The present invention relates to a stimulus information compiling method for tests, characterized in that, the stimulus information compiling method comprises the following steps: selecting and using stimulus information which follows a statistics-normal distribution and is analyzed and measured by validity to form a stimulus information database (Q); reasonably setting at least one stimulus information to be a test including at least one test sequence, for a subject to make a selection according to his or her personal interests; comparing a test score of the subject with that of a valid sample person in a normal distribution model, thereby confirming the distribution of the subject in each cognitive dimension. The present invention compares a test score of the subject with that of a valid sample person in a normal distribution model, thereby confirming the test result of the subject.

The present invention provides a method of categorising words and/or text wherein the following steps are performed: a) compiling a catalogue of selected words of a language which are identified and selected from at least one dictionary and which are descriptive of intrapersonal behaviours and/or interpersonal interactions, and the selected words being of one of, or combinations of two or more of, or all of, the following types: verbs, adjectives, nouns and idioms (nouns may be descriptors of behaviour, personality or emotion); b) identifying synonyms for each one of the selected words from at least one thesaurus; c) identifying archetypal words from the respective groups of one selected word and its respective synonyms; d) rating the archetypal words with scores relating to affiliation and dominance thereby producing a matrix; e) applying ratings to all of the selected words and the synonyms.

A mental health assessment system comprises a mobile device comprising a plurality of sensors for receiving respective inputs over time; a feature monitoring module configured to interpret the sensor inputs as representing behaviours of a user of the mobile device; an analysis module for analysing the represented behaviours relative to external data to determine a risk measure; and a response module for responding to the behaviours according to the risk measure.

Provided is a method for predicting a PTSD diagnosis in a patient comprising receiving audio input data from a patient; determining one or more audio input indicators based on the audio input data, wherein each audio input indicator of the one or more audio input indicators represents a likelihood of a positive PTSD diagnosis based on the audio input data; receiving clinical assessment data from the patient; determining one or more clinical assessment indicators based on the clinical assessment data, wherein each clinical assessment indicator of the one or more clinical assessment indicators represents a likelihood of a positive PTSD diagnosis based on the clinical assessment data; combining the one or more audio input indicators and the one or more clinical assessment indicators using a prediction model chosen by a clinician; and determining a PTSD diagnosis in the patient based on the audio input data and the clinical assessment data.

A matching device comprises an answer acquisition unit, a biometric information acquisition unit, an assessment unit, a classification unit, and a matching unit. The answer acquisition unit acquires an answer from a subject person to a question that is addressed to the subject person for matching the subject person with other persons. The biometric information acquisition unit acquires the biometric information of the subject person. The assessment unit assesses the state of the subject person on the basis of the biometric information acquired by the biometric information acquisition unit. The classification unit performs classification on the basis of the answer acquired by the answer acquisition unit and the state of the subject person assessed by the assessment unit in order to achieve matching of the subject person. The matching unit matches the subject person with other persons on the basis of the classification result of the classification unit.

Systems, methods, and computer software are disclosed for determining group dynamics. This can include receiving input data related to a particular group and determining, by a machine learning algorithm, a quantified group dynamic for the particular group. The machine learning algorithm can be trained with at least group information, user information, sensor data, and subjective evaluation data. A client device can generate an electronic indication of the quantified group dynamic.

Methods, apparatus, and system for an artificial intelligence intention and behavior feedback module to receive and organize intentions, goals, tasks, behaviors, and personality types for a large group of individuals and to provide feedback to an individual regarding the tasks, behaviors, and personality type of the individual, to predict behaviors, to predict outcomes of predicted behaviors, to suggest alternative behaviors, to suggest alternative tasks, and to suggest mentorship relationships among the large group of individuals.

A method and system for creating virtual personality renderings generated from a personality profile exclusively associated with an individual, the rendering depicted in the form of a geometric object in virtual 3D space representing personality traits of the individual. The rendering may comprise a virtual shape having a surface area divided into multiple regions, with each region representative of a personality trait. Personality datapoints may be introduced into the base personality model to generate a unique rendering which is unique for the individual's personality. As personality datapoints are added to personality profile, multiple vectors may project from the base model, causing the base personality model to reconfigure into a non-uniform shape, which is representative of the individual's unique personality. The vectors may represent the magnitude of personality traits.

The present invention is directed to a method for differentiating between ASD phenotype 1, phenotype 2 and other ASD patients, wherein the method comprises: administering an Nrf2-activator to a subject, and identifying the subject as an ASD phenotype 1 patient if the subject shows a negative response, as a phenotype 2 patient if he shows a positive response and as another ASD patient if he does not show a positive nor a negative response, wherein the subject is a patient previously diagnosed with idiopathic ASD or a subject displaying clinical signs of ASD. Likewise, the present invention is directed to an Nrf2-activator for use in differentiating between autism spectrum disorder (ASD) phenotype 1 patients, phenotype 2 patients and other ASD patients, wherein the Nrf2-activator is administered to a subject, wherein a phenotype 1 patient is identified by a negative response, a phenotype 2 patient is identified by a positive response and other ASD patients are identified by the absence of a positive and a negative wherein the subject is a patient previously diagnosed with idiopathic ASD or a subject displaying clinical signs of ASD.

At least one characteristic of a face of the person to be improved is inputted. Artificial intelligence is used to analyse a visual of the person's face and generate data sets of modifications to improve the visual appearance of the person in different ways towards a selected characteristic. The visual of the face of the person is modified based on the data sets of modifications and computer-modified visuals of the face of the person are generated and displayed. One of the computer-modified visuals is selected as the desired face of the person. A video is provided that shows a computer-modified visual of the desired face of the person.