types of to-be-reviewed materials submitted by a user can be comprehensively evaluated, and key contents of the to-be-reviewed materials are extracted by combining information of the image file, the text file and the digital file, so that the reliability of a review result is improved.

The invention discloses an intelligent patent retrieval method and system based on a large language model reordering technology, objects processed by the method comprise a variety of input data including patent public numbers, free texts and text files, key information is extracted, query texts are constructed, retrieval recall is executed, sparse and dense vector retrieval technologies are combined, and the retrieval efficiency is improved. And results are fused through a min sum merge algorithm. The de-noising step uses a sliding window strategy and vector coding to extract information fragments related to the query text. And finally, by training a reordering model and finely adjusting a recommendation list, the ranking of retrieval results is optimized, and the accuracy and correlation of retrieval are improved. According to the intelligent retrieval method, the patent retrieval efficiency and accuracy are improved.

The invention discloses a rich text multi-dimensional difference analysis and accurate revision method and system based on AI, and relates to the technical field of artificial intelligence and data analys.The rich text multi-dimensional difference analysis and accurate revision method comprises the steps that firstly, a rich text file is analyzed, a document structure is reconstructed, vectorization and key information labeling are conducted on semantic blocks, and structuralization and semantic enhancement of a text are achieved; the method comprises the following steps of: firstly, carrying out text surface layer, semantic deep layer and structure association dimension difference analysis in parallel, then carrying out system knowledge graph-based revision influence degree evaluation on the basis of difference analysis, tracing associated terms through penetrating analysis, calculating an influence degree score and early warning potential conflicts, and automatically generating accurate revision suggestions; visual difference preview and one-key generation of a revised draft are provided, a closed loop from problem discovery to problem solving is realized, and the efficiency and accuracy of system revision are greatly improved.

The invention relates to the technical field of electronic document processing, particularly provides a processing method and device for an electronic document containing sensitive data, equipment and a medium, and aims to solve the technical problem that contents of a text file are easy to leak during processing. In order to achieve the purpose, the method comprises the steps of obtaining a text document; performing conversion processing on the text document to obtain text content and format data; performing desensitization processing on the text content to obtain desensitized text content and removed sensitive data; sending the desensitized text content and format data to a cloud, so that the cloud intelligently processes the desensitized text content and format data to obtain output text content, and sending the output text content to a user terminal; adding the removed sensitive data to the output text content to obtain restored text data; and according to a preset format restoration model and the format data, performing reconstruction processing on the format of the restored text data to obtain a final text document.

The invention belongs to the field of sentiment analysis, and particularly discloses a fine-grained sentiment analysis method and system based on deep learning, a terminal and a medium. Initial text semantic information of different levels is extracted from a text file and a sentiment label file; extracting different levels of initial picture semantic information from the picture file; inputting the initial text semantic information and the initial picture semantic information of the same layer into a cross-modal context attention network to obtain cross-modal fusion semantic information; inputting each layer of cross-modal fusion semantic information into a discriminator, and performing multi-label classification based on a multi-label cross entropy loss function to realize fine-grained sentiment analysis model training; and performing fine-grained sentiment analysis on the to-be-analyzed picture-text pair by using the trained fine-grained sentiment analysis model. According to the method, fine-grained sentiment analysis is realized, complementary information between pictures and texts is fully utilized, and the classification accuracy is improved.