212 results about "Mood" patented technology

The system and method establish a communication with a first party. A first emotion is detected during a first step during the communication. Steps in the communication can include: establishing a call, waiting in a queue, talking with an agent, communicating with an IVR system, and the like. A second emotion is detected at a different step in the communication. Alternatively, the first and second emotions are detected in two different but related communications. The detection of the first and/or second emotion determines how to process the communication. Examples of processing a communication can include routing the communication differently, selecting a different script for an agent, selecting a different agent, and the like. In addition, the communication can also be processed differently based on other additional parameters.

The disclosed technology enables, among other things, the identification of persons and the characterization of mental perceptions (e.g., pain, fatigue, mood) and/or intent (e.g., to perform an action) for medical, safety, home care, and other purposes. Of significance are applications that require long-term patient monitoring, such as tracking disease progression (e.g., multiple sclerosis), or monitoring treatment or rehabilitation efficacy. Therefore, longitudinal data must be acquired over time for the person's identity and other characteristics (e.g., pain level, usage of a cane). However, conventional methods of person identification (e.g., photography) acquire unnecessary personal information, resulting in privacy concerns. The disclosed technology allows measurements to be performed while protecting privacy and functions with partial or incomplete measurements, making it robust to real-world (noisy, uncontrolled) settings, such as in a person's home (whether living alone or with others).

A system provides a user an interactive graphic software interface to navigate to an image representative of any particular emotion from within a matrix containing representations of a continuous range of emotional states. The user can then send the selected image to one or more other users or to aggregator servers in order to communicate emotions or to tag or rate various things with emotional significance, so that quantitative statistics on emotional significance can be computed for those things. The system can also be used to track emotional state through a range of time periods or geospatial locations.

The invention belongs to the technical field of information, and particularly discloses a method and system for calculating emotion spreading of the voice social contact media. The method includes the four steps of social contact media range voice preprocessing, voice characteristic parameter extracting, emotion recognition calculating and emotion spreading process quantizing. The system comprises four modules which have the functions of the four steps. According to the method and system, the emotion spreading of a social contact media range is calculated from the voice emotion perspective, and the emotion spreading process in the social contact media range is quantized and identified through a numerical value by means of PAD three-dimensional emotion parameter marking and BP neural network modeling. The method and system have the advantages that the characteristic that phrase type conversations are many in the voice social contact media is sufficiently considered, the voice characteristics are extracted at certain time intervals, the emotion recognition accuracy is easily improved, and emotions in the social contact media range can be dynamically reflected; an emotion spreading process quantizing graph is provided, and changes in the emotions in the social contact range can be conveniently observed visually.

The application discloses a speech emotion recognition method comprising the following steps: carrying out intelligent conversation with a call request end according to a received call request to obtain incoming call speech information; carrying out text conversion on the incoming call speech information to obtain text information, and carrying out emotion analysis on the text information to obtain emotion features; carrying out feature extraction on the incoming call speech information to obtain mood features; integrating the emotion features and the mood features to obtain combined feature words; matching in a preset emotion information base to obtain emotion categories corresponding to the combined feature words; and outputting the emotion categories. According to the speech emotion recognition method, more effective data information can be obtained according to the incoming call information, so that a user can process the incoming call according to feedback information more conveniently, the intelligent demand of the user for a telephone communication service is met, and the user experience is further improved. The application further discloses a speech emotion recognition device, electronic equipment and a computer readable storage medium, all of which have the above beneficial effects.

The invention discloses an intelligent dialogue generation method and a device, and a computer storage medium, relates to the technical field of artificial intelligence, and can comprehensively consider factors of dialogue contents and emotional emotions, so that answers of intelligent dialogues more accord with user expectations, and the interactivity of the intelligent dialogues is improved. Themethod comprises the steps of obtaining a question and answer corpus in an intelligent dialogue; based on a pre-trained emotion classifier, performing emotion marking on the question and answer corpus in the intelligent dialogue; inputting the question and answer corpus carrying the emotion label into a network model for training to obtain a dialogue control model; when a dialogue generation request is received, inputting a question statement requesting dialogue generation into a coding part of the dialogue control model to obtain a context semantic vector; and jointly inputting the emotion classification label requesting to generate the dialogue and the context semantic vector into a decoding part of a dialogue control model to obtain word vector representation of an answer statement matched with the emotion classification label, and outputting the corresponding answer statement after the word vector representation is formed.

The invention discloses an emotion cognition method based on electroencephalogram signal feature analysis. The method comprises the following steps of: S1, obtaining a corresponding type of subject, and collecting electroencephalogram signals which are induced by the subject under different emotions and are used for reference and analysis; S2, performing denoising and separating processing on the electroencephalogram signals, and performing feature extraction and analysis based on a method of combining Hilbert transform and information entropy; and S3, calculating the Hilbert spectral entropy of the electroencephalogram signals in different emotional states, and performing statistical analysis. According to the method, the electroencephalogram signals of the subject under different emotions are obtained, then Hilbert transformation and information entropy are combined, the Hilbert spectral entropy of electroencephalogram rhythms of different brain regions and different genders under different emotional states is analyzed, better statistical performance is achieved, changes of time-frequency domain complexity of the electroencephalogram signals are represented, the change rule of the amplitude of the signals along with time and frequency in the whole frequency band is accurately described, the signal analysis efficiency is improved, the Hilbert spectral entropy is more reliable than approximate entropy, and the method is more comprehensive than single time domain analysis and frequency domain analysis.

A method of music therapy informed processes provides an effective online process to help a user regulate their mood through appropriate music selection choices. The method offers mood and music assessments to evaluate the user's mood, music preferences, goals, and understanding of the correlation between various moods and music. A mood sequence formula (MSF) is subsequently created to generate stylized and personalized music medicine pill (MMP) playlists. With additional recommendations of music exercise routine/regimen (MER) to the user, the method delivers therapeutic MMP playlists of the music therapy informed process with personalized music to manage physiological and behavioral responses, emotional regulation, performance and emotional intelligence. Using daily tracking and specific measurement data of biometrical and physiological devices, the user can track the progress and thus enable the method to fine tune the MSF and MMP playlists for the best effectiveness of the music therapy informed method.

A method for judging the emotion of a user according to browsing history and expression recognition comprises the following steps: recording different indexes F of the emotion for a single page, wherein F can comprise n emotion states; obtaining and calculating an emotion set FP obtained by user browsing page residence time; obtaining the emotion value set FC of the number of times of repeatedly checking of browsing the page within the set time; obtaining an emotion value FE corresponding to the facial expression browsed in each single page (including repeatedly browsed); obtaining a result FWof the weather emotion state value in a specified range; obtaining results FT of the holiday emotion state values within a predetermined time range; and screening the statistical results of the n emotion states under various factors by using a decision tree method, and performing calculating to obtain the final probability distribution of the current emotion category of the user. The whole flow calculation is simple and clear and can be completed without the aid of special hardware equipment, the prediction effect is good, the requirement for data sources is simpler, and extremely high flexibility is achieved.

The invention relates to a multi-modal emotion interaction method, intelligent equipment, a system, electronic equipment and a medium. The multi-modal emotion interaction method comprises the following steps: determining a multi-modal input parameter set for representing the emotion of a user, determining the current emotion state of the user according to the multi-modal input parameter set, and determining behavior performance fed back to the user according to the current emotion state of the user and a preset emotion response library; and feeding back the determined behavior representation to the user by the hardware device or the virtual agent. According to the multi-modal emotion interaction method, multiple modal parameters representing the emotion of the user are collected through multiple channels, and then the current facial emotion of the user is comprehensively and accurately sensed through fusion processing of the collected modal parameters; and the defects of misjudgment and the like when the traditional emotion interaction method only identifies the emotion of the user based on single channels such as vision are avoided.

The present invention relates generally to the fields of neuroscience, proteomics and mood disorders. More particularly, the present invention relates to the identification of a group of proteins modulated in subjects with a mood disorder; methods for detecting or screening mRNA encoding these proteins and methods for diagnosing mood disorders.

The embodiment of the invention provides a determination method of emotion processing tendency and a related product. The method comprises the steps that visual stimulation is presented, wherein the visual stimulation comprises positive emotion stimulation and negative emotion stimulation at the same time; a fixation point of eyes of a test target within a first time threshold value after the testtarget receives the visual stimulation and fixation duration of the fixation point are acquired; and the emotion processing tendency of the test target is determined according to the fixation point and the fixation duration. According to the method and the product, a stimulation paradigm presented by the positive emotion stimulation and the negative emotion stimulation at the same time is combined with an eye movement tracking technology, therefore, the emotion processing tendency of an individual can be objectively and accurately determined, and the efficiency of determining the emotion processing tendency of the individual is improved.

The invention discloses a method for analyzing emotion anxiety, depression and tension based on AR artificial intelligence, and the method comprises the following steps: temporary data collection, sleep data collection, sound emotion collection, sound data processing, resting test collection, deep breath data collection, motion test, and data import processing. Wherein the temporary data acquisition comprises the following steps: firstly, carrying out a five-minute resting test, recording electrocardiogram pulse wave data of a testee in a resting state by a data acquisition platform, normally breathing for 30 seconds, and then carrying out a one-minute deep breathing test. According to the method for analyzing emotion anxiety, depression and tension based on AR artificial intelligence, switching collection can be accurately conducted on static and moving physiological data, the collection accuracy is improved, emotion anxiety, depression and tension analysis is more accurately conducted, sleep data and sound emotion data of a patient can be collected, the types of analysis are increased, and the analysis of emotion anxiety, depression and tension can be more reliably carried out.

The present invention extends to methods, systems, and computer program products for forecasting mood changes from digital biomarkers and more generally for forecasting changes in a neuropsychological clinical assessment. User interaction data indicative of a user's interaction with a mobile device is passively captured over a period of time. A function mapping (modeling a neuropsychological test for a brain health metric) is executed to compute a digital biomarker for a brain health metric from the captured user interaction data. A prior digital biomarker for the brain health metric (computed from previously captured user interaction data by executing the function mapping) is accessed. A difference is detected between the digital biomarker and the prior digital biomarker. A change in a score of the neuropsychological test score is forecast to occur within a specified time range in the future based on the detected differences. The forecast change is output.

The invention relates to a non-contact target emotion recognition method and system. The method comprises the steps: obtaining a video of a tested person in a quasi-static state, and enabling the video to at least comprise a human face, a head, a neck and shoulders; extracting face emotion features in the video to obtain an emotion feature information sequence; analyzing the emotion feature information sequence to obtain an average value and a variation value of each emotion; performing comprehensive calculation according to the average value and the variation value of each emotion to determine the physical and psychological health degree of the tested person; and determining the historical trend of the physical and psychological health degree according to the historical data, and predicting the future emotion by combining the average value and the variation value of the physical and psychological health degree of the tested person. According to the method, high-frequency detection can be performed on the tested person, possible emotion problems of the tested person can be found in time according to a prediction result, long-term and continuous detection and tracking can be performed on fixed crowds, early warning is performed according to the emotion development trend of the tested person, and therefore high credibility and effectiveness are achieved.