279 results about "Text document" patented technology

Enumerated source text passages may be determined based on one or more source text documents. The enumerated source text passages may include source text passage identifiers uniquely identifying the passages. A novel text passage including novel text portions may be determined based on a query and the enumerated source text passages. One or more of the novel text portions may be verified by a large language model to produce text verification information. A novel text generation message including novel text generated by the large language model may be determined based on the text verification information and sent to a client machine.

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.

Existing techniques for automated generation of test data for testing web applications need detailed requirement documents. The present disclosure receives a plurality of textual documents to extract context. Rephrasing the extracted context by implementing a plurality of rules and passing extracted context along with a first set of prompts to Large Language Model (LLM). Generating program, validator and first set of constraints for extracted context and generating test data by running the generated program. Assigning ranking to test data and selecting the test data with highest ranking. Statically refining the generated program by calling a mathematical library function on the highest ranked test data to generate structural information and modifying language of the second set of prompts passed to the LLM. Dynamically refining the generated program by passing feedback generated by executing the highest ranked test data on a web application and refining the response obtained.

A large language model (LLM) message generation system and method tor generating a response to a user message. The method includes: obtaining a text document specified by a user; chunking the text document into a plurality of chunks; generating text gloss data for each of the plurality of chunks based on the chunk and a predetermined prompt; storing the text gloss data for each chunk into a vector data store along with an identifier for the chunk and / or tine chunk itself; receiving a user message; querying the vector data store with a vector data store query, wherein the vector data store query is generated based on the user message; obtaining chunk(s) based on querying the vector data store with the vector data store query; generating a language model input based on the one or more identified chunks; and generating a response message based on inputting the language model input.

The present disclosure relates to systems, methods, and non-transitory computer-readable media that generates a temporal dependency graph. For example, the disclosed systems generate from a text document, a structural vector, a syntactic vector, and a semantic vector. In some embodiments, the disclosed systems generate a multi-dimensional vector by combining the various vectors. In these or other embodiments, the disclosed systems generate an initial dependency graph structure and an adjacency matrix utilizing an iterative deep graph learning model. Further, in some embodiments, the disclosed systems generate an entity-level relation matrix utilizing a convolutional graph neural network. Moreover, in some embodiments, the disclosed systems generate a temporal dependency graph from the entity-level relation matrix and the adjacency matrix.

Techniques described herein provide for generation of structured output for documents identified using automated near-duplicate detection. In one example, a system can receive a set of documents including at least one pair of similar documents determined to be similar to one another based on similarity scores generated using a predefined similarity scoring technique. The system can generate document groups by merging together pairs of documents that share at least one document. The system can, for each of the document groups, identify a representative document for the document group. The system can generate an output for display including a section for each document group, in which each section includes the representative document for the document group and, for each document in the document group, the similarity score relative to the representative document for the document group.

The present invention relates to the field of artificial intelligence technology, and in particular to a dynamic relevance enhanced retrieval generation system and method driven by an intelligent knowledge graph. The knowledge graph construction module of the present invention identifies and extracts entities and entity relationships from a text document collection with a massive amount of text documents to construct a knowledge graph and a knowledge graph index, and optimizes the index of the knowledge graph, which greatly shortens the query response time and improves the user experience. The related document collection module performs in-depth analysis and relevance evaluation on the relationship between text documents and user queries to extract related text documents, thereby ensuring the diversity and depth of the retrieval results. The retrieval enhancement generation module retrieves relevant information from the knowledge graph based on user queries and related text documents, and combines the retrieved relevant information with the user query to generate an answer, which helps to achieve more fine-grained related content retrieval, thereby improving the quality of the answer.

This disclosure describes utilizing a generative document system to dynamically build and provide generative text documents using one or more generative artificial intelligence (AI) models. For example, the generative document system efficiently utilizes various systems and one or more generative AI models to determine intents and topics, curate topic sections, and generate a generative text document that includes a directed answer along with select curated topic sections for search queries. In various implementations, the generative document system performs additional actions that enhance the efficiency and accuracy of operations used to produce generative text documents. Additionally, in many cases, these generative text documents provide a foundation for providing an interactive, intuitive, wide-ranging, and flexible curation of answers to users that address the corresponding search queries.

The invention provides a preference learning method which can be applied to the technical field of artificial intelligence. The preference learning method comprises the steps that sample questions matched with preset standards are determined from a text document pre-trained by a large model, the preset standards comprise a forward standard and a reverse standard, and the sample questions are used for representing questions generated when a user interacts with the large model; inputting the plurality of sample questions into a large model to generate a plurality of question and answer pairs according to answers corresponding to the sample questions output by the large model; evaluating answers in each question and answer pair based on a forward standard by using a plurality of evaluation models to obtain an evaluation label corresponding to each question and answer pair; and training the large model by using preference data generated based on the evaluation label to obtain a target model aligned with the forward standard. The invention further provides a model application method, a preference learning device, equipment, a storage medium and a program product.

Herein from tabular data in a text document, a machine learning text classification pipeline infers natural language syntax and semantics to prepare the tabular data for graph analytics. In an embodiment, a computer infers a respective classification of each column in a table in a text document that contains natural language. Based on the classifications of the columns in the table, the vertices of the knowledge graph are automatically generated. Based on those column classifications and automatic analysis of a particular document portion that does not contain the table, the edges of the knowledge graph are automatically generated. The knowledge graph may be generated and operated as a property graph. Pipeline subsystems herein include column classification, edge type identification, and subject / object detection that provide sufficient semantic enrichment and context sensitivity to faster generate a more accurate knowledge graph than the state of the art.

Techniques described herein provide for automated detection of near-duplicate documents. In one example, a system can cluster documents into a set of clusters based on character frequencies associated with the documents. For a given cluster, the system can generate first similarity scores associated with every pair of documents in the cluster. The system can then select a filtered group of documents associated with first similarity scores that meet or exceed a first predefined similarity threshold. Next, the system can convert the filtered group of documents into matrix representations. The system can generate second similarity scores for every pair of matrix representations. The system can then identify documents, from among the filtered group of documents, associated with second similarity scores that meet or exceed a second predefined similarity threshold. The identified documents can be duplicate or near-duplicate text documents.

Herein are techniques for topic modeling and content perturbation that provide machine learning (ML) explainability (MLX) for natural language processing (NLP). A computer hosts an ML model that infers an original inference for each of many text documents that contain many distinct terms. To each text document (TD) is assigned, based on terms in the TD, a topic that contains a subset of the distinct terms. In a perturbed copy of each TD, a perturbed subset of the distinct terms is replaced. For the perturbed copy of each TD, the ML model infers a perturbed inference. For TDs of a topic, the computer detects that a difference between original inferences of the TDs of the topic and perturbed inferences of the TDs of the topic exceeds a threshold. Based on terms in the TDs of the topic, the topic is replaced with multiple, finer-grained new topics. After sufficient topic modeling, a regional explanation of the ML model is generated.

The invention relates to the technical field of natural language processing, in particular to an RAG retrieval process optimization method and system based on a fine-tuning embedding model, and the method comprises the steps: determining the topic expression distribution degree of each topic in each long text document; obtaining all window sequences of each text segmentation topic based on the topic intensity sequence of each text segmentation topic by adopting a sliding window algorithm, and determining the topic mutation degree of each window sequence of each text segmentation topic based on the difference of the average distribution condition between the first half element and the second half element in each window sequence, short texts under all text segmentation themes are obtained; and determining the text topic concentration ratio of each short text, and optimizing the RAG retrieval process in combination with a large language model. According to the method, the text segmentation quality is improved by analyzing the topic expression distribution conditions of different topics in the long text document.

The invention discloses a retrieval enhancement generation method based on a multi-modal document. The method comprises the following steps: S1, data construction; s2, feature extraction of a multi-modal knowledge searcher is carried out; s3, carrying out feature mapping on the multi-modal knowledge retriever; s4, calculating the relevancy of the multi-modal knowledge retriever; and S5, multi-modal answer generation: the large language model generates a text reply according to multi-modal input. According to the method, a multi-modal document formed by combining pictures and texts is used as a knowledge carrier, and a multi-modal retrieval enhancement generation scheme is designed. Compared with an existing end-to-end model scheme, the scheme is based on a retrieval enhancement generation framework, and the accuracy and interpretability of answers are guaranteed; compared with a retrieval enhancement generation scheme using a text document as a knowledge carrier, the scheme has the advantages that visual information is added to the document to construct the multi-modal document, the knowledge retriever and the answer generator are improved to utilize the multi-modal document, and the accuracy of the knowledge-intensive visual question and answer task is improved.

The invention belongs to the technical field of natural language processing and knowledge engineering, and discloses a method, a system, equipment and a medium for automatically constructing a multi-field scientific knowledge base, and the method comprises the following steps: based on an input identifier list or a field search word, retrieving a full text of a literature, analyzing and converting the full text into a structured text; combining a pre-configured large language model with a field cue word, extracting key information in the structured text, and outputting structured data; the automatic script filters irrelevant, repeated or incomplete structured data according to a pre-configured filtering rule; performing standardization processing on the filtered structured data so as to realize the consistency and comparability of the data; and inserting the standardized structured data into an interactive knowledge base or database, establishing data association, and verifying association logic. The method supports cross-field rapid adaptation, solves the problems of low efficiency and high error rate of a traditional method, and can be widely applied to the fields of biomedicine, material science, synthetic biology and the like.

The present invention relates to a method to provide comprehensive key vocabulary for search involving the steps of receiving (101) a user-specified term or phrases with text cleaning (101A), identifying (102) domain specific vocabulary, extracting (103) relationship between plurality of key phrases in a text document, deriving (104) a knowledge graph that involves in establishing relationship between two phrases and a connection to form edge relationship thereby deriving a lemma, improving (105) the quality of the key phrases, storing (106) phrases, document ids and statistical details about phrases, ordering (107) the phrases identified using the ranking criteria based on the phrases statistics, selecting (108) and returning the best key phrases as search results and showing (109) the search results as word cloud for the users to quickly identify the key vocabulary.

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support intelligent model selection for style-specific digital content generation. For example, a system that provides a digital content generation service may include a trained style detection model may receive reference digital content items from a user and extract a user style embedding that represents a style preference of the user. In some implementations, the reference digital content items may include text documents or images provided or selected by the user. The system may compare the user style embedding to a plurality of model style embeddings that each correspond to a respective generative artificial intelligence (AI) model to generate a ranked list of generative AI models. The system may access one or more highest ranked generative AI models from the ranked list to generate novel digital content based on a prompt from the user.

This disclosure presents an image generation system designed to generate a series of contextually-persistent visual images for a text document. For instance, the image generation system utilizes multiple computer-based models, entity identifiers, and visual entity embeddings to create multiple synthetic images for a given text document. These synthetic images share a consistent theme and style. Additionally, the synthetic images include the same characters, places, and objects. Indeed, the image generation system implements seamless and consistent visual representations of the entities throughout the text document.

Training and using a machine learning model for data extraction is provided. The method comprises receiving keys of interest received from a user through an interface and receiving a batch of documents containing unstructured text. Unstructured text of a first document is processed to extract structured text. The model predicts text classifications of the structured text according to the keys of interest. The predicted text classifications are output to the user through the interface. Annotations to correct any incorrect predictions are received from the user, and the model is retrained according to the annotations. The above steps are repeated for less than ten additional documents from the batch until the model has been trained to predict text classifications with a specified level of accuracy. The trained model then classifies extracted structured text in the remaining documents in the batch.

Techniques for automatic intake and handling of the documents are discussed herein. A system may automatically classify a received document as image or text, and based on the classification, further process the document to determine a scene class for image documents, and a document category for a text document. In examples, the system may use trained machine-learning models for performing one or more tasks, and provide training data for training the ML models. Further, based on the document category and characteristics of the text, the system may determine and populate associated fields in insurance records with values from the document, and determine further processing actions and associated priorities.

A method includes obtaining a text document, generating a first mark-up of the text document by a first natural language processing (NLP) model, and displaying the text document and the first mark-up. The method also includes receiving a user interaction with a portion of the first mark-up. The portion of the first mark-up of the text document may be associated with a portion of the text document. The method additionally includes, based on the user interaction with the portion of the first mark-up, selecting a second NLP model by which to process the portion of the text document, where the second NLP model is different from the first NLP model. The method further includes, based on selecting the second NLP model, generating a second mark-up of the text document by processing at least the portion thereof by the second NLP model, and displaying the text document and the second mark-up.

Disclosed is a mutually generative artificial intelligence system based on multi-dimensional spatiotemporal information vector graphics, relating to the field of artificial intelligence and engineering applications. Based on a geographic information system (GIS) or a computer-aided design (CAD) platform and a data source, a multi-dimensional vector spatiotemporal large model terminal, a multi-dimensional spatiotemporal information processing agent terminal, and an intelligent information system application terminal are constructed. For multi-dimensional spatiotemporal data such as two-dimensional and three-dimensional vectors and temporal states, a multimodal spatiotemporal large model having an understanding capacity for an engineering professional knowledge system, a data processing flow, multi-dimensional vector graphics, and thematic graphics-text documents is pre-trained to achieve the mutual expression and generation of engineering multi-dimensional vector graphics and thematic graphics-text documents and to form an intelligent engineering graphic data processing application.

Aspects of this disclosure are directed to techniques that enable efficient automated text-to-speech pronunciation editing for long form text documents. A computing device comprising a memory and a processor may be configured to perform the techniques. The memory may store a text document. The processor may process words in the text document to identify first candidate words that are predicted to be mispronounced during automated text-to-speech processing of the text document. The processor may next filter the first candidate words to remove one or more candidate words of the first candidate words and obtain second candidate words that have fewer candidate words than the first candidate words. The processor may then annotate the text document to obtain an annotated text document that identifies the second candidate words, and output at least a portion of the annotated text document that identifies at least one candidate word of the second candidate words.

In one implementation, a device receives relevancy parameters from a user interface indicative of how relevant different portions of a text document are to a user. The device segments the text document into segments based on the relevancy parameters. The device generates a summary of the text document using the segments. The device provides, to the user interface, the summary of the text document and an indication of how much each of the segments contributed to the summary.

The invention provides a document image-text integrated intelligent understanding and processing method and system, and relates to the technical field of artificial intelligence. Comprising the following steps: separating image-text elements and establishing context association; calling a graph vectorization engine, and executing intelligent vector conversion processing on the rasterized illustration; performing hierarchical classification, attribute endowing and structured reconstruction on the vector primitives in combination with text semantic analysis to generate vector entity objects with complete attributes; the text elements and the vector entity objects are jointly input into a pre-trained multi-mode large language model, unified knowledge expression containing vectors, topology, attributes and document contexts is generated, and applications such as question answering, editing, abstracting and reporting of image-text integration are supported. According to the method, the problems of graph semantic loss, difficulty in fine analysis and the like caused by vector-to-grid illustration in papers, reports and other types of documents are solved, the overall understanding depth and interpretability of AI for complex image-text documents are remarkably improved, and high-performance semantic understanding capacity is provided for document-based training and intelligent processing.

A system and method for an AI-based Automated Software Application Development Platform which intelligently gathers requirements from a business analyst and generates a runnable software application from those requirements. The AI-based platform enables the business analyst with no technical knowledge to visually model their requirements rapidly without requiring any programming Using the abstract requirements architecture and the platform, the analyst creates requirements models for the application's business processes and business tasks for each of the processes, including defining the supporting information needs. The system intelligently assists the user in defining the information needs for each of the business processes and their tasks. Once the requirements models are created by the analyst with the help of the platform, the platform automatically designs and creates an appropriate persistent canonical data model for the application from the defined information needs. The platform then creates a runnable software application. The system also provides a template for the user to input their requirements in descriptive text, and accepts requirements document for applications as written free form text or text documents written in English as input, and in a non-English language that the system automatically translates them into English. The system provides a method for reverse engineering an existing application, including generating a requirements document for the existing application such as a legacy modernization or digital transformation an old applications for redesigning, updating or revising them to accommodate a new requirements set for such applications.

To provide an invention support system which enables efficient achievement of superior inventions without requiring a lot of human resources.SOLUTION: An invention support system 100 is provided, comprising a user interface configured to transmit a text document to an artificial intelligence service provision site that responds in an interactive manner, requesting the site to apply problem solving theory, solve a problem, and establish an invention.SELECTED DRAWING: Figure 2

Methods and apparatus to facilitate annotation projects to extract structured information from free-form text using NLP techniques. Annotators explore text documents via automated preannotation functions, flexibly formulate annotation schemes and guidelines, annotate text, and adjust annotation labels, schemes and guidelines in real-time as a project evolves. NLP models are readily trained on iterative annotations of sample documents by domain experts in an active learning workflow. Trained models are then employed to automatically annotate a larger body of documents in a project dataset. Experts in a variety of domains can readily develop an annotation project for a specific use-case or business question. In one example, documents relating to the health care domain are effectively annotated and employed to train sophisticated NLP models that provide valuable insights regarding many facets of health care. In another example, annotation methods are enhanced by utilizing domain-specific information derived from a novel knowledge graph architecture.

The invention discloses a financial business document verification method and device, equipment and a medium, and relates to the technical field of computers, and the method comprises the steps: obtaining a verification request of a current business document, and reading the text content in the current business document to obtain a formattless text document; the current business document is a document obtained by filling an initial data table stored by a business system based on business data; determining a target rule verification template from a template library according to the verification request; performing field extraction operation on the text document and the to-be-verified business table based on the target rule verification template to obtain a corresponding filling value and an actual business value; the to-be-verified business table is a latest data table stored by the business system based on the business data corresponding to the approved data; and performing comparison verification on the filling value and the actual business value to obtain a business verification result. Therefore, the accuracy of data extraction can be improved, and the accuracy of checking the data in the to-be-checked business table and the data in the business document stored in the business system is 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.