A processing unit includes a contribution-degree calculation unit configured to calculate contribution degrees that indicate respective degrees by which a plurality of attribute items included as predetermined attributes of one target contribute to a predicted intervention effect, the calculating being performed on a basis of an estimation model for estimating the predicted intervention effect from the predetermined attributes of the one target. The predicted intervention effect on the one target is a numerical value corresponding to an increase in gain to a beneficiary, the gain being expected to be larger in a case where an intervention is implemented to the one target than in a case where the intervention is unimplemented to the one target.

A processing unit includes a contribution-degree calculation unit configured to calculate contribution degrees that indicate respective degrees by which a plurality of attribute items included as predetermined attributes of one target contribute to a predicted intervention effect, the calculating being performed on a basis of an estimation model for estimating the predicted intervention effect from the predetermined attributes of the one target. The predicted intervention effect on the one target is a numerical value corresponding to an increase in gain to a beneficiary, the gain being expected to be larger in a case where an intervention is implemented to the one target than in a case where the intervention is unimplemented to the one target.

This application relates to a method of constructing a training program for improving symptoms of a mild cognitive impairment patient. In one aspect, the method includes arranging, on designated days, direct training algorithms regarding at least one of visualization, fusion, or semantic word fluency directly related to a cognitive function area and controlling the arranged direct training algorithms to be output to a user terminal according to the designated days, and receiving a result value from the user terminal. The method may also include calculating an achievement level for each direct training algorithm, based on the result value, and determining one of the direct training algorithms based on the calculated achievement level. The method may further include rearranging the direct training algorithms, together with the determined direct training algorithm based on both the calculated achievement level and a training algorithm matched to the determined direct training algorithm.

This application relates to a method of constructing a training program for improving symptoms of a mild cognitive impairment patient. In one aspect, the method includes arranging, on designated days, direct training algorithms regarding at least one of visualization, fusion, or semantic word fluency directly related to a cognitive function area and controlling the arranged direct training algorithms to be output to a user terminal according to the designated days, and receiving a result value from the user terminal. The method may also include calculating an achievement level for each direct training algorithm, based on the result value, and determining one of the direct training algorithms based on the calculated achievement level. The method may further include rearranging the direct training algorithms, together with the determined direct training algorithm based on both the calculated achievement level and a training algorithm matched to the determined direct training algorithm.

A platform providing methods and systems for prevention and/or treatment of a health condition, where a method can include: simultaneously reducing severity symptoms of the health condition and comorbid conditions upon: receiving a set of samples from one or more subjects; receiving a biometric dataset from one or more subjects; receiving a lifestyle dataset from one or more subjects; returning a genomic single nucleotide polymorphism (SNP) profile and a baseline microbiome state upon processing the set of samples, the biometric dataset, and the lifestyle dataset with a set of transformation operations; generating personalized intervention plans for the one or more subjects upon processing the genomic SNP profile and the baseline microbiome state with a multi-omic model; and executing the personalized intervention plans for the one or more subjects.

A system and method for providing sensory relief from distractibility, inattention, anxiety, fatigue, and/or sensory issues to a user in need. The user can be autistic/neurodiverse, or neurotypical. The system can be configured to connect to a datastore storing one or more sensory thresholds specific to a user of a wearable device of the system, the sensory thresholds selected from auditory, visual or physiological sensory thresholds; record, using one or more sensors of the wearable device, a sensory input stimulus to the user; compare the sensory input stimulus with the sensory thresholds to determine an intervention to be provided to the user, the intervention configured to provide the user relief from distractibility, inattention, anxiety, fatigue, or sensory issues; and provide the intervention to the user, the intervention comprising filtering, in real-time, an audio signal presented to the user or an optical signal presented to the user.

The subject matter described herein includes methods, systems, and computer readable media for automated attention assessment. According to one method, a method for automated attention assessment includes obtaining head and iris positions of a user using a camera while the user watches a display screen displaying a video containing dynamic region-based stimuli designed for identifying a neurodevelopmental and/or psychiatric (neurodevelopmental/psychiatric) disorder; analyzing the head and iris positions of the user to detect attention assessment information associated with the user, wherein the attention assessment information indicates how often and/or how long the user attended to one or more regions of the display screen while watching the video; determining that the attention assessment information is indicative of the neurodevelopmental/psychiatric disorder; and providing, via a communications interface, the attention assessment information, a diagnosis, or related data.

A method for an augmented reality goal assistant is described. The method includes detecting an object associated with a behavioral goal of a user. The method also includes altering an appearance of the object based on the behavioral goal of the user. The method further includes displaying the altered appearance of a detected object on an augmented reality headset, such that the altered appearance of the detected object is modified based on the behavioral goal of the user.

Provided are a brain stimulation system, method, apparatus and storage medium based on artificial intelligence. The system includes: a plurality of brain stimulation terminals and a cloud platform. The cloud platform is configured to, with artificial intelligence algorithm especially machine learning and deep learning, generate multi-dimensional psychological big data using physiological data and psychological state evaluation parameters gotten from the plurality of brain stimulation terminals and established models of algorithm for disease diagnosis. The brain stimulation terminal is configured to analyze the physiological data and psychological state evaluation parameters of a target subject, measure a mental state of the target subject, obtain brain stimulation parameters required for the target subject according to the mental state, and generate corresponding non-invasive brain stimulation for the target subject according to the brain stimulation parameters based on the multi-dimensional big data through the artificial intelligence algorithm.

An example of a system for programming neurostimulation according to a stimulation configuration may include stimulation configuration circuitry, volume definition circuitry, stimulation effect circuitry, and recording circuitry. The stimulation configuration circuitry may be configured to determine the stimulation configuration. The volume definition circuitry may be configured to determine stimulation field model(s) (SFM(s)) each representing a volume of tissue activated by the neurostimulation. The stimulation effect circuitry may be configured to determine a stimulation effect type for each tagging point specified for the SFM(s) and to tag the SFM(s) at each tagging point with the stimulation effect type determined for that tagging point. The stimulation effect type for each tagging point is a type of stimulation resulting from the neurostimulation as measured at that tagging point. The recording circuitry may be configured to generate SFM data representing the determined SFM(s) with the stimulation effect type tagged at each tagging point.