3213 results about "Text entry" patented technology

An interactive cyber-security user-interface for cybersecurity components can receive a voice input from a user as well as ii) a text input as a user input. The interactive cyber-security user-interface works with a set of differently trained LLMs to carry out tasks on behalf of the user input. The interactive cyber-security user-interface cooperates with the set of differently trained LLMs, which are grouped together to operate as an orchestrated system to provide different tasks. The tasks can include a collection of supplementary information, a summarization of cyber security information, translating a query in the natural human speech format into the required search syntax, how to integrate with an API, acting as a first line of support to user inquiries, a suggested response to a cyber security issue, etc. The interactive cyber-security user-interface for the cybersecurity components acts as the user interface for one or more of the cybersecurity components.

The invention provides a cross-modal image-text retrieval processing method and a cross-modal image-text retrieval processing system, which are applied to the field of information retrieval, and the method comprises the following steps: obtaining a query text input by a user in an image-text retrieval process; encoding the query text through a text encoder to generate a query text feature vector; through a cross-modal image-text retrieval model, similarity matching is carried out based on query text feature vectors and multi-modal embedding representations stored in an external knowledge base, related results corresponding to the multi-modal embedding representations larger than a matching threshold value are returned, and the multi-modal embedding representations are used for representing joint features of images and texts; when the related result comprises the image and the text at the same time, the related result and the query text are input into a preset multi-modal large model, image question answering with text assistance is carried out, and a retrieval result output by the multi-modal large model is obtained; according to the method, the semantic association between the image and the text can be better captured, so that the accuracy of image-text retrieval is improved.

Provided in the embodiments of the present disclosure are a dialogue content generation method, a model training method, a prompt text generation method, a device and a storage medium. The dialogue content generation method comprises: receiving an input question; determining whether the question needs to be answered in combination with external knowledge; if the question needs to be answered in combination with external knowledge, acquiring target knowledge from a preset knowledge base on the basis of the question, generating prompt text corresponding to the target knowledge, inputting into a dialogue content generation model the question, the target knowledge and the prompt text corresponding to the target knowledge, and generating dialogue content by means of the dialogue content generation model, wherein the prompt text is used for prompting the dialogue content generation model to answer the question with reference to the target knowledge; and if the question does not need to be answered in combination with external knowledge, inputting the question into the dialogue content generation model, and generating dialogue content by means of the dialogue content generation model, wherein the dialogue content generation model is obtained by means of performing fine tuning on a pre-trained language model.

Systems and methods are described for inputting text input to a trained machine learning model; generating, using the trained machine learning model and based on the text input, a single-layer image comprising a plurality of objects; segmenting the single-layer image to generate a plurality of images, each image of the plurality of images comprising a depiction of a respective object of the plurality of objects of the single-layer image; extracting, from the text input, a portion of the text input describing a background portion of the single-layer image; generating, using the trained machine learning model and based on the extracted portion of the text input, a background image; and generating the multi-layer image based on the plurality of images and the background image.

The embodiment of the invention discloses a context-based big language model dialogue processing method and device and electronic equipment, and can solve the problem of how to provide a dynamic management method based on context similarity analysis and intelligent screening to optimize big language model interaction. The method comprises the steps of obtaining current text information input by a user; coding the current text information and a plurality of pieces of pre-stored historical dialogue information to obtain a current text vector and a plurality of historical dialogue vectors; according to the semantic similarity between each historical dialogue vector and the current text vector, screening the plurality of historical dialogue vectors to obtain screened target historical dialogue information; splicing the current text information and the screened target historical dialogue information to obtain a target input text; the target input text is input into a target language model, target output response information is obtained, and the target language model comprises a large language model.

The invention discloses a large-model real-time voice text intention recognition method and system based on text prediction, and the method comprises the steps: obtaining the real-time voice data of a user, carrying out the real-time voice recognition processing through a streaming voice recognition interface, and obtaining a part of transcriptional text; inputting the partial transcription text into a mask language model for text prediction, and generating a plurality of high-credibility complete sentence candidates; based on the complete sentence candidates, the complete sentence candidates are input into a large language model in parallel for intention recognition, a corresponding intention result is obtained, and a mapping relation between the candidate sentences and the intention recognition result is established; and obtaining a sentence completely expressed by the user, calculating the similarity between the complete actual sentence and a plurality of high-credibility complete sentence candidates through a multi-level text similarity algorithm, selecting the candidate sentence with the highest similarity score, and directly obtaining a corresponding final intention recognition result based on the mapping relationship. The objective of the invention is to solve the technical problem of high response delay of an existing voice intention recognition system.

Embodiments described herein provide a video generation framework built on a decoupled multimodal cross-attention module to simultaneously condition the generation on both an input image and a text input. The video generation may thus be conditioned on the visual appearance of a target object reflected in the input image. In this way, zero-shot video generation may be achieved with little fine-tuning efforts.

The invention discloses an SQL (Structured Query Language) statement generation method and device based on a large language model, equipment and a medium. The method comprises the following steps: obtaining a user query text; inputting the user query text into a pre-trained Text2SQL (Structured Query Language) model to obtain an SQL statement output by the Text2SQL model; the SQL statement is input into a pre-trained SQL2Text model, so that a target query text output by the SQL2Text model is obtained; calculating semantic similarity between the user query text and the target query text; and if the semantic similarity is greater than or equal to a preset target value, determining that the SQL statement is the SQL statement corresponding to the user query text. By means of the method, the problem that the accuracy rate of SQL statement generation is low is solved, and the accuracy rate and efficiency of SQL statement generation are improved.

A system for sentiment analysis in language texts based on deep learning, consisting of: a preprocessing unit configured to receive and normalize raw text input from various sources, with the unit performing tokenization, punctuation removal, text stemming, and standardization to prepare text data for analysis; an embedding unit that is operationally connected to the preprocessing unit and is configured to convert normalized text data into numerical representations, wherein the embedding unit also contains pre-trained language embeddings to capture semantic relationships in the text and dynamically selects embeddings based on the source and linguistic context of the text data; a sentiment analysis neural network (SANNet) operationally connected to the embedding unit, comprising layers of a convolutional neural network (CNN) for local pattern recognition and layers of a recurrent neural network (RNN) configured to capture sequential dependencies within text data, with the SANNet also including an attention mechanism to dynamically weight word relevance based on contextual meaning, and the SANNet being trained to interpret and classify complex sentiment patterns in the text; A context module integrated into the neural network for mood analysis. The module includes a bidirectional transformer layer for the simultaneous processing of forward and backward word dependencies, enabling the system to capture complex language structures, including sarcasm, irony, and mixed emotions, and thus perform an accurate mood classification; an output interpretation unit that is operationally connected to the neural network for sentiment analysis and is configured to convert probability-based classification results into human-readable sentiment categories with associated confidence levels, wherein the output interpretation unit also provides interface compatibility for external applications, thus enabling direct integration of sentiment classifications; A continuous training module connected to the neural network for sentiment analysis. The module is configured to use real-time user feedback and sentiment accuracy metrics to retrain the SANNet, thus ensuring adaptive learning for evolving language patterns and domain-specific contexts; a GPU / TPU-optimized processing card that is operationally connected to the neural network for sentiment analysis and is configured to accelerate deep learning calculations within the system, thereby enabling high-throughput, real-time sentiment analysis.

The invention discloses an intelligent data query method and device based on a large model and a medium, and relates to the technical field of large model application. The method comprises the steps of receiving a natural language query text input by a user, and determining a target interface group from pre-associated interface groups through a preset business line classification rule and a keyword matching algorithm; inputting the natural language input text into a pre-trained large model to analyze a parameter set required by interface calling; wherein the parameter set comprises at least two of the following items: a time range field, a business main body field and a statistical dimension field; calling an API interface in the target interface group according to the parameter set, and obtaining original business data; performing context supplementation on the original business data based on the business logic rule to generate a standardized data result containing business background description; and reconstructing the standardized data result according to a display format preset by a user, and outputting a combined visual result containing charts, tables and text description.

Systems and methods are described for generating, using the first trained machine learning model and based on text input, a single-layer image comprising a plurality of objects; generating a plurality of masks associated with the plurality of objects; determining a plurality of attributes associated with the plurality of objects; generating, using a second trained machine learning model, a plurality of textual descriptions respectively corresponding to the plurality of objects; inputting the plurality of textual descriptions, and the plurality of attributes, to the first trained machine learning model; generating, using the first trained machine learning model, a plurality of images respectively corresponding to the plurality of textual descriptions; and generating the multi-layer image by combining the plurality of images and by using the plurality of masks, wherein the plurality of images respectively correspond to a plurality of layers of the multi-layer image.

The invention discloses a mechanical arm operation method based on a multi-mode large visual language model, which comprises the following steps of: firstly, acquiring image data of a mechanical arm operation scene through a visual sensor, and acquiring a user instruction in combination with a voice recognition module or a text input module; and inputting the natural language instruction and the visual scene data into a multi-modal large visual language model for multi-modal task analysis. The model identifies the category, position and attribute of a target object in a scene, and generates a specific operation sequence in combination with an input instruction. And then, according to the operation sequence generated through analysis, the system directly drives the mechanical arm to execute corresponding tasks including specific actions such as grabbing, moving and placing of the target object, and in the task execution process, the mechanical arm sequentially completes the actions according to the operation sequence generated in advance. The problems that in the prior art, a mechanical arm is inaccurate in complex task instruction analysis, poor in operation flexibility and adaptability and poor in real-time response capacity in a dynamic environment are effectively solved.

Disclosed herein are methods, systems, and computer-readable media for regenerating a region of an image with a machine learning model based on a text input. Disclosed embodiments involve accessing a digital input image. Disclosed embodiments involve generating a masked image by removing a masked region from the input image. Disclosed embodiments involve accessing a text input corresponding to an image enhancement prompt. Disclosed embodiments include providing at least one of the input image, the masked region, or the text input to a machine learning model configured to generate an enhanced image. Disclosed embodiments involve generating, with the machine learning model, the enhanced image based on at least one of the input image, the masked region, or the text input.

Provided in the embodiments of the present disclosure are a question-answering processing method, and a device, a product and a storage medium. In the question-answering processing method, after a query instruction is acquired, target knowledge information that matches the query instruction can be acquired from among a plurality of pieces of knowledge information in a knowledge base, and the query instruction and content-parsed text that corresponds to the target knowledge information are input into a large language model for question-answering processing, wherein the content-parsed text that corresponds to the target knowledge information is obtained by means of performing content parsing on a target document element that corresponds to the target knowledge information, and when the target document element comprises a document element of a non-text modality, content parsing is performed on the target document element before the target document element is input into the large language model, such that the document element of the non-text modality in the target document element can be understood by the large language model, so as to provide question-answering reference knowledge with a relatively high reliability for the large language model. Therefore, the accuracy of answering of the large language model for the query instruction can be improved.

A method and apparatus for training a text-graph model, a device, and a storage medium, relating to the artificial intelligence. The method comprises: on the basis of a training set of graph-text sample pairs, performing iterative training on a model to be trained, to obtain a text-graph model; in a training process: selecting a graph-text sample pair from the training set of graph-text sample pairs, the graph-text sample pair comprising a sample image and a description text, and the sample image comprising at least two objects; obtaining mask images corresponding to the at least two objects and associated object class names, the mask images being used for distinguishing position regions of the objects in the sample image; inputting the sample image and the description text to a model to be trained, to obtain an image prediction noise of the sample image, and inputting at least two mask images and the associated object class names to the model to be trained to obtain object prediction noises respectively associated with the at least two mask images; and constructing a loss function on the basis of the image prediction noise and the object prediction noises, and by using the loss function, adjusting a parameter for the model to be trained.

The invention belongs to the technical field of artificial intelligence and big data analysis, and particularly relates to a scenic spot passenger flow trend prediction analysis method based on an AI big language model. According to the method, big data and artificial intelligence technologies are utilized, LBS data, scenic spot data, holiday and festival information, third-party maps, weather data and other multi-source data are comprehensively applied, a comprehensive multi-dimensional knowledge base is constructed, and meanwhile, the knowledge search and generation capability is improved by utilizing an RAG technology; through model pre-training and fine tuning, the universal model capability is developed, and a multi-agent system is further constructed; based on the natural language processing capability of the AI large language model, the LLM can intelligently analyze the text input of the scenic region administrator, drive the cooperative work among multiple agents, and achieve the intelligent prediction and analysis of the scenic region passenger flow trend. The method can more accurately predict the passenger flow trend of the scenic area.

The invention discloses a retrieval enhancement generation method based on token-level word embedding, and relates to the technical field of artificial intelligence and natural language processing. The method specifically comprises the following steps of: segmenting a text block, namely segmenting a document into paragraphs; enhancing the document information; the text coding is used for converting a text into a numerical vector; segmenting the problem to obtain a character array, encoding the character array, and converting the problem into a numerical vector; calculating the similarity between the problem and the text block by adopting the average maximum similarity to obtain a related text block; performing Prompt construction on the question and the related text block to obtain a prompt text; the prompt text is input into a large language model LLMs, and question answers conforming to the answer format are output; and in combination with the question answers and the related text blocks, quoting and marking the question answers to obtain answers with quotations. According to the method, a token-level text embedding method is utilized to vectorize questions and text blocks, so that the accuracy of RAG retrieval is improved, and the confidence of LLMs answers is increased.

The invention provides a bank financial management product recommendation method and system, and the method comprises the steps: firstly collecting a multi-modal dialogue data flow which is generated by interaction of a user terminal and an intelligent customer service system and contains voice input signals and text input contents, and then carrying out the semantic recognition of the voice input signals, and generating a structured demand description set; performing entity extraction and intention analysis on text input content to generate a multi-dimensional semantic tag set, performing time-space alignment and fusion on the text input content and the multi-dimensional semantic tag set to form a user portrait enhanced feature matrix, and performing multi-level association analysis on the user portrait enhanced feature matrix and a financial product knowledge graph based on a deep matching model to generate an initial recommendation result queue; and finally, according to the real-time environment perception data, queue context perception optimization is carried out, a dynamic adaptive recommendation list is generated, and visual rendering output is carried out through a multi-channel interaction interface, so that accurate and dynamic financial management product recommendation is realized by utilizing multi-modal data, deep analysis and combination with a real-time environment.

The invention discloses a visual language model illusion suppression method based on self-adaptive dynamic attention intervention. The method is used for reducing the problem that a visual language model generates wrong associated information. The method comprises the steps that text input, visual input and historical response are acquired, and an attention accumulation vector is initialized; in the layer-by-layer calculation process of the language model, dynamically adjusting a non-normalized attention matrix, enhancing the weight of a visual sensitive attention head, and performing visual Token pruning in a deep network to optimize cross-modal information interaction; and finally, generating an output Token based on the adjusted attention mechanism, and carrying out loop iteration until a complete response is generated. According to the method, a method of combining text deviation correction through self-adaptive attention head modification and visual attention convergence-based Token pruning is adopted, so that the performance of the model in a multi-modal task is remarkably improved, and the illusion phenomenon caused by a language modal dominant reasoning process is effectively relieved.

The invention relates to the technical field of artificial intelligence and the field of financial science and technology, and discloses a speech recognition error correction method, which comprises the following steps of: training and generating a target field Chinese character text understanding model by collecting dialogue Chinese character texts of a target field; collecting pinyin data, and training to generate a pinyin understanding model; receiving to-be-detected voice data and converting the to-be-detected voice data into to-be-detected Chinese character texts and pinyin texts Inputting the Chinese character text and the pinyin text to corresponding understanding models to generate semantic features and pinyin features; combining the semantic features and the pinyin features to form joint features; inputting the joint features into a speech recognition error detection model, generating error probability distribution of each word, and outputting an error tag; and correcting the Chinese character text through a template error correction module based on the error tag. According to the method, more comprehensive feature representation is formed by combining semantic features and pinyin features, and the accuracy of speech recognition error detection is improved; and a template error correction module is utilized to effectively correct identification errors.

A method is provided. The method includes: obtaining a first sample dataset; inputting at least one first question text corresponding to at least one piece of first sample data into a dialog model separately to obtain at least one first answer prediction result; inputting each second question text into the dialog model to obtain a second answer prediction result output by the dialog model; inputting the second answer prediction result into a reward model to obtain a score of the second answer prediction result output by the reward model; determining a comprehensive loss based on the at least one first answer prediction result, a first answer text of each of the at least one piece of first sample data, and a score corresponding to each of at least one piece of second sample data; and adjusting at least one parameter of the dialog model based on the comprehensive loss.

The invention relates to the technical field of man-machine interaction, in particular to a mixed interaction system based on an AI large model, which comprises a multi-modal question acquisition module, a preprocessing and classification module, a retrieval and analysis module, a mixed processing module, a result integration and output module, an interaction module and an answer display module, the problem preprocessing and classification module performs text cleaning, semantic analysis and classification, and the hybrid processing module distributes tasks to natural language processing or AI large models according to a data complexity threshold value, so that the problem processing flow is optimized, the system performance is improved, and the system performance is improved. According to the system, model parameters are optimized based on a machine learning classification algorithm and transfer learning training, classification accuracy is ensured, the system adapts to problem changes, through cooperation of all the modules, the existing technical problems are effectively solved, the man-machine interaction efficiency and quality are improved, the system has wide application prospects in multiple fields, and the man-machine interaction technology is promoted to be developed to be intelligent and efficient.

The invention relates to a method, a device, equipment and a product for executing tasks. The method comprises the following steps: acquiring an operation sequence of a user for a first task on a user interface, wherein the operation sequence comprises operation steps required for completing the first task; the method includes converting the operating steps into a model context protocol service based on the sequence of operations. The method includes obtaining a user input, the user input indicating a second task, the user input including at least one of a text input or a voice input. The method includes determining a model context protocol service to be executed based on the second task, the model context protocol service to be executed being associated with the second task. The method includes executing a second task based on task parameters of the second task and service parameters of a to-be-executed model context protocol service, the second task being composed of the to-be-executed model context protocol service associated with the second task, and the task parameters of the second task determining and displaying an execution result of the second task based on user input.

The invention relates to the technical field of semantic analysis, can be applied to business scenes of financial science and technology, medical treatment and health and the like, and discloses an intelligent correction method and device based on semantic analysis, equipment and a medium. The method comprises the following steps: extracting a structured document content from an explanation data source, extracting an auxiliary explanation text corresponding to each unit in the structured document content from the explanation data source, carrying out semantic understanding processing on the structured document content and the auxiliary explanation texts by utilizing a semantic analysis module to generate a semantic analysis result, and executing a correction operation through an intelligent correction module based on the semantic analysis result. And after the correction operation is completed, the structured document content and the auxiliary explanation text are input into a format reconstruction module, and a specification-combined document is generated and output. According to the method, semantic understanding is carried out by combining structured document content and the auxiliary explanation text, and an automatic and standardized document compliance processing flow is realized based on intelligent correction and format reconstruction driven by a compliance semantic analysis result.

The invention provides a financial fraud detection method based on a large language model. The method comprises the steps of obtaining a to-be-recognized text; performing word segmentation on the to-be-recognized text through the target word segmentation tool and the financial fraud dictionary, and determining a fraud sensitive word list; calculating the weight of each sensitive word in the sensitive word list according to a target algorithm to obtain a sensitive word weight feature vector; inputting the sensitive word weight feature vector and a to-be-recognized text into a large language model, and determining a context semantic vector of the sensitive word in combination with a word embedding technology; determining the similarity between the context semantic vector of the sensitive word and a preset financial fraud type semantic vector; and determining a financial fraud type according to the similarity. Through the implementation of the method, the generalization ability of the pre-training model is utilized to capture text deep semantics, priori knowledge is injected in combination with a sensitive word weight mechanism, the model is guided to focus high-risk vocabularies, the defect of a traditional method in semantic comprehension is overcome, the financial fraud recognition rate is increased, and the omission ratio is reduced.

The invention, which relates to the technical field of intelligent interaction, discloses an AI-based adaptive scene intelligent interaction system comprising a multi-modal data acquisition module, a modal preprocessing module, a multi-modal embedded coding module, an intention fusion and representation module, a service scene matching module and a service execution and reinforcement learning module. The method comprises the following steps: acquiring multi-modal original data in a user interaction process, including voice signals, text input and user behavior tracks, and synchronously recording an acquisition timestamp; according to the method, through a multi-modal unified embedding and dynamic weighting mechanism, the problem of characteristic dimension imbalance is effectively solved, and the user intention recognition accuracy is improved; meanwhile, reinforcement learning and a multi-factor scoring model are combined, personalized scene matching and dynamic response are achieved, the adaptive capacity and service accuracy of the system in a complex environment are improved, and therefore the stability and user experience of the intelligent interaction system are remarkably optimized.

In some embodiments, while a keyboard is visible in a three-dimensional environment, the computer system detects a gaze of a user move from a position away from a first key of the keyboard to the first key. In some embodiments, in response to detecting the gaze of the user moving from the position away from the first key to the first key, in accordance with a determination the one or more criteria are satisfied, the computer system initiates a process to select a first character corresponding to the first key for entry. In some embodiments, in response to detecting the gaze of the user moving from the position away from the first key to the first key, in accordance with a determination that the one or more criteria are not satisfied, the computer system forgoes initiating the process.

A neural network system is trained to generate textual reports (that is, medical reports, such as radiology reports) from one or more medical images, by fine-tuning a pre-trained neural network system (a visual language model, "VLM") operative, upon receiving an input comprising at least one image and a textual input, to generate a value indicative of a predicted likelihood of one or more candidate text continuations of the textual input. The fine-tuning of the neural network system is performed to reduce the value of a cost function which includes a first prediction cost term based on a first training database including first training datasets of at least one medical image and an associated text report, the first training datasets corresponding to first individuals. The first prediction cost term further includes a cost value for each individual, inversely dependent on a likelihood value of the associated textual report, conditioned on the at least one medical image, and created by the neural network system.

The invention provides a construction and training method and device for an eye fundus image multi-task model, and belongs to the field of image processing, and the method comprises the steps: S1, collecting and sorting a public eye fundus image data set, constructing an image text pair according to a real label, and carrying out the two-stage training of a multi-modal large language model, the multi-mode large language model comprises a visual encoder, a visual projector and a large language model; s2, inputting the image data # imgabs0 # into a visual encoder in the trained multi-modal large language model to obtain an enhanced visual feature # imgabs1 #, and extracting a visual feature # imgabs3 # from the # imgabs2 # through a visual projector; and S3, embedding the text input # imgabs4 # to obtain a text feature # imgabs5 #, splicing the text feature # imgabs5 # with the visual feature # imgabs6 #, and inputting the spliced text feature # imgabs5 # and the visual feature # imgabs6 # into a large language model to generate a prediction text A. According to the method, a wide range of fundus image data is collected for training, multilevel lesion features in the fundus image are fully utilized, and the performance of the model for executing a fundus disease auxiliary diagnosis task can be effectively improved.

The invention discloses a multi-agent computer-aided engineering and a computer-aided design method. CAE simulation and design optimization for the design of a complex product are high in threshold and difficulty. According to the method, autonomous planning and automatic execution of CAE simulation and CAD design optimization tasks are realized by using a multi-agent technology based on a large language model. A user interacts with the multi-agent system in the modes of text input, voice, a graphical user interface and the like, the multi-agent system autonomously plans workflow steps according to the requirements of the user, all agents cooperatively execute the working steps in a labor division mode, CAE and CAD software is called to execute simulation and design change, CAE and CAD result data are read and analyzed, the task completion condition is evaluated, and the task completion condition is evaluated. And the task can be re-planned if necessary. The system can automatically process error information, and can notify a user to process when user intervention is needed. The performance of the system can be improved through fine tuning of a large language model and retrieval enhancement generation technology.