13 results about "Attention focus" patented technology

The invention provides an intelligent structured medical record generation method and system based on multi-modal doctor-patient interaction, and the method comprises the steps: collecting dialogue voice in real time, transcribing the dialogue voice into a text sequence, and recognizing a visual attention entity through monitoring the operation of a mouse in an electronic medical record system; a logic demonstration track is constructed based on a historical visual attention entity and a text sequence, and an implicit reward function is reversely derived from the logic demonstration track by using a reverse reinforcement learning algorithm. The function is used for calculating the action return value of each combination of the visual attention entity and the dialogue text, and the highest value combination is selected as an optimal alignment strategy to determine the time sequence causal relationship. And mapping the entity and the text to a medical knowledge graph, extracting a shortest semantic path as an implicit clinical reasoning chain, and generating a structured electronic medical record. According to the method, diagnosis and treatment decision logic is deduced from multi-modal behaviors of doctors through reverse reinforcement learning, and the technical problem that internal causal association between visual attention focuses of doctors and oral contents cannot be established in a traditional method is solved.

The application discloses a subject-driven personalized generation method based on decoupled mask prompt attention fine-tuning, and belongs to the field of deep learning, computer vision and artificial intelligence generated content. A mask prompt decoupling module is designed to decompose the unified text prompt into a text prompt containing only the subject and a text prompt containing only the context. A subject attention focusing module and a context attention adjusting module are introduced, and independent subject feature extraction paths and context semantic adaptation paths are established, respectively. The subject identity learning and the context scene modeling are explicitly separated, and the double constraint loss guided by the mask is used for joint optimization to further prevent feature coupling. Finally, the algorithm effectively preserves the fine appearance features of the subject, significantly enhances the adaptability of the model to novel context instructions, and effectively improves the quality of personalized generation and the flexibility of context editing.

The application provides a kind of intelligent structured medical record generation method and system based on multimodal doctor-patient interaction, which comprises: real-time acquisition of dialogue voice and transcription into text sequence, while recognizing visual attention entity by listening to mouse operation in electronic medical record system.Based on the history of visual attention entity and text sequence, a logical demonstration track is constructed, and an implicit reward function is derived from it using a reverse reinforcement learning algorithm. Use the function to calculate the action reward value of each combination of visual attention entity and dialogue text, select the highest value combination as the optimal alignment strategy to determine the timing causal relationship. Map the entity and text to the medical knowledge graph, extract the shortest semantic path as the implicit clinical reasoning chain, and generate structured electronic medical record. The application deduces the diagnosis and treatment decision logic from the doctor's multimodal behavior through reverse reinforcement learning, solving the technical problem that traditional methods cannot establish the internal causal relationship between the doctor's visual attention focus and spoken content.

This invention discloses an adaptive adjustment method and system for a virtual environment, relating to the field of virtual reality technology. The method includes: performing background subtraction processing on a target image sequence when a user is experiencing a virtual environment; extracting detailed features from the target image sequence, and identifying key points using target anchor boxes based on the detailed feature information; determining the coordinates of three-dimensional key points based on the target key point information for user action behavior analysis; extracting a user eye image sequence from the target image sequence to determine eyeball image information, and analyzing the gaze direction based on the eyeball image information; analyzing the user's attention focus using image brightness distribution based on the gaze direction information; and adjusting elements of the virtual environment based on the user's action behavior information and user attention focus information. This invention enables more fine-grained virtual environment adjustment, improving the quality and naturalness of the user's immersive experience in the virtual environment.

The application discloses a method, device and electronic equipment for obtaining attention information of an object, the method comprising: displaying a stereoscopic virtual image in a virtual scene, the stereoscopic virtual image being a stereoscopic image corresponding to a virtual object in the virtual scene, the virtual object being a physical object; determining a target object in the stereoscopic virtual image, the target object being present in a display area; and determining an attention focus object in the stereoscopic virtual image based on the target object, the attention focus object belonging to at least part of the stereoscopic virtual image. The application can accurately obtain attention information of a user for a product or other physical object.

The invention discloses a semiconductor valve analysis method and system based on multi-agent cooperation and an attention mechanism, and relates to the technical field of semiconductor equipment test and intelligent analysis, and the method comprises the steps: carrying out the denoising, missing value filling and test condition label matching operation of all-condition test data, and outputting a standardized test data matrix; fusing a multivariate statistical method and a space attention mechanism, and focusing a test data dimension which has obvious influence on the performance of the gas path system; performing dynamic weight distribution on historical data and real-time data of the multiple test items; and other intelligent agents are linked according to user operation feedback to dynamically adjust the attention focusing direction. According to the method, the problem of association fuzziness in traditional test data analysis is solved, and the precision of performance evaluation and prediction of the gas path system is improved.

The application provides a learning guidance method and device, electronic equipment and a storage medium, relates to the technical field of computers, and is used for improving the flexibility of learning guidance and thus improving learning efficiency. The method comprises the following steps: acquiring learning data of a user and a task type currently practiced by the user in the case that the electronic equipment is in a language learning mode, wherein the learning data is used for indicating the current learning state of the user; and outputting a voice prompt according to the task type and the learning data, wherein the task type is one of animation watching, follow-up reading or interactive practice, and the voice prompt is related to an attention focus corresponding to the task type and the current learning state of the user.

The application belongs to the technical field of time prediction, and particularly relates to a time prediction method and device based on different channel attention focusing mechanisms. A scaled household electricity time sequence data vector is input into an MLP to obtain a processed output vector, and a stationary wavelet transform is performed on the output vector; based on high-frequency detail components, Granger causality analysis is performed, inverse Granger transformation is performed on the high-frequency detail components, and reconstructed household electricity time sequence data is obtained; based on low-frequency components and medium-frequency components, the correlation between different channel attentions is calculated, and multiple channels with the strongest dependency are screened; self-attention coupling is used for the channels with strong dependency, and geometric self-attention coupling is used for the channels with weak dependency; the outputs of the self-attention and the geometric self-attention are fused to obtain comprehensive features; the reconstructed frequency domain sequence data and the comprehensive features are inversely transformed again, and a final prediction result is obtained through an FFN.

The application relates to the technical field of network marketing, in particular to an online marketing promotion system and method based on the Internet, which comprises the following steps: real-time monitoring of a first interaction behavior sequence of a user on a browsing interface, wherein the first interaction behavior sequence comprises a cursor moving track, a page scrolling speed and a scrolling direction; prediction of a current attention focus area and a potential migration path of the attention focus of the user through a predefined attention model based on the first interaction behavior sequence; selection of adapted target advertisement content from an advertisement library based on the potential migration path, and control of the target advertisement content to appear at a preset position on the potential migration path. Through the current focus area position and the historical scrolling direction, candidate content blocks that the user may pay attention to next are predicted, and a visual path between the two is taken as a core basis for advertisement delivery, so that the advertisement can be arranged on the path in advance in the process of natural migration of the user's attention, and the effective reach rate of the advertisement is improved.

This invention relates to the field of intelligent educational interaction and discloses an adaptive cognitive guidance method for children's microscopes based on multimodal perception. The method includes: real-time acquisition of sample image sequences and user operation time-series data; extraction of semantic features through image understanding, generation of a spatial attention map through behavioral analysis, and fusion of the two to obtain a feature vector; inputting the feature vector of a continuous time window into a cognitive state inference model, outputting cognitive state labels containing attention focus, exploration intention, and confusion level; based on the target category corresponding to the intention and focus, retrieving popular science materials from a knowledge graph, and dynamically assembling them by a multimodal generator according to the guidance state machine logic into guidance content including highlighted annotations, voice narration, and animation, which is then rendered and presented in real time. This invention achieves intelligent understanding and personalized guidance of children's observation intentions, transforming one-way observation into interactive inquiry-based learning, improving cognitive conversion efficiency and the scientific enlightenment experience.

The invention relates to the technical field of human-computer interaction, in particular to a human attention focus area simulation and page churn rate prediction method and system, and the method comprises the steps: obtaining the visual input content and human-computer interaction information of a user; classifying and identifying the visual input content by using machine vision, and distinguishing at least one of identifiable content and unidentifiable content; simulating at least one attention focus area of a person according to the recognizable and unrecognizable contents, and calculating an attention value of the at least one attention focus area; according to the attention value, the classification result and the man-machine interaction information, the staying time and the display sequence of each attention focus area are calculated; and according to the display sequence, the retention time and the human-computer interaction information of each attention focus area, generating and outputting an attention focus area dynamic change process of the viewer, an attention focus area with relatively long accumulated retention time, an unconcerned attention focus area, an attention focus area which is not understood by the viewer, a page churn rate, viewing time and design suggestions.

The invention relates to the crossing technical field of AR and building model display, and discloses an AR interaction method and system for a building sand table, and the method comprises the following steps: S1, collecting the sight, head posture and physical sand table touch data of a user based on AR equipment and a sensor array; s2, fusing sight line and head posture data, and calculating a three-dimensional attention focus; s3, quantifying regional interests according to focus staying and touch history, and inferring user intentions; s4, generating AR information in response to the intention, and optimizing the layout to minimize visual occlusion; and S5, evaluating the cognitive load of interaction feedback, and adaptively adjusting AR information presentation. Through multi-modal behavior perception and dynamic weight fusion, an intention decision tree and a space optimization algorithm, and in combination with physiological feedback adjustment, AR information accurate layout and cognitive load adaptive control are realized, and the building sand table interaction efficiency is improved.

The class-incremental image classification method based on dual attention visual Transformer network is suitable for the field of computer vision. The method transfers the attention information as knowledge and enhances the semantic knowledge of class-incremental learning. The core is the dual attention mechanism, that is, the dual key learning inter-task attention and intra-task attention are used in each Transformer layer. The inter-task attention can implicitly absorb the knowledge in the previous task, effectively alleviating the catastrophic forgetting, while the intra-task attention focuses on the knowledge of the current task, which can improve the plasticity of the model. By fusing the knowledge obtained by the two attentions, a good balance between stability and plasticity can be achieved. In addition, the invention also proposes a neighbor-invariant loss and an adaptive attention solidification loss. The method solves the stability-plasticity dilemma and the model preference problem caused by sample imbalance to improve the accuracy of class-incremental image classification.