1718 results about "Emotionality" patented technology

A multi-party conversation analyzer and logger uses a variety of techniques including spectrographic voice analysis, absolute loudness measurements, directional microphones, and telephonic directional separation to determine the number of parties who take part in a conversation, and segment the conversation by speaking party. In one aspect, the invention monitors telephone conversations in real time to detect conditions of interest (for instance, calls to non-allowed parties or calls of a prohibited nature from prison inmates). In another aspect, automated prosody measurement algorithms are used in conjunction with speaker segmentation to extract emotional content of the speech of participants within a particular conversation, and speaker interactions and emotions are displayed in graphical form. A conversation database is generated which contains conversation recordings, and derived data such as transcription text, derived emotions, alert conditions, and correctness probabilities associated with derived data. Investigative tools allow flexible queries of the conversation database.

A system, method and computer-readable medium for practicing a method of emotion detection during a natural language dialog between a human and a computing device are disclosed. The method includes receiving an utterance from a user in a natural language dialog between a human and a computing device, receiving contextual information regarding the natural language dialog which is related to changes of emotion over time in the dialog, and detecting an emotion of the user based on the received contextual information. Examples of contextual information include, for example, differential statistics, joint statistics and distance statistics.

Features, including one or more acoustic features, visual features, linguistic features, and physical features may be extracted from signals obtained by one or more sensors with a processor. The acoustic, visual, linguistic, and physical features may be analyzed with one or more machine learning algorithms and an emotional state of a user may be extracted from analysis of the features. It is emphasized that this abstract is provided to comply with the rules requiring an abstract that will allow a searcher or other reader to quickly ascertain the subject matter of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims.

The present invention is a method and system for measuring human emotional response to visual stimulus, based on the person's facial expressions. Given a detected and tracked human face, it is accurately localized so that the facial features are correctly identified and localized. Face and facial features are localized using the geometrically specialized learning machines. Then the emotion-sensitive features, such as the shapes of the facial features or facial wrinkles, are extracted. The facial muscle actions are estimated using a learning machine trained on the emotion-sensitive features. The instantaneous facial muscle actions are projected to a point in affect space, using the relation between the facial muscle actions and the affective state (arousal, valence, and stance). The series of estimated emotional changes renders a trajectory in affect space, which is further analyzed in relation to the temporal changes in visual stimulus, to determine the response.

The invention discloses an attention CNNs and CCR-based text sentiment analysis method and belongs to the field of natural language processing. The method comprises the following steps of 1, training a semantic word vector and a sentiment word vector by utilizing original text data and performing dictionary word vector establishment by utilizing a collected sentiment dictionary; 2, capturing context semantics of words by utilizing a long-short-term memory (LSTM) network to eliminate ambiguity; 3, extracting local features of a text in combination with convolution kernels with different filtering lengths by utilizing a convolutional neural network; 4, extracting global features by utilizing three different attention mechanisms; 5, performing artificial feature extraction on the original text data; 6, training a multimodal uniform regression target function by utilizing the local features, the global features and artificial features; and 7, performing sentiment polarity prediction by utilizing a multimodal uniform regression prediction method. Compared with a method adopting a single word vector, a method only extracting the local features of the text, or the like, the text sentiment analysis method can further improve the sentiment classification precision.

A video game character or avatar is generated using images of the player so that the on-screen avatar being controlled by the player appears like the player. Answers to a questionnaire or a psychological profile are provided by the player to determine characteristics that the player views as desirable, which are used to generate a final version of the avatar. Iterations of the avatar appearance are generated from the initial appearance to the final, more desirable appearance. Using the avatar, as play of the game proceeds the player's avatar begins as a character appearing like themselves and gradually becomes a character that is more as they would like to appear. The avatar increases the emotional impact of games by providing strong visual and psychological connections with the player. The avatar may instead begin from an initial character that does not appear similar to the player. The iterations may make the avatar gradually less appealing or with other changes in appearance, in some embodiments.

Various embodiments of the present invention create a novel system for rating an event in a media based on the strength of the emotions viewers feel towards the event. The viewer's responses to the media can be measured and calculated via physiological sensors. The metric for rating the strength of the media is created based on the mathematical coherence of change (up or down) of all pertinent physiological responses across multiple viewers. Such rating offers an objective ability to compare the strengths of events of the media, as there is a strong correlation between high coherence of physiological responses (all viewers feel the same thing at the same time) and strong ratings of emotionality, engagement, likeability, success in the marketplace/on screen.

A semantically organized domain space is created from a training corpus. Affective data are mapped onto the domain space to generate affective anchors for the domain space. A sentiment associated with an input text is determined based the affective anchors. A speech output may be generated from the input text based on the determined sentiment.

A communication robot control system displays a selection input screen for supporting input of actions of a communication robot. The selection input screen displays in a user-selectable manner a list of a plurality of behaviors including not only spontaneous actions but also reactive motions (reflex behaviors) in response to behavior of a person as a communication partner, and a list of emotional expressions to be added to the behaviors. According to a user's operation, the behavior and the emotional expression to be performed by the communication robot are selected and decided. Then, reproductive motion information for interactive actions including reactive motions and emotional interactive actions, is generated based on input history of the behavior and the emotional expression.

A system determines neuro-response stimulus and stimulus attribute resonance. Stimulus material and stimulus material attributes such as communication, concept, experience, message, images, audio, pricing, and packaging are evaluated using neuro-response data collected with mechanisms such as Event Related Potential (ERP), Electroencephalography (EEG), Galvanic Skin Response (GSR), Electrocardiograms (EKG), Electrooculography (EOG), eye tracking, and facial emotion encoding. Neuro-response data is analyzed to determine stimulus and stimulus attribute resonance.

A system performs stimulus targeting using neuro-physiological and neuro-behavioral data. Subjects are exposed to stimulus material such as marketing and entertainment materials and data is collected using mechanisms such as Electroencephalography (EEG), Galvanic Skin Response (GSR), Electrocardiograms (EKG), Electrooculography (EOG), eye tracking, and facial emotion encoding. Neuro-physiological and neuro-behavioral data collected is analyzed to select targeted stimulus materials. The targeted stimulus materials are provided to particular subjects for a variety of purposes.

Performing detailed sentiment analysis includes generating a first sentiment score for a first entity based on a content source. The first sentiment score is generated with respect to a first dimension. A second sentiment score for the first entity is generated based on the content source. The second sentiment score is generated with respect to a second dimension.

A system analyzes neuro-response measurements from subjects exposed to video games to identify neurologically salient locations for inclusion of stimulus material and personalized stimulus material such as video streams, advertisements, messages, product offers, purchase offers, etc. Examples of neuro-response measurements include Electroencephalography (EEG), optical imaging, and functional Magnetic Resonance Imaging (fMRI), eye tracking, and facial emotion encoding measurements.

The invention relates to a robot man-machine interaction method based on user mood and intension recognition. User mood recognition is carried out by combining human body biological information such as breath, the heart rate and the dermal electricity with facial expression recognition. The user intension is recognized through different kinds of physical sensor information such as pressure, photoelectricity and temperature. Intelligent control and decision making are carried out according to the user mood and intention recognition results. A corresponding execution mechanism of a robot is controlled to complete limb movements and voice interaction. By means of the robot man-machine interaction method based on user mood and intension recognition, the robot can understand the user intension more thoroughly, know the metal changes of a user, meet the functional requirement for emotion care for the user, and better participate in the life of users such as the old people and children.

The invention discloses a comment text emotion classification model training and emotion classification method and device and equipment and belongs to the field of text emotion classification in natural language processing. Model training comprises the steps that a comment text and associated subject and object information are acquired; a comment subject and object attention mechanism is fused based on a first-layer Bi-LSTM network to extract sentence-level feature representation; the comment subject and object attention mechanism is fused based on a second-layer Bi-LSTM network to extract document-level feature representation; and a hyperbolic tangent non-linear mapping function is adopted to map document-level features to an emotion category space, softmax classification is adopted to train parameters in a model, and an optimal text emotion classification model is obtained. According to the method, the hierarchical bidirectional Bi-LSTM network model and the attention mechanism are adopted, context semantic robust perception and semantic expression of the text can be realized, the robustness of text emotion classification can be remarkably improved, and the correct rate of classification is increased.

A mobile device user interface method activates a camera module to support a video chat function and acquires an image of a target object using the camera module. In response to detecting a face in the captured image, the facial image data is analyzed to identify an emotional characteristic of the face by identifying a facial feature and comparing the identified feature with a predetermined feature associated with an emotion. The identified emotional characteristic is compared with a corresponding emotional characteristic of previously acquired facial image data of the target object. In response to the comparison, an emotion indicative image is generated and the generated emotion indicative image is transmitted to a destination terminal used in the video chat.

An Intelligent Tutoring System (ITS) system is provided that is able to identify and respond adaptively to the learner's or student's affective states (i.e., emotional states such as confusion. frustration, boredom, and flow/engagement) during a typical learning experience, in addition to adapting to the learner's cognitive states. The system comprises a new signal processing model and algorithm, as well as several non-intrusive sensing devices, and identifies and assesses affective states through dialog assessment techniques, video capture and analysis of the student's face, determination of the body posture of the student, pressure on a pressure sensitive mouse, and pressure on a pressure sensitive keyboard. By synthesizing the output from these measures, the system responds with appropriate conversational and pedagogical dialog that helps the learner regulate negative emotions in order to promote learning and engagement.

Environmental characteristics of habitable environments (e.g., hotel or motel rooms, spas, resorts, cruise boat cabins, offices, hospitals and/or homes, apartments or residences) are controlled to eliminate, reduce or ameliorate adverse or harmful aspects and introduce, increase or enhance beneficial aspects in order to improve a “wellness” or sense of “wellbeing” provided via the environments. Control of intensity and wavelength distribution of passive and active Illumination addresses various issues, symptoms or syndromes, for instance to maintain a circadian rhythm or cycle, adjust for “jet lag” or season affective disorder, etc. Air quality and attributes are controlled. Scent(s) may be dispersed. Noise is reduced and sounds (e.g., masking, music, natural) may be provided. Environmental and biometric feedback is provided. Experimentation and machine learning are used to improve health outcomes and wellness standards.

The invention relates to speech emotion recognition equipment and a speech emotion recognition method. The speech emotion recognition equipment comprises an analysis device, a probability calculating device and an emotion judging device, wherein the analysis device matches emotional characteristics of input speech with a plurality of emotion models so as to ensure a plurality of possible emotional states; the probability calculating device calculates the final probability of the possible emotional states in the previous emotional states of a speaker according to the knowledge of conditional probability converted among the emotional states in the speaking process of the speaker; and the emotion judging device selects a possible emotional state with the maximum final probability from the possible emotional states as the emotional state of the input speech.

A video game and game system incorporating a game character's morality level that is affected by game occurrences such as moral, amoral, or immoral choices in an epic story's deeper context. The character's morality level affects the game's environment. Such a feedback system based on moral premises provides an efficient means to enhance and deepen game play, as a sensible, realistic, meaningful, profound, and epic story naturally emerges. The measurement of moral choices will allow a player's soul to be rendered upon the screen in cinematic action paralleling internal dramatic action, thus providing the dramatic elements of classic literature and film. The presentation of moral choices in the game, based upon moral premises, will allow plot points that result in character arcs, romantic relationships, exalted game play, and epic story. Moral choices will lead to overall success, while immoral or amoral choices will lead to overall failure.