12530 results about "Neuroscience" patented technology

Various implementations disclosed herein include devices, systems, and methods that interpret user activity as user interactions with user interface (UI) elements positioned within a three-dimensional (3D) space such as an extended reality (XR) environment. Some implementations enable user interactions with virtual elements displayed in 3D environments that utilize alternative input modalities, e.g., XR environments that interpret user activity as either direct interactions or indirect interactions with virtual elements.

In order to solve the problem that the bottleneck exists when image-text features are fused in a traditional anti-modal recognition model and deep-level interaction information between modals cannot be fully mined in an existing method, the invention provides a multi-modal anti-modal recognition model (MSCAG) fusing two-way cross attention and a gating mechanism, and a technical scheme is provided for solving the problem that the traditional anti-modal recognition model has the bottleneck when the image-text features are fused in the traditional anti-modal recognition model. Related attention features on a text layer and an image layer are respectively refined through a bidirectional cross attention mechanism, the text attention features, the images and basic features of the texts are integrated through residual connection, and the image attention features, the texts and the basic features of the images are integrated through residual connection; a gating mechanism is applied to enhance information interaction and key area focusing between the two modes. And the context features and the local features are combined to realize more reliable chiffon recognition. The invention provides an innovative method for multi-mode siphonage recognition, has important practical application value, and can be applied to siphonage recognition of netizen comments in social media.

A mechanical end effector for a humanoid robot includes a plurality of identical finger assemblies. Each of the finger assemblies is removably connected to a frame. Each of the finger assemblies is fully self-contained and operable independently of every other one of the finger assemblies and independently of every other component connected to the frame. Each of the finger assemblies includes a single electric motor and is configured to be fully operable using only the single electric motor.

Devices and methods for the non-invasive stimulation of nerves, such as the vagus nerve, include a housing and an electrode coupled to the housing. The electrode is configured to be positioned adjacent to, or in contact with, an outer skin surface of a patient. A source of energy is coupled to the housing and operably coupled to the electrode. The source of energy emits an electrical impulse to the electrode such that the electrical impulse passes through the outer skin surface of the patient to a nerve at a target region in the patient sufficient to modulate the nerve. The electrical impulse comprises burst periods and constant periods, wherein each burst period includes 2 to 20 pulses and has a duration of less than 20,000 microseconds.

The invention relates to a multi-modal intelligent robot system and an interaction method, and belongs to the technical field of robot digital data processing, and the method comprises the following steps: S1, multi-modal input collection; s2, carrying out multi-modal input preprocessing; s3, performing multi-modal information fusion, inputting the information into a pre-trained multi-modal feature fusion model, and performing feature fusion on each modal feature through a quantitative feature data fusion mechanism to obtain a user interaction intention vector and a user emotional state vector; s4, interaction response generation: matching a preset interaction response strategy library based on the user interaction intention vector to obtain basic response content, and performing emotion adaptation adjustment on the basic response content in combination with the user emotion state vector to generate multi-modal response content including text information, voice information and action information; s5, interactive feedback is executed; the method has the beneficial effects that the robot is fused with multi-modal information through the basic response content and the user emotional state vector, and interaction is dynamically optimized.

Systems, devices and methods are provided for stimulating one or more nerves within a user. A system comprises stimulator having an electrode configured for contacting the outer skin surface at a target location and an energy source coupled to the stimulator, The energy source is configured to generate at least one electrical impulse and to transmit the electrical impulse transcutaneously from the electrode through the outer skin surface of the user to a selected nerve in the user. In certain aspects, the systems and methods are particularly useful for non-medical purposes, such as reducing stress and anxiety, enhancing relaxation, improving sleep, energy, concentration and mood, increasing mental or physical performance, promoting mental health, recovery and wellness, generally improving the health and wellbeing of a user and / or for temporarily or permanently improving intelligence, learning capacity, memory retention, recall, mood, alertness and / or sleep efficiency in human beings.

An in vitro training method for training genioglossus muscle strength includes adhering an electrode patch of an in vitro training device to a bottom of a chin of a user during a non-sleep period. The electrode patch receives an electrical stimulation signal from an electrical stimulation module of the in vitro training device to stimulate the genioglossus muscle of the user through transdermal electrical stimulation. The electrode patch includes a body surface adhering face and an assembling face opposite to the body surface adhering face. The body surface adhering face is adhered to the bottom of the chin of the user to align with the genioglossus muscle. The electrical stimulation module is disposed on the assembling face and in electrical connection with the electrode patch. The electrical stimulation module sends an electrical stimulation signal to stimulate the genioglossus muscle through transdermal electrical stimulation.

The invention discloses a multi-modal cognitive impairment evaluation system based on a multi-dimensional cognitive function hypergraph. The multi-modal cognitive impairment evaluation system comprises a multi-modal signal acquisition system, a cognitive function evaluation normal form design, a universal feature extraction framework of each modal and online hypergraph learning based on a multi-dimensional cognitive function. The system constructs a dimensionality evaluation normal form containing the cognitive function based on multi-modal physiological and behavior signals, so that comprehensive evaluation of the cognitive function of the elderly is realized. Firstly, physiological and behavior responses of a user under different tasks and situations are recorded and analyzed in real time by fusing multi-mode signals such as electroencephalogram, electrocardio, voice and facial expressions. And then, mining fusion representation among the multi-modal signals, generating unified feature representation, introducing an online hypergraph of clinical knowledge, capturing a complex multi-modal data relationship, and providing an analysis result containing cognitive function evaluation. Finally, in combination with the technical means, the system can realize early detection of cognitive impairment.

Catheter apparatuses, systems, and methods for achieving renal neuromodulation by intravascular access are disclosed herein. One aspect of the present technology, for example, is directed to a treatment device having a multi-electrode array configured to be delivered to a renal blood vessel. The array is selectively transformable between a delivery or low-profile state (e.g., a generally straight shape) and a deployed state (e.g., a radially expanded, generally helical shape). The multi-electrode array is sized and shaped so that the electrodes or energy delivery elements contact an interior wall of the renal blood vessel when the array is in the deployed (e.g., helical) state. The electrodes or energy delivery elements are configured for direct and / or indirect application of thermal and / or electrical energy to heat or otherwise electrically modulate neural fibers that contribute to renal function or of vascular structures that feed or perfuse the neural fibers.

The present disclosure relates to systems and methods for enhancing the interaction between users and automated agents, such as digital assistants, by employing Large Language Models (LLMs) to infer the intent of spoken language. The invention involves continuously monitoring ambient audio, converting speech to text, and utilizing LLMs to determine whether spoken language is intended for the automated agent. A structured prompt, including the converted text and specific instructions, is sent to the LLM, which is fine-tuned to process domain-specific prompts. The LLM provides a structured output in a standardized format, indicating the user's intent. The system may involve multiple prompts to perform separate tasks, such as identifying intent and generating additional context-specific data. This approach facilitates a more natural and intuitive user experience by eliminating the need for wake words and allowing seamless conversational interaction with virtual assistants across various platforms and devices.

Systems, devices, and methods for neurostimulation using a combination of implantable and external devices to treat pain are disclosed.

The invention provides a wheeled humanoid robot which comprises a wheeled chassis walking mechanism, a robot trunk mechanism is arranged above the wheeled chassis walking mechanism, a robot head mechanism is mounted on the robot trunk mechanism, and robot arm mechanisms are mounted on the two sides of the robot trunk mechanism. The end, away from the robot trunk mechanism, of each robot arm mechanism is provided with a clamping jaw. A robot lower limb waist mechanism is arranged between the robot trunk mechanism and the wheel type chassis walking mechanism, and the robot trunk mechanism and the wheel type chassis walking mechanism are connected through the robot lower limb waist mechanism. Through cooperative arrangement of all the structures and arrangement of the wheeled humanoid robot, the problem that in the prior art, a foot type humanoid robot is low in moving speed under the flat ground environment is effectively solved, the degree of freedom of the whole robot is high, the robot can adapt to various working environments, and popularization is facilitated.

The present invention relates to the field of voice interaction analysis technology, and in particular to a voice interaction analysis method and system for an intelligent voice robot. The method comprises the following steps: improving the clarity of user interaction voice through multi-scale noise decomposition and filtering enhancement, performing granular segmentation and long-distance semantic analysis on clear voice, generating a semantic structure, performing multi-level semantic recognition based on the structure, extracting specific meanings and making preliminary responses, generating personalized semantic graphs through independent graph structure mapping, predicting user interaction dialogues, building an advance response engine based on dialogue templates, preparing to respond to user needs, embedding the engine into an intelligent voice robot, and realizing efficient voice interaction analysis. The present invention realizes a more accurate and efficient voice interaction analysis method for an intelligent voice robot.

To provide methods for treating a subject having or at risk of developing a neurocognitive disorder, such as frontotemporal lobar degeneration or neuronal ceroid lipofuscinosis.SOLUTION: Provided is a method comprising administering, to a subject diagnosed as having a neurocognitive disorder (NCD), a composition comprising a population of cells comprising a transgene encoding a progranulin (PGRN) or a granulin (GRN).SELECTED DRAWING: Figure 1A

The invention provides an intelligent evaluation method for cognitive function detection, and relates to the technical field of cognitive function detection. Comprising the following steps: constructing a virtual reality situation with a plurality of virtual reality tasks, carrying out customized setting on the virtual reality situation, collecting physiological data and behavior data of a testee in real time, recording performance indexes of the testee, collecting subjective feedback of the testee in the virtual reality tasks, constructing an evaluation model based on a support vector machine, and evaluating the virtual reality tasks according to the evaluation model. And dynamically adjusting the task difficulty according to an evaluation result, constructing a multi-dimensional cognitive file of the testee, and constructing a cognitive function evaluation model based on a long short-term memory network to evaluate the cognitive function of the testee. According to the invention, by setting different types of cognitive tasks, the cognitive function condition of the testee can be evaluated more comprehensively; by performing fusion analysis on the physiological signals and the behavior data, multiple dimensions of the cognitive function can be better captured, and comprehensiveness and accuracy of cognitive function detection are achieved.

In one implementation, a device obtains a prompt from a source for input to a language model. The device performs a search for similar prompts to that of the prompt in a prompt database. The device determines, based on results of the search, whether the prompt represents a potential attack. The device prevents a response to the prompt from being returned from the language model to the source, when the device determines that the prompt represents a potential attack.

Systems, apparatuses, methods, and computer program products are disclosed for predicting an emotion in real-time based on facial gestures and limb gestures derived from captured images. An example method includes receiving a series of images captured in real-time. The example method further includes causing generation of a face segmentation and one or more limb segmentations using the series of images. The example method further extracting one or more face segmentations vectors and one or more limb segmentation vectors. The example method further includes causing generation of weighted vectors using the one or more face segmentation vectors and the one or more limb segmentation vectors for each of the one or more the limb segmentations. The example method includes calculating a probability distribution based on the one or more probabilities corresponding to one or more emotions. The example method finally includes determining a predicted emotion based on the probability distribution.

The invention relates to a multi-mode intention recognition method and system, and the method comprises the steps: carrying out the time domain and frequency domain enhancement of the features of text, video and audio modes, carrying out the splicing to obtain non-language mode fusion features, combining the features of an original text, modeling the time synchronization relation of audio-text and video-text, and carrying out the recognition of a multi-mode intention. Standardized audio features, video features and text features are obtained through context alignment processing; fusing the standardized features of the three modalities to obtain a fused feature vector, and mapping the fused feature vector back to the text modal space to be connected with the weighted residual error of the original text feature to obtain a fused semantic vector; extracting global semantic anchor points and mask positions from the fused semantic vector, and splicing the global semantic anchor points and the mask positions with the original text features and the fused semantic vector to obtain input features; and obtaining probability distribution of multiple intention categories by using the input features. Three types of heterogeneous modal input can be supported, and the accuracy and robustness of intention recognition are improved through fine-grained semantic supervision and enhancement strategies.

The invention discloses a brain image analysis method and system based on multi-modal fusion, and relates to the technical field of brain image processing. A brain image analysis system based on multi-modal fusion comprises a data acquisition module, a data preprocessing module, a feature extraction and fusion module, a brain network analysis module, a comprehensive analysis module and a focus detection module. The comprehensive analysis model adopts a double-branch structure, deep processing is performed on multi-modal features and brain network features, and interactive fusion of the two types of features is realized through a cross-modal attention mechanism; the model is further combined with a classification branch and a regression branch to cooperatively complete brain disease classification and focus quantitative analysis, and the adaptive capacity and diagnosis performance of an existing model in a complex task scene are improved.

Techniques for detecting attributes and / or gestures associated with pedestrians in an environment are described herein. The techniques may include receiving sensor data associated with a pedestrian in an environment of a vehicle and inputting the sensor data into a machine-learned model that is configured to determine a gesture and / or an attribute of the pedestrian. Based on the input data, an output may be received from the machine-learned model that indicates the gesture and / or the attribute of the pedestrian and the vehicle may be controlled based at least in part on the gesture and / or the attribute of the pedestrian. The techniques may also include training the machine-learned model to detect the attribute and / or the gesture of the pedestrian.

Method and systems are described for identifying electrodes for stimulation of a patient using a stimulation system. The stimulation system includes at least one stimulation lead implanted in a patient and having electrodes disposed thereon. The method includes obtaining a plurality of bioelectrical signals, wherein each of the bioelectrical signals is obtained using a different one, or a different combination, of the electrodes; analyzing a dataset including the bioelectrical signals to identify at least one fundamental component of the dataset, each of the at least one fundamental component identifying a contribution of one or more of the electrodes to the fundamental component; and using at least one of the at least one fundamental component to identify one or more of the electrodes for stimulation according to the contribution of each of the one or more of the electrodes to the at least one of the at least one fundamental component.

The invention relates to the technical field of brain-computer interfaces, and discloses a self-adaptive dynamic feedback brain-computer interface training method and system based on virtual reality. The training method comprises the steps that a user performs motor imagery according to a training scene presented by the VR display device; the electroencephalogram acquisition equipment acquires electroencephalogram signals; configuring a data processing module, and operating a motor imagery continuous control model to calculate characteristic parameters by taking the electroencephalogram signals as input; under the condition that the task exists, the characteristic parameters are used as input, and a dynamic self-adaptive characteristic-feedback mapping model is operated to calculate control parameters; sending the control parameters to a functional electrical stimulation device to load electrical stimulation of corresponding intensity to the muscle to obtain tactile feedback; meanwhile, the dynamic adaptive feature-feedback mapping model is dynamically updated according to latest input feature parameters; the control parameters are sent to the VR display device to present visual feedback, and the user adjusts the motor imagery based on the visual feedback and the haptic feedback. The participation sense of the user and the training efficiency can be improved.

The invention discloses an alimentary canal pathological diagnosis visual language large model construction method based on reinforcement learning and application thereof, and belongs to the technical field of medical image processing. The method comprises the following steps: firstly, extracting pathological information through layout analysis and adaptive threshold processing, and recombining the pathological information into a structured data set containing an inference chain; secondly, a visual encoder and a multi-branch classifier are used for extracting features and confidence coefficients, and dynamic structured cue words are generated; and finally, inputting the image and the cue word into a multi-modal large model, carrying out supervised fine-tuning hot start, and carrying out reinforcement learning training by adopting a group relative strategy optimization algorithm in cooperation with a composite reward function containing format, semantics and diagnosis dimensions. The problems that a general model is prone to generating illusion in a pathological scene and lacks reasoning logic are solved, and the accuracy and logicality of pathological report generation are remarkably improved.

A generative mediator engine can perform a requested interaction with a conversational agent of a target entity on behalf of a user. An internal conversational platform can identify intents for the requested interaction. An external artificial intelligence engine can perform intent discovery when an intent is not identified above a confidence threshold. A discovered intent unknown to the generative mediator engine can be received from the external artificial intelligence engine and used, with input requirements determined by the generative mediator for the requested interaction, by a dialog generator to generate a sample dialog for the requested interaction. User feedback can be received after review of action items and expected inputs identified from the sample dialog. The generative mediator engine can perform the requested interaction with the conversational agent on behalf of the user and without receiving user intervention during the requested interaction.

A method for facilitating conversational interaction with users to help the users includes transmitting a conversational interaction interface for conversationally interacting with a user to a user device, receiving a request of the user through the conversational interaction interface from the user device, identifying an information based on the request, generating an input comprising the request and the information for a machine learning model based on the request and the information, processing the input using the machine learning model, generating a response for the request based on the processing of the input, transmitting the response through the conversational interaction interface for conversationally interacting with the user to the user device, and storing the machine learning model, the request, and the response.

The present disclosure describes systems and methods for large-scale conversational deliberation and conversational collective intelligence with selectable levels of synchrony. In some aspects, a collaboration server defines a plurality of sub-groups of networked computing devices (e.g., human participants). Each subgroup may be associated with a conversational surrogate agent configured to observe real-time conversational deliberation among participants of its subgroup, pass insights to and receive insights from other conversational surrogate agents associated with other subgroups, and conversationally express insights received from other subgroups. Additionally, each subgroup may be associated with a conversational contributor agent configured to participate in local conversation of that subgroup by offering answers, insights, opinions, or factual information that is independently AI generated rather than being derived based on conversational content of participants in other subgroups. According to some embodiments, computer mediated collaboration is thus enabled through communication between the conversational surrogate agent and the conversational contributor agent.

Aspects of the disclosure relate to methods of reducing fatigue in a subject having myotonic dystrophy type 1 (DM1). Aspects of the disclosure relate to methods of treating one or more symptoms assessable by the MDHI (e.g., a GI symptom, myotonia, upper extremity function impairment, fatigue, mobility impairment, impairment in the ability to perform activities, pain, vision impairment, communication impairment, sleep impairment, emotional issues, cognitive impairment, social satisfaction impairment, social performance impairment, breathing impairment, swallowing impairment, and / or hearing impairment) in a subject having myotonic dystrophy type 1 (DM1). In some embodiments, the methods comprise administering to the subject a composition comprising complexes (e.g., muscle targeting complexes) comprising an oligonucleotide (e.g., a DMPK- targeting oligonucleotide) covalently linked to an antibody (e.g., anti-TfRl antibody).

The invention discloses a transcranial electrical stimulation intracranial vector electric field detection device and method, and belongs to the transcranial electrical stimulation intracranial electric field detection technology, the device inputs an individual head model in a stimulation system, sets a target spot and calculates transcranial electrical stimulation parameters; the implantation position and angle of the vector detection electrode are planned through the navigation system, electrical stimulation is applied to a brain target area through the stimulation electrode, and intracranial electric signals are collected in real time through the vector detection electrode; performing high-precision amplification on the acquired weak signal, and converting the weak signal into a digital signal; the collected electric field signals are calculated and processed in real time, and electric field distribution generated by transcranial electrical stimulation in the brain is analyzed and visualized; according to the method, time domain and frequency domain analysis is carried out on electric signals before and after stimulation, the influence of stimulation on neural signals in a detection area is evaluated, and high-precision measurement of the intensity and direction of a three-dimensional electric field is achieved through the design of a high-precision and multi-layer vector detection electrode.

In an approach to coordinating a conversational agent with a large language model for conversation repair, one or more computer processors receive a failure indicator from a first conversational agent. One or more computer processors retrieve a descriptive prompt associated with the first conversational agent. One or more computer processors transmit the descriptive prompt to a large language model. One or more computer processors transfer control of the failed conversation from the first conversational agent to the large language model. One or more computer processors determine the intent of the user associated with the failed conversation using the large language model. One or more computer processors determine whether the intent of the user associated with the failed conversation matches a capability of the first conversational agent. One or more computer processors transfer by one or more computer processors, the user back to the first conversational agent.