1116 results about "Electronic document" patented technology

The invention provides a talent evaluation management method and system based on AI intelligence, and relates to the technical field of artificial intelligence analysis, and the method comprises the steps: S1, converting a handwritten resume image into structured text data through OCR image recognition; converting the interview record into a dialogue text with a time sequence mark through voice transfer; performing format analysis on the electronic document to extract original text content; performing coding standardization processing on the structured text data, the dialogue text and the original text content to generate a standardized text data set containing semantic tags; and S2, performing context semantic coding on the standardized text data set, performing node alignment and semantic disambiguation processing on professional terms through a domain knowledge graph, and generating a text feature vector containing an entity association relationship. According to the method, through multi-source data integration, semantic deep analysis and dynamic matching calibration, the talent ability is accurately evaluated, the result is scientific and quantitative, and intelligent and interpretable talent evaluation and decision support is provided.

The invention relates to the technical field of management and control systems, in particular to a digital intelligence system applied to collaborative management and control of water-power engineering construction participating units, which comprises a project data management module covering water-power engineering construction full life cycle management, extracting multi-source information data from various subsystems, and constructing a project view and a project management information base; the project risk analysis module is used for constructing an intelligent risk analysis model for risk analysis and generating a dynamic evaluation result, a graded early warning notification and an auxiliary decision scheme; the project collaborative management module is used for processing graded early warning notification and auxiliary decision-making schemes by using a multi-layer perceptron model, and generating finalizing service achievements and management process records; and the project document management module is used for performing compliance automatic checking and processing on the electronic documents needing to be archived, and dynamically updating and optimizing the project management information base. Through the closed-loop management and control system, the cooperative management and control efficiency of water-power engineering construction participation units is improved.

The invention discloses an enterprise-level knowledge management system based on a large language model. The enterprise-level knowledge management system comprises a knowledge storage module, a knowledge analysis module and a knowledge application module, the knowledge storage module is responsible for managing and storing knowledge from different sources, supporting uploading, updating, deleting and sharing of electronic documents and multimedia documents, and is provided with authority control; the knowledge analysis module is used for carrying out deep understanding and structured processing on text and multimedia contents through a special analyzer by utilizing an advanced large language model technology; the knowledge application module provides intelligent retrieval and intelligent question and answer functions and supports advanced applications such as behavior portrait analysis and standard process supervision. According to the method and the system, the acquisition, understanding and application efficiency of enterprise knowledge is remarkably improved by fusing a large language model technology and a traditional knowledge management method, an intelligent, automatic and safe knowledge management solution is provided for an enterprise, and value mining and innovative application of enterprise knowledge assets are effectively promoted.

The invention discloses an electronic archive data security system and method, and relates to the technical field of electronic document management. The electronic archive data preservation method comprises the following steps: acquiring a plurality of storage files to be stored and file attribute data of each storage file in real time; analyzing the risk level value of each storage file according to the file attribute data of the storage files, and marking the risk level value; classifying and storing the plurality of marked storage files into an electronic archive database; after a plurality of storage files are stored in a classified mode, time period access data of each storage file are obtained in real time, and real-time analysis is carried out in combination with risk level values. File attribute data are obtained in real time, and risk level evaluation is carried out on the file attribute data, so that the files can be marked and stored in a classified mode in real time; according to the method, the high-risk file is ensured to accept stricter protection measures, the risk assessment index is updated in real time, and the encryption strength is dynamically adjusted according to the risk assessment index, so that the protection capability on sensitive information is remarkably improved.

Techniques are described for deploying software applications associated with document management. The techniques may include retrieving, by a computing system and from a software repository, configuration information for a software application for managing an electronic document, the configuration information specifying a plurality of widgets for deploying the software application. The techniques may further include receiving, by the computing system and from a client device, a request to access the software application. The techniques may further include generating, by the computing system, a response to the request comprising an indication of the configuration information and instructions configured to cause the client device to request, from the software repository, the plurality of widgets specified by the configuration information. The techniques may further include outputting, by the computing system, the response to the client device to provide the software application at the client device.

The invention provides a student growth archive management method, which comprises the following steps of: performing structured processing on multi-source heterogeneous data of paper archives, electronic documents and audio and video data by adopting a data acquisition and conversion technology according to heterogeneous data characteristics of student growth archive management, performing data cleaning, conversion and integration operation to obtain unified structured digital information, and storing the structured digital information into a database; a foundation is laid for subsequent big data analysis; based on a result of machine learning analysis, a student portrait and a knowledge graph are constructed, a comprehensive and dynamic student personal file is formed through multi-dimensional description of learning tracks, ability characteristics and psychological characteristics of students, entities such as students, courses, teachers and the like and relationships thereof are visualized by utilizing a knowledge graph technology, and a knowledge graph is created. The association relationship and influence factors among the students are mined, and a basis is provided for personalized education and accurate teaching.

A computer-implemented method for searching electronic documents is provided. The method executing a non-semantic search, such as lexical search, to identify documents from a document corpus that meet the search criteria of the non-semantic search. A subsequent semantic search can be scoped based on the results of the non-semantic search. The method can thus include executing a semantic search scoped to the documents identified in the non-semantic search result to generate a semantic search result that identifies content that is semantically relevant to a natural language query. Thus, the semantically relevant content can have both non-semantic (e.g., lexical) and semantic relevance.

A method for AI-driven natural language search includes receiving a user query for one or more items from a user, processing the user query by searching against at least one relational database associated with the query, where the relational database is generated by extracting features from electronic documents of a plurality of items associated with the one or more items and by identifying specifications or respective values corresponding to the extracted features of the plurality of items, generating one or more query results based on the processing of the user query, where the one or more results include at least one item identified from the plurality of items and a justification for explaining an irrelevance of the at least one item, and transmitting the one or more query results to a user device for presentation to the user.

Methods and systems for artificial intelligence (AI)-based document retrieval during a virtual meeting are provided herein. Discussion data based on multi-media stream(s) provided by client devices of participants of a virtual meeting is obtained. A determination is made of whether the discussion data corresponds to an information access query. In response to a determination that the discussion data corresponds to the information access query, the discussion data is provided as input to an AI model trained to identify one or more electronic documents including content that is relevant to given discussion data. An electronic document associated with at least one of the participants including content that is relevant to the given discussion data is identified based on output(s) of the AI model. At least a portion of the content of the electronic document is provided for presentation via a user interface (UI) at each client device associated with the participants.

A method, an apparatus, and a computer-readable storage medium for generating a document graph. A plurality of electronic documents is received. Each electronic document has a predetermined document type. A machine learning model is selected from the plurality of machine learning models based on the predetermined document type. The selected machine learning model is instructed to extract a plurality of document portions from each electronic document in the plurality of electronic documents in accordance with the predetermined document type. A relationship between two or more document portions is defined based on the content of each document portion, and the document portions are associated based on the relationship. A graph structure having a plurality of nodes is generated. Each node includes at least one document portion. Each node is connected to another node in accordance with the relationship between document portions included in the nodes. The graph structure is stored.

Presented herein are interactive graphical user interfaces (GUIs) for providing automated, real-time interactive assistance to a human user creating electronic documents. In certain embodiments, the GUI includes an alphanumeric text processing region, an AI-generated text region, and a reference list region. The AI-generated text region contains one or more alternative blocks of text responsive to a user query and based on specific references (e.g., articles, books, or other written works). The references are listed in the reference list region of the GUI.

The invention relates to the technical field of intelligent file processing, and discloses an element extraction method and device of a test report, computer equipment and a storage medium, the method comprises the following steps: obtaining scanning document documents of a plurality of paper test reports, and screening out a multi-modal format document from the scanning document documents; identifying and extracting text features, picture features and table features in the multi-modal format document based on an optical character identification technology model; performing paragraph combination on the extracted text features; and extracting structured data from the text features, the picture features and the table features after paragraph combination by adopting a multi-modal element extraction model to obtain a complete electronic document corresponding to the multi-modal format document. Based on an optical character recognition technology, character features, image features and table features in various paper test reports are accurately recognized and extracted by using a number, image and character recognition and extraction technology, and the recognition and extraction accuracy and extraction efficiency are improved.

The embodiment of the invention provides a document-based intelligent question and answer method and device, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: performing document analysis on a target electronic document to obtain document attributes and document contents; based on the document attribute and the document content, document segmentation is conducted on the target electronic document, document segmentation units and unit position information of the document segmentation units are obtained, and the document segmentation units comprise segmentation unit identifiers; based on the document attribute, performing network construction on the target electronic document to obtain a document structure tree network; on the basis of the unit position information, the document segmentation units are arranged in sequence, and a document linear list is obtained; performing document query on the document structure tree network or the document linear list based on the document problem information and the segmentation unit identifier to obtain document query information; and based on the document query information, performing question reply on the document question information. According to the embodiment of the invention, the intelligent question and answer accuracy based on the document can be improved.

The invention provides a private deployment-oriented multi-modal AI knowledge base construction system. The private deployment-oriented multi-modal AI knowledge base construction system comprises a knowledge storage module, an intelligent document loading module, a document partitioning engine module, a data enhancement engine module, a multi-language semantic vector alignment module and a private deployment module, the knowledge storage module comprises a knowledge authority management sub-module and a knowledge source management sub-module, the knowledge authority management sub-module is used for managing and storing knowledge from different sources, and the knowledge source management sub-module is used for managing electronic documents and multimedia documents; according to the method, intelligent identification, partitioning, vectorization and source file storage can be carried out on different types of electronic files, knowledge graph construction is carried out for specific fields, semantic relevance between texts and topics and key entities is fully considered, the method has wider applicability, higher robustness and controllability, the data leakage risk is effectively reduced, and the method is suitable for popularization and application. The method is suitable for enterprise sensitive data protection and personal user elastic computing power requirements.

Techniques for hybrid natural language generation (NLG) using a symbolic NLG engine and a large language model (LLM) are disclosed. The techniques include: obtaining a first text segment configuration; obtaining values of at least some of a first set of data variables; generating a first natural language text segment for an electronic document using a first text segment configuration, the values, and the symbolic NLG engine; obtaining an initial text segment; obtaining a second text segment configuration specifying information to use for generating a second natural language text segment using the ML NLG engine from the initial text segment; generating a second natural language text segment for the electronic document by using the initial text segment, the second text segment configuration, and the ML NLG engine; and outputting the generated electronic document.

The invention provides an electronic file intelligent management method and system based on AI. The method belongs to the technical field of information, and comprises the following steps: carrying out multilevel feature extraction on file content based on a convolutional neural network and a recurrent neural network, and training an adaptive classification model based on the extracted features; a user portrait is constructed based on a deep neural network according to historical operation records and behavioral habits of a user, a reinforcement learning mechanism is introduced, and a recommendation strategy is dynamically adjusted according to the user portrait and a current context. Through the convolutional neural network and the recurrent neural network, multi-level features can be extracted from multi-modal files such as texts, images and audios, internal information of the files is fully mined, and the file classification accuracy is improved.

The invention relates to the technical field of computers, and provides a document digitization method and device, electronic equipment and a readable storage medium. The method comprises the following steps: converting original data into a to-be-processed electronic document in a specified format; performing primary page classification processing on the to-be-processed electronic document to obtain a primary page classification result; based on the primary page classification result, determining a page information extraction strategy for the to-be-processed electronic document; extracting key information elements and a page text set from the to-be-processed electronic document based on a page information extraction strategy; and performing structured processing on the key information elements and the page text set to obtain structured data corresponding to the to-be-processed electronic document, and storing the structured data. According to the method, deep intelligent digital processing can be carried out on the original data from different data sources, the processing efficiency is high, and subsequent data processing analysis and retrieval utilization are facilitated.

Systems and methods are disclosed for training an electronic document extraction model, including the generation of the training data to train the model based on sampling a pool of electronic documents based on a rareness metric of the documents. Each electronic document has a document-level rareness metric generated, with the document-level rareness metric being based on one or more of a structural rareness metric or a content rareness metric of the document. The structural rareness metric measures the rareness of the document structure, which may be irrespective of the text content of the document. The content rareness metric measures the rareness of the document content, which may be irrespective of the document structure. The electronic documents are sampled based on the document-level rareness metrics to increase the number of rare documents in the training data without unduly biasing the sampling to optimize the training data for training the extraction model.

A method for processing one or more electronic documents for enhanced search includes defining a bounding box around each key and value of key-value pairs in a first schema file, tagging coordinates of a key corresponding to a first bounding box and coordinates of a value corresponding to a second bounding box in the first schema file. Furthermore, obtaining a first inference file, detecting coordinates of a key corresponding to a third bounding box, and determining coordinates of a fourth bounding box and the value of the first inference file that are determined by applying a normalization operation. Thereafter, extracting value encompassed by the fourth bounding box of the first inference file and automatically creating a searchable index of the first inference file with searchable key-value pairs. The method achieves an efficient and accurate clustering of data items with an accurate, meaningful, and formal objective function.

A method, an apparatus, and a computer-readable storage medium for executing obligation management. One or more document portions are extracted from an electronic document using at least one machine learning model selected from a plurality of machine learning models based on at least one parameter associated with the electronic document. One or more entities are identified in one or more document portions of the electronic document. The entities are sent to a generative artificial intelligence (AI) model. The generative AI model is configured to generate one or more rules defining one or more obligations associated with one or more entities. One or more rules are executed to monitor compliance with one or more obligations by one or more entities.

Systems and methods receive a selection of and filter documents for an electronic document search. A semantic vector analysis module is applied to text of the filtered documents to build respective floating point vectors for each of the documents, and the respective floating point vectors are stored to a vector database. A textual query is received and vectorized to produce a query vector representing search criteria. The vector database is searched in accordance with the query vector to identify similar documents that satisfy a similarity measure. A ranked list of vectors that include highest similarity scores relative the query vector is generated and similar documents are identified. A neural network is used to process document data of the similar documents to generate a textual response to the textual query, and control signal(s) are transmitted to a user device to initiate displaying the generated textual response and question answer events.

In various examples, systems and methods are disclosed relating to displaying images in chatbot / NPC / virtual agent / digital avatar / etc. responses. A system can identify text corresponding to an image in an electronic document and can store a representation of the text in association with an identifier of the image. The system can receive an input prompt for a machine-learning model. The system can generate a response to the input prompt using the machine-learning model. The response can include the image responsive to identifying the representation of the text using a searching function and an output of the machine-learning model.

Techniques are described for a system document management comprising one or more processors having access to a memory. The system is configured to open an electronic document that is to be electronically signed, the electronic document having an active component connected to an external data source. The system is further configured to, while the electronic document is open, execute the active component to retrieve data from the external data source. The system is further configured to insert the data retrieved from the external data source into a field of the electronic document. The system is further configured to output, for display, a graphical user interface that presents the content of the electronic document to a user device.

Aspects of the present disclosure are directed to systems, methods, and computer readable media for executing actions for events associated with use of applications. A computing system can identify free text associated with an application to be evaluated for at least one of a plurality of events associated with a use of the application. The computing system can apply the free text to a machine learning (ML) architecture. The computing system can determine, based on applying the free text to the ML architecture, a value indicating a likelihood of occurrence of an event associated with the use of the application. The computing system can provide to a generative ML model, a model input based on the free text and the value, to obtain data for an electronic document characterizing the event. The computing system can execute an action using the data for the electronic document.

The method comprises the steps, implemented by a computer system, of:converting electronic documents in a first database (1100) into vectors of numbers;inserting said vectors into a second vector database (1200);receiving, via a conversational agent (1400), a query to search for information in the electronic documents;generating, via a generative AI agent (1500) using the second database (1200), a response to the query;in the event of non-validation of the response, receiving, via the conversational agent (1400), context data related to the circumstances of non-validation of the response;identifying a source document to be updated in the first database (1100) on the basis of the query and / or context data;determining an update action based on the context data and / or the query, via the generative AI agent (1500);executing an update of the first database (1100) based on the update action.

Provided is a method executed by one or more computers, the method including acquiring a user decentralized identifier for identifying a signatory who provides a handwritten signature to an electronic document, calculating a hash value of a document file representing the electronic document provided with the handwritten signature by the signatory, and generating a certificate including the user decentralized identifier and the hash value of the document file.

A method, a system, and a computer program product for generating a benchmarking dataset. One or more queries for generation of one or more summaries of one or more electronic documents are received. The queries are modified using one or more parameters associated with the electronic documents to generate modified queries. The electronic documents are sent to a generative artificial intelligence (AI) model. The generative AI model generates summaries of the electronic documents based on at least one of: the initial queries and the modified queries. One or more labels for the electronic documents are generated using the summaries.

A machine learning based computing method for extracting data from electronic documents, is disclosed. The machine learning based computing method includes steps of: receiving the electronic documents from data sources; converting formats of the electronic documents into first images associated with the electronic documents; classifying the first images associated with the electronic documents as second images including tabular data, and third images including non-tabular data, based on a CNN based machine learning model; determining tabular regions in tabular data associated with the second images by identifying bounding box coordinates indicating tabular regions in tabular data, based on a YOLO architecture based machine learning model; extracting first data from the tabular regions; extracting second data from non-tabular regions; and providing an output of the first data and the second data to second users on a user interface associated with electronic devices.

Embodiments disclosed herein include systems and methods for generating and editing digital documents, and applying electronic signatures to the documents. The system further performs forensic operations on digital documents for determining the authenticity of the document data or metadata and validating the status of document. The forensic operations may include, for example, security operations, audit trail analysis, and authenticity / verification analysis, which the system performs on an input document being scrutinized for authenticity and a master digital document purportedly corresponding to the input digital document. The system may apply a machine-learning architecture to identify discrepancies otherwise imperceptible to humans or to speed the review process. The system may generate a report for the user who submitted the forensics request indicating the results of the forensic operations. The available forensics operations and level of information about the master digital document are tailored to permissions afforded to the submitting-user.

The invention provides a campus intelligent terminal paper management service system and method, and the system comprises a data alignment module, a structured storage module, an intelligent query module and an association retrieval module: the data alignment module is used for carrying out the feature extraction and semantic alignment of obtained multi-modal education data, and carrying out the extraction and semantic alignment of the obtained multi-modal education data; obtaining a cross-modal feature data set; wherein the multi-modal education data comprises at least one of a paper homework scanning image, an electronic document and classroom audio and video; the structured storage module is used for constructing a teaching spatio-temporal topology database containing a subject knowledge point graph based on the cross-modal feature data set; the intelligent query module is used for performing semantic analysis on the obtained natural language instruction to generate a composite query instruction; and the association retrieval module is used for executing cross-modal association retrieval on the teaching spatio-temporal topology database based on the composite query instruction and outputting a comprehensive query result. By adopting the system, the paper material management level can be improved, and the teaching quality can be improved.