152 results about "Context based" patented technology

A system and method for automatic generation of database queries using zero-shot, context-based machine learning may output and / or execute database queries and / or analytics insights or plots based on text prompts, and may include or involve: wrapping a text prompt to include database structure information; generating, by a large language model (LLM), a query based on the wrapped prompt, where the query may include one or more database operations; and extracting data or information items from a database based on the query. Some embodiments may include additional prompt or query processing operations such as, e.g., wrapping queries to include corresponding database operations, validating that queries do not include malicious or undesirable commands, and automatically performing appropriate computer actions based on generated queries. Some embodiments of the invention may relate to databases and text prompts describing user actions input to a computer and collected by a desktop data collection software.

Disclosed herein are methods, systems, and computer-readable media for causing a machine learning model to generate improved answer data based on an extended reality environment. In an embodiment, a method may include receiving the query, identifying at least one extended reality component associated with the extended reality environment as relating to the query, the at least one extended reality component comprising at least one of an object, a recording, or transcript information, and generating a prompt based on the query and the at least one extended reality component. The method may further include transmitting the prompt to a machine learning model, in response to the transmitted prompt, receiving answer data from the machine learning model, and based on the received answer data, generating content in the extended reality environment.

The invention relates to the technical field of natural language processing, in particular to a context-based Prompt generative model optimization system, which comprises a context analysis module, a cue word generation module, a context optimization module, a semantic check module and a structure reconstruction module. According to the method, the context path and the semantic hierarchy information of the semantic unit are introduced, the fine degree of semantic matching degree recognition is improved, semantic guide deviation caused by statement template solidification is avoided, the cue words are recombined in combination with the semantic coherence weight and the logic dependency relationship, and the recognition accuracy is improved. The consistency and expression accuracy of the prompt content in the context are enhanced, the prompt word insertion sequence and connection mode are dynamically adjusted through a semantic conflict detection and structure rechecking mechanism, coherence and stability of a semantic structure and controllable generation of the prompt content are kept, semantic conflicts and expression chaos caused by static matching are effectively avoided in the generation process, and the generation efficiency is improved. And dynamic adaptation of prompt configuration and smooth optimization of language output are integrally realized.

The invention provides an intelligent education resource sharing and authority management system based on a block chain, and belongs to the technical field of intelligent education, and the system comprises a block chain network layer, a resource right confirmation module, an intelligent contract module, a distributed storage network, a cross-chain interaction gateway and a dynamic optimization module, comprising a resource authentication chain, an authority management chain and a cross-chain interaction chain, and the chains realize data intercommunication through relay nodes; the resource right confirmation module adopts a three-level Hash nested structure to generate a non-tampering digital fingerprint, and binding and uplink the educational resource metadata and the creator DID; the intelligent contract module comprises a dynamic authority management contract and a resource transaction contract, and supports an access control strategy based on context awareness. According to the intelligent education resource sharing system, the core problems that the right of the education resources is difficult to confirm, the sharing efficiency is low, and the right management is inflexible are solved, and the safe, intelligent and extensible intelligent education resource sharing system is constructed.

The invention relates to a large language model dynamic routing method and device based on context learning model characterization and a readable storage medium. Query is embedded and mapped to a language model input space by using a projection model, semantic alignment is realized, a representative evaluation set covering multi-dimensional capability is automatically screened from a benchmark question bank, and the representative evaluation set is used for evaluating the multi-dimensional capability. Performance characteristics of the model on an evaluation set are efficiently obtained at a time, and high-quality context model capability representation is formed; then real-time query embedding and context model capability representation are combined, a lightweight routing language model is used for supervised learning, so that the fine-grained model distinguishing capability is achieved, an increment embedding updating mechanism is designed, and when a new model is accessed or an old model is upgraded, cold start can be rapidly completed only through a very small number of fixed questions, so that the efficiency is improved. The calculation and maintenance cost is greatly reduced, and the accuracy, real-time performance and flexible expansibility of model routing are effectively improved.

The invention discloses a remote sensing target detection large model construction method based on context feature deep learning, and the method comprises the following steps: S1, dividing an original data set according to a strategy, forming a training set, a verification set and a test set, and guaranteeing that each subset has representativeness in sample number, scene diversity and target distribution; s2, constructing a remote sensing target detection large model by adopting a deep learning method based on context features, and fusing multi-level features and introducing a query optimization mechanism; and S3, performing model testing and performance evaluation, selecting optimal model parameters, performing comprehensive verification on the test set, and evaluating the comprehensive performance of the model in the aspects of detection precision, speed and robustness. According to the method, the pre-trained DINOv2 backbone network, the context feature enhancement module and the intensive supervision strategy are introduced, so that rich context information can be captured in a multi-scale and complex scene, and the detection precision is greatly improved.

The invention discloses a multi-source data intelligent question answering system based on a large language model and an application method, and the method provides a unified registration and publishing normal form of multi-source data, and carries out the unified question completion and question classification of context-based user question semantics through the large model. According to types, knowledge graph query, service interface API calling and document content understanding are divided, calling results are summarized and extracted through a large language model, and finally automatic generation of answers is achieved. The method has the capabilities of question semantic understanding and complex task decomposition and calling, and has the advantages that the large model can accurately understand user intentions and drive various applications to obtain question answers while the large model can be efficiently accessed to an application system, and the large model can be integrated into answer styles most convenient for users to understand; and the universality and accuracy of intelligent question answering are effectively improved.

The invention relates to the technical field of intelligent questioning and answering, in particular to an intelligent questioning and answering method based on context semantics and dynamic retrieval, which comprises the following steps of: splicing data to form a complete dialogue text, extracting a dialogue core theme by using a large model in combination with a thinking chain and small sample learning, and extracting the dialogue core theme based on the dialogue text and a current question. Generating a dynamic keyword set in combination with BM25 and BERT MLM, finally splicing the current problem, the keyword set and the core topic into a multi-dimensional semantic representation, and vectorizing the multi-dimensional semantic representation by using a BGE-M3 embedding model to generate an embedded vector; by combining context topic extraction and dynamic keyword generation technologies, the system can capture core intentions and semantic changes in multiple rounds of dialogues in real time, so that knowledge base retrieval does not depend on fixed keyword matching any more, but dynamically adjusts a retrieval strategy according to a dialogue context; therefore, the matching precision of the question and the knowledge base content is remarkably improved.

The invention discloses a vertical federal learning feature selection method based on context awareness, and belongs to the technical field of artificial intelligence and data privacy protection. According to the method, firstly, an unsupervised sparse network is utilized at a client to initialize the importance of local features so as to accelerate convergence and reduce calculation complexity; and then, obtaining the embedded representation of each client in a pre-training stage, and screening the embedded representation by combining context features through a server side, thereby indirectly identifying key features. In the feature selection stage, the client side performs local feature screening according to the significant embedded index issued by the server, and the influence of irrelevant features on calculation and communication is further reduced. According to the invention, an attention mechanism is introduced to dynamically evaluate contributions of different participants, so that fair weight distribution is realized. According to the method, through staged joint optimization, the communication and calculation cost in the federation training process is effectively reduced, and meanwhile, the prediction precision and interpretability of the model are improved.

The invention provides a multi-attribute controllable text generation method based on context-aware multi-granularity prompt fusion. The method comprises the steps of constructing a single-attribute prompt variant set, constructing an intra-layer multi-granularity prompt fusion module and performing general multi-attribute task prompt. According to the method, diversified soft prompt variants are trained on a single-attribute data set, so that the flexibility of single-attribute control is improved; an in-layer multi-granularity prompt fusion mechanism based on context awareness is designed, and an attribute prompt fusion weight is dynamically generated, so that hierarchical fusion of prompts is realized; by introducing universal multi-attribute prompt and adopting two-stage optimization training, the generation accuracy and text fluency under multi-attribute combination control are effectively improved. According to the method, flexible and efficient text generation control can be realized under different attribute combination conditions, and the method is widely applicable to multi-attribute controllable generation tasks in natural language processing.

The invention provides a multi-objective optimization method fusing a classifier and an active learning strategy, and belongs to the technical field of artificial intelligence and optimization design, and the method specifically comprises the steps: obtaining a sample data set containing a plurality of target performance function values; performing non-dominated sorting on the sample data set to extract a Pareto frontier point set, and extracting geometric features from the point set as a geometric feature vector sequence; using the geometric features and the corresponding convex marks to train a classifier model of a Transform architecture, wherein the classifier model supports a multi-head attention mechanism and a position coding structure; judging the convex confidence coefficient of the current Pareto front by using a classifier model, and obtaining a candidate scheme; and inputting the candidate scheme into a double-attention mechanism prediction model based on context reasoning, and predicting the multi-target performance of the candidate scheme. According to the method, the experiment frequency can be reduced, the target performance can be accelerated to be achieved, and the intelligence and efficiency of the multi-target optimization process are improved.

The invention discloses a language model training method based on context position coding and Fourier transform, which belongs to the technical field of computer natural language processing, and comprises the following steps: S1, preprocessing text data input by a user; s2, performing context position coding on the preprocessed text information; s3, after context position coding, Fourier transform is carried out on each relative position coding vector; s4, performing model pre-training and instruction fine tuning on the position coding vector after Fourier transform, and measuring the difference between a prediction result and a real target by using a minimization loss function; and S5, finally completing model tensor distributed parallel training. According to the method, the accuracy in the model training process is improved, the method is suitable for a long context scene, and accurate text generation can be realized.

The invention provides an urban area dynamic representation learning method based on context representation learning, and belongs to the field of urban function and spatio-temporal data mining, and the method comprises the steps: S1, constructing a continuous dynamic graph according to public transport data; s2, an urban area dynamic representation extractor under multiple time dimensions is constructed, the extractor is used for extracting structure information and semantic information in the continuous dynamic graph, nodes in the continuous dynamic graph are converted into representation vectors of fixed dimensions, and the representation vectors are dynamically updated along with time; s3, pre-training the extractor by using a plurality of prediction tasks, including designing a corresponding loss function to guide the extractor to learn more universal urban region characterization; and S4, utilizing the trained plurality of extractors to obtain the dynamic representation of the urban area, and combining the dynamic representation of the urban area with context representation learning to be applied to a plurality of downstream tasks. According to the method, comprehensive space-time semantic information can be captured, and the dynamic region representation of the city is dynamically captured from multiple time granularities.

The invention relates to the technical field of natural language processing (NLP), in particular to an intelligent self-adaptive document segmentation method oriented to an RAG system. The method comprises the steps that S1, a deep learning model is adopted to dynamically adjust the size and the step length of a window according to document content density, structure information and context semantics; s2, in combination with a window representation result, calculating the context semantic similarity of the segmentation blocks by adopting a language model, and automatically adjusting the size of an overlapping region based on the context semantic similarity; and S3, based on a context segmentation block representation result, associating the segmentation block with the context by introducing a BERT model, automatically adjusting an overlapping part and a window size, and optimizing a segmentation effect. The invention aims to provide the intelligent self-adaptive document segmentation method oriented to the RAG system so as to improve the document segmentation efficiency and quality and optimize the recall rate and the generation quality of the RAG system.

The invention discloses an AI processing method and system for long-term memory of mechanical design knowledge, and the method comprises the steps: carrying out the semantic analysis and feature extraction of heterogeneous knowledge, such as parameterization formulas, engineering charts, technological processes and design principles, in the field of mechanical design through a multi-agent collaborative learning system; and establishing a multi-dimensional modular knowledge base and context-based Prompt strategy coupling mechanism. The method solves the problems of low efficiency, insufficient analysis accuracy, limited innovation support capability and the like in the existing mechanical design knowledge processing technology, and particularly solves the technical problems of high calculation complexity, inaccuracy in reasoning, poor adaptability and the like when multiple types of design knowledge are processed. Through the implementation of the method, the processing efficiency of the design knowledge and the design automation level are remarkably improved, the technical threshold of a designer is reduced, the design period is shortened, efficient analysis, accurate matching and innovative application of the mechanical design knowledge are achieved, and powerful support is provided for intelligent development of mechanical design.

The invention provides an embedded data synthesis method and device fusing retrieval and large model distillation and a medium. The method comprises the following steps of: preprocessing an unstructured document in a vertical field, and dividing the unstructured document into multi-granularity text blocks with a hierarchical association relationship; forming a context based on the combination of the multi-granularity text blocks, injecting disturbance information corresponding to the priori knowledge in the vertical field into the context, calling a generative model to generate a retrieval query according to the context, and determining a target text block corresponding to the retrieval query as an initial positive sample; false negative sample text blocks are filtered according to the incidence relation between the text blocks, and a positive sample set and a negative sample set are formed; and constructing a comparative learning training sample, and training the semantic representation model by using the comparative learning training sample to generate an embedded vector for the retrieval task. According to the method, the retrieval task construction efficiency and authenticity can be improved, the positive sample coverage integrity is improved, and the contrast learning training stability and retrieval precision are enhanced.

A system and method for automatic generation of database queries using zero-shot, context-based machine learning may output and / or execute database queries and / or analytics insights or plots based on text prompts, and may include or involve: wrapping a text prompt to include database structure information; generating, by a large language model (LLM), a query based on the wrapped prompt, where the query may include one or more database operations; and extracting data or information items from a database based on the query. Some embodiments may include additional prompt or query processing operations such as, e.g., wrapping queries to include corresponding database operations, validating that queries do not include malicious or undesirable commands, and automatically performing appropriate computer actions based on generated queries. Some embodiments of the invention may relate to databases and text prompts describing user actions input to a computer and collected by a desktop data collection software.

The invention relates to the technical field of natural language processing, in particular to a Chinese text error correction method, device and equipment based on a context fusion thinking chain, and the method comprises the steps: carrying out the feature coding of Chinese text data to be corrected based on a pre-training language model, and executing the multi-granularity feature fusion, enhancing the context representation by applying a context awareness attention mechanism, detecting the position of an error character based on the enhanced context representation, and determining an error type; dynamically generating an error analysis process; and correcting errors in combination with a thinking chain reasoning process, and generating a corrected text and a corresponding thinking chain error correction basis. Therefore, through innovative combination of multi-granularity feature fusion and thinking chain reasoning, not only is the accuracy of Chinese text error correction improved, especially the capability of processing complex error types improved, but also the interpretability of the error correction process is realized, a clear and understandable error correction basis is provided for a user, and the practicability of an error correction system and the user experience are remarkably improved.

The invention relates to a man-machine mixed text detection method based on context expansion, which expands the detection range from a single target sentence to the context of the sentence by introducing a sliding window mechanism, and designs a method for weakening context noise interference to improve the accuracy and generalization of model prediction. The method comprises the following steps: firstly, collecting texts with the window length as a training set and a verification set, and extracting semantic features and coherence features for training to obtain a trained detection model; a sliding window mechanism is applied to a test set to detect all window texts, and a small sliding window step length is set, so that sentences in the window texts can obtain a plurality of prediction probabilities under different context conditions. And adopting a detection mechanism based on confidence to perform confidence weighting on the plurality of prediction probabilities to obtain a final probability that the sentence is the AI text.

Systems and methods for advanced event ranking and correlation for threat detection. A method includes real-time processing of events with stateful threat detection, combining immediate detection capabilities with sophisticated threat analysis. A method further includes multi-stage processing in a detection engine, where generic events from endpoint detection and response (EDR) agents are scored and enriched to provide extended context based on machine learning models for event scoring, enriched events correlation, and applying security rules to detect threats.

Systems and methods are disclosed for optimizing a first machine learning (ML) model using a second ML model. In some examples, a system generates modifications to the first ML model. Each of the modifications is associated with a respective node of the first ML model. The system tracks a processing characteristic corresponding to modified variants of the first ML model (corresponding to the modifications) processing a test dataset to generate respective results. In some examples, the system trains the second ML model based on context (the modifications and the respective changes). The system identifies, using the second ML model and based on the context (e.g., the training), a modification to the first ML model that adjusts the processing characteristic of the first ML model in a predetermined direction. The system modifies the first ML model according to the modification to generate a modified first ML model.

System and method including an artificially intelligent assistant are described. An example method includes, in response to initiation of an artificially intelligent assistant at a head-wearable device, capturing contextual data. The contextual data includes one or more of image data, audio data, and / or sensor data. The method includes determining, based on the contextual data, a contextual cue, and providing a portion of the contextual data and a portion of the contextual cue to the artificially intelligent assistant. The method includes determining, by the artificially intelligent assistant, a user request based on the portion of the contextual data and the contextual cue, and receiving a response to the user request. The response is generated using a machine learning model. The method further includes causing the head-wearable device to present the response.

The invention relates to the technical field of data processing, in particular to a large model reasoning optimization method based on context incremental updating, which comprises the following steps: processing a multi-modal data stream through timestamp alignment and a filtering algorithm, extracting features by adopting a shared encoder and a private encoder, and realizing feature decoupling through a depth information bottleneck principle. A dynamic emotion map is constructed by using Gaussian process regression and a random process algorithm, and self-adaptive updating control is realized by combining meta-learning and Bayesian optimization. Incremental state management is realized by adopting a neural Turing machine, reasoning consistency is guaranteed through a generative adversarial network, model parameters are optimized in combination with a digital twin system and reinforcement learning, and a mental health service response is finally generated through a conditional generation model and hierarchical reinforcement learning. According to the method, the problem of asynchronism of multi-modal emotion feature dynamic evolution and context increment updating is effectively solved, accumulated drift of emotion state tracking is eliminated, and the continuity of reasoning logic is guaranteed.

The invention discloses a target-driven navigation method and device based on context awareness and imitation learning, and the method comprises the steps: recognizing an object instance of interest in an image based on a target detector DETR, and constructing an object graph; based on context perception graph reasoning, in the navigation process, dynamic context information such as images, actions and memories serves as guidance, object features are projected to hyperplanes of corresponding contexts by means of a TransH method at each time step, the object relation is dynamically learned, and an intelligent agent can better understand the complex environment. Based on visual representation of Transform, visual features and graph features are fused, and spatial semantic information of the environment is better captured. Based on generative adversarial imitation learning, a new dynamic reward function is designed, and an intelligent agent is helped to avoid a deadlock state in combination with environment rewards. Based on a standard asynchronous dominant actor-commentator algorithm, an effective navigation strategy is trained by using a new reward function, and the navigation success rate and efficiency of the intelligent agent in an unfamiliar environment are improved.

The invention belongs to the technical field of information extraction, and relates to a hybrid time information extraction method and system based on context awareness. The method comprises the steps of collecting text data containing time information, carrying out context modeling on the time information, and carrying out hybrid time analysis and time expression standardization processing. According to the method, the time information in the text is identified and analyzed more accurately by combining rule matching with the deep learning model, and the accuracy of time information extraction and the adaptability to texts in different fields are improved; through context modeling, the relationship between time expression and context is fully extracted by using an attention mechanism, so that the accuracy of time information analysis is greatly improved; and time information in different forms is unified into a standard format, so that the universality and operability of data are improved.

The invention discloses a test code repairing method which is applied to a code development platform and comprises the steps that a test code corresponding to a source code is obtained, then the test code is executed, execution information of the test code is obtained, and the execution information comprises abnormal stack tracking information output when the test code is executed; then error positioning is conducted on the test code according to the execution information, position information of an error code is obtained, context information is extracted according to the position information of the error code, and the context information comprises at least one of an error test case, error description or error content; and inputting a prompt constructed based on the context information into a language model for reasoning to obtain a first repair code. According to the method, error positioning is carried out by combining execution information, context information is extracted according to position information of error codes, and based on the context information, more accurate and complex repair codes are generated through an advanced language model so as to adapt to various complex error scenes, and the repair efficiency and accuracy are improved.

The invention provides a dialogue method and system based on context awareness, electronic equipment and a storage medium, and the method comprises the steps: carrying out the vectorization of a current dialogue instruction, obtaining a current dialogue instruction vector, calculating the correlation between the current dialogue instruction and N historical dialogue instructions, and obtaining a plurality of first correlation; determining whether to call the context based on the plurality of first correlations; calculating the correlation between the current dialogue instruction and the N historical dialogue instructions to obtain a multi-dimensional correlation score vector, and determining a plurality of second correlations based on the multi-dimensional correlation score vector; for each second correlation, performing multi-dimensional feature extraction on the historical dialogue instruction based on the second correlation to obtain a multi-dimensional feature vector corresponding to the historical dialogue instruction; obtaining a corresponding target input vector based on the target input sub-vector corresponding to each dimension; and determining a target answer based on the target input vector and the current dialogue instruction vector. The accuracy of reply in the dialogue process can be improved.

A method, computer system, and computer program product are provided for generating transcriptions of multimedia data using a context-based machine learning model. Multimedia data including video data and audio data associated with the video data is analyzed to identify one or more features in the video data. One or more candidate words are obtained based on the one or more features identified in the video data. A particular candidate word of the one or more candidate words is determined to match a particular utterance in the audio data. The particular candidate word is selected for the particular utterance based on the audio data.

The application discloses a large language model prompt word injection attack detection method and device based on context learning, and belongs to the technical field of artificial intelligence, and comprises the following steps: based on a Bert pre-training model, inherent features and dependency relationships between different levels and different labels are learned by introducing a label-based attention module, a multi-level, multi-label and fine-grained classification model for prompt word injection attack is designed, and accurate identification of the prompt word injection attack is realized.Meanwhile, according to the context learning, the prediction ability of the classification model is combined with the ability of the large language model, and the defense ability of the large language model to the prompt word injection attack is improved.The application can automatically detect the prompt word injection attack, improve the effectiveness and comprehensiveness of detection, and can be effectively applied to the field of large model security detection.

A training method of a conversation model according to various example embodiments of the present disclosure may include identifying a first context, identifying a first response set corresponding to the first context based on a first model, identifying a response subset selected from the first response set based on a gold response corresponding to the first context and training a second model based on the first context information and the response subset.