1. A system for determining psychological status of a user, comprising: a) an input module for receiving data related to the user's mood swing status, behavior, and physiological state; b) an artificial intelligence module configured to analyze the received data and determine the presence of psychological status; and c) an output module for presenting the determined psychological status of the user.

2. The system of claim 1, wherein the input module comprises one or more sensors for detecting the user's physiological parameters, including heart rate, skin conductance, body temperature, and/or facial expressions.

3. The system of claim 1, wherein the artificial intelligence module comprises a machine learning algorithm trained on a dataset of user mood data.

4. The system of claim 1, wherein the output module presents the determined mood swings in real-time to the user via a mobile device, smartwatch, or other computing device.

5. A method for determining psychological status of a user, comprising: a) receiving data related to the user's mood, behavior, and physiological state; b) analyzing the received data using an artificial intelligence algorithm to determine the presence of mood swings; and c) presenting the determined psychological status of the user.

6. The method of claim 5, wherein the received data includes information about the user's social interactions, sleep patterns, exercise routine, and/or dietary habits.

7. The method of claim 5, wherein the artificial intelligence algorithm is a deep learning algorithm trained on a dataset of user mood data.

8. The method of claim 5, wherein the determined mood swings are presented to the user in the form of a personalized report or visual representation on a computing device. ARTIFICIAL INTELLIGENCE BASED EMOTIONAL STATE/MOOD SWING DETERMINATION Abstract The present disclosure provides system and method to determine an emotion state of a person. The method may comprise step of receiving data related to the user's mood, behaviour and physiological state, and analysing the received data using an artificial intelligence algorithm to determine the presence of mood swings. Further method may include step of presenting the determined psychological status of the user. , Claims:Claims :

1. A system for determining psychological status of a user, comprising: a) an input module for receiving data related to the user's mood swing status, behavior, and physiological state; b) an artificial intelligence module configured to analyze the received data and determine the presence of psychological status; and c) an output module for presenting the determined psychological status of the user.

2. The system of claim 1, wherein the input module comprises one or more sensors for detecting the user's physiological parameters, including heart rate, skin conductance, body temperature, and/or facial expressions.

3. The system of claim 1, wherein the artificial intelligence module comprises a machine learning algorithm trained on a dataset of user mood data.

4. The system of claim 1, wherein the output module presents the determined mood swings in real-time to the user via a mobile device, smartwatch, or other computing device.

5. A method for determining psychological status of a user, comprising: a) receiving data related to the user's mood, behavior, and physiological state; b) analyzing the received data using an artificial intelligence algorithm to determine the presence of mood swings; and c) presenting the determined psychological status of the user.

6. The method of claim 5, wherein the received data includes information about the user's social interactions, sleep patterns, exercise routine, and/or dietary habits.

7. The method of claim 5, wherein the artificial intelligence algorithm is a deep learning algorithm trained on a dataset of user mood data.

8. The method of claim 5, wherein the determined mood swings are presented to the user in the form of a personalized report or visual representation on a computing device.

Description:ARTIFICIAL INTELLIGENCE BASED EMOTIONAL STATE/MOOD SWING DETERMINATION
Field of the Invention
[0001] The present invention relates generally to automated emotional recognition, more particularly to a system and method to determine an emotional state of a person.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] In most cases, a person's or a user's emotions are expressed through their facial expressions. The user's facial emotions are produced by the contraction of facial muscles, which temporally deforms face characteristics such the skin texture, lips, nose, eye brows, eye lids, and eye lashes. This is frequently noticeable by wrinkles and bulges. The majority of users only experience transient changes in their face characteristics, rarely lasting longer than a few seconds or less than 250 milliseconds (ms). The amount of time that the changes in facial characteristics last is a very personal parameter that varies greatly from person to person. This makes interpreting the user's emotions from their facial features difficult. Often it is very much required to determine actual sentiments or mood swings of a person. This becomes more challenging when person pretends differently than actual mood at any instance. Accurate determination of mood swings is tough but very much essential in various applications such as investigation of crime.
[0004] Various technological solutions are disclosed in patent literature to study mood of user. Few of the exemplary documents are discussed below.
[0005] JP4590555B2 (By: Nagaoka University of Technology) provides an emotional state determination method capable of quantitatively and accurately measuring an emotional state of a human being without requiring a special measuring environment. At least one of oxy-hemoglobin densities and deoxy-hemoglobin densities in blood of a plurality of measurement regions of a human brain cortex of a subject 1 are measured in time series, respectively, using a near-infrared spectroscopy. Then, cross-correlation coefficients of plural sets of time-variable change data are computed for each time (for each sampling period). Each of the plural sets of the time-variable change data comprises two of the time-variable change data which are selected by permutations and combinations from among at least one of the time-variable change data on the measured oxy-hemoglobin densities and the time-variable change data on the measured deoxy-hemoglobin densities. Then, by analyzing time-variable change patterns of the computed cross-correlation coefficients of the plural sets of the time-variable change data using a predetermined determination method, the emotional state of the subject is quantitatively measured.
[0006] US8571646B2 (By: Toffee Inc) discloses teleconferencing system for providing remote assistance comprises a local user apparatus that captures both the field of view of the local user along with physiological state of the local user, anda remote user apparatus to An emotional state determining apparatus capable of determining an emotional state of a subject without reducing determination accuracy even if the number of brain wave signals to be used is reduced. A multifractal dimension computing section 3 computes multifractal dimensions based on brain wave signals or brain wave difference signals. An emotional state determining section 4 receives input data on the multifractal dimensions, and determines an emotional state of the subject based on determination criteria which are determined in advance by using as reference data the brain wave signals obtained from a reference person. Generalized latent dimensions (vector) respectively obtained by substituting a plurality of different values determined in advance for a Hurst exponent characteristic q in a generalized latent dimension Dq=1/Hq, which is a reciprocal number of a generalized Hurst exponent Hq obtained from the brain wave signals or the brain wave difference signals, are used as the multifractal dimensions.
[0007] US20200233220A1 (By: APPLE) A head-mounted display includes a display unit and a facial interface. The display unit displays graphical content to the user. The facial interface is removably coupleable to the display unit and engages a facial
[0008] However, the technological solutions for mood swing detection suffers from various limitations such as, complexity, etc. Therefore, more advancement in this field of technology is required. More specifically, to a system and method to determine an emotion state of a person.
[0009] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.

Summary
[00010] The present invention relates generally to automated emotional recognition, more particularly to a system and method to determine an emotion state of a person.
[00011] Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
[00012] The following paragraphs provide additional support for the claims of the subject application.
[00013] Embodiments of the present disclosure may include conductance, body temperature, and/or facial expressions.
[00014] Embodiments of the present disclosure may also include a machine learning algorithm trained on a dataset of user mood data.
[00015] Embodiments of the present disclosure may also include the output module that presents determined mood swings in real-time to the user via a mobile device, smartwatch, or other computing device.
[00016] Embodiments of the present disclosure may also include a method for determining psychological status of a user, including receiving data related to the user's mood, behavior, and physiological state. Embodiments may also include analyzing the received data using an artificial intelligence algorithm to determine the presence of mood swings. Embodiments may also include presenting the determined psychological status of the user.
[00017] In some embodiments, the received data includes information about the user's social interactions, sleep patterns, exercise routine, and/or dietary habits. In some embodiments, the artificial intelligence algorithm may be a deep learning algorithm trained on a dataset of user mood data. In some embodiments, the determined mood swings may be presented to the user in the form of a personalized report or visual representation on a computing device.
Brief Description of the Drawings
[00018] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00019] FIG. 1 is a block diagram illustrating artificial intelligence based emotional state/mood swing determination, according to some embodiments of the present disclosure.
[00020] FIG. 2 is a block diagram illustrating the system for artificial intelligence based emotional state/mood swing determination, according to some embodiments of the present disclosure.
[00021] FIG. 3 is a block diagram illustrating the detailed system, according to some embodiments of the present disclosure.
[00022] FIG. 4 is a flowchart illustrating a method, according to some embodiments of the present disclosure.
Detailed Description
[00023] The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
[00024] In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
[00025] Following are more detailed descriptions of various concepts related to, and implementations of, methods, apparatuses, and systems of present disclosure. The various concepts introduced above and discussed in greater detail below may be implemented in any of numerous ways.
[00026] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items.
[00027] The present invention relates generally to automated emotional recognition, more particularly to a system and method to determine an emotion state of a person.
[00028] FIG. 1 is a block diagram illustrating artificial intelligence based emotional state/mood swing determination, according to some embodiments of the present disclosure. The method 100 may include body temperature 110 and facial expressions 120.
[00029] FIG. 2 is a block diagram illustrating the system 210 for artificial intelligence based emotional state/mood swing determination, according to some embodiments of the present disclosure. In some embodiments, the artificial intelligence module 220 may include a machine learning algorithm 222 trained on a dataset of user mood data.
[00030] FIG. 3 is a block diagram illustrating the detailed system, according to some embodiments of the present disclosure. In some embodiments, the output module may present the determined mood swings in real-time to the user via a mobile device, smartwatch, or other computing device.
[00031] FIG. 4 is a flowchart illustrating a method, according to some embodiments of the present disclosure. In some embodiments, at 410, the method may include receiving data related to the user's mood, behavior, and physiological state. At 420, the method may include analyzing the received data using an artificial intelligence algorithm to determine the presence of mood swings. At 430, the method may include presenting the determined psychological status of the user. In some embodiments, the received data may include information about the user's social interactions, sleep patterns, exercise routine, and/or dietary habits. In some embodiments, the artificial intelligence algorithm may be a deep learning algorithm trained on a dataset of user mood data. In some embodiments, the determined mood swings may be presented to the user in the form of a personalized report or visual representation on a computing device.
[00032] In some embodiments, the machine-learning (ML) model may be selected from Supervised Machine Learning Model, Decision Tree, Decision Treed), Neural Networks, Support Vector Machine and combination thereof. In some embodiments, sending a new text communication message to the sensor. The new text communication message may include a request for contextual data or emotional support information. In some embodiments, at least one of the calculating and the determining may be carried out based on the facial eye features, facial jaw features, facial acoustic facial features and combinations thereof. In some embodiments, the neural network engine may be configured to divide a pre-stored data into a reference training data set and test data set.
[00033] One possible implementation of the present disclosure is an adaptive sampling technique for identifying a user's emotional state, which involves receiving a feed from the user through one or more sensors at a default sample frequency. Other embodiments may additionally include setting up the data processing unit to employ a neural network engine to educate an initial ML model to extract user face characteristics.
[00034] Depending on the embodiment, one might additionally generate a second ML model to determine the user's emotional quotient from their face characteristics. In certain implementations, the emotional quotient is used as part of the input for a trigger to recalculate the feed's sampling frequency. In certain implementations, the trigger is used to determine a new sample frequency, and data is collected at that new sampling frequency. In certain implementations, the user's facial characteristics may be diagnosed by writing instructions for the wireless transmitting device to gather the feed at the increased sample frequency.
[00035] At least one of the calculating or deciding may be performed in accordance with the facial eye features, face jaw features, facial acoustic facial characteristics, and combinations thereof in accordance with certain embodiments. The neural network engine may be set up to separate a mass of stored information into a reference training data set and a test data set in certain implementations.
[00036] The current disclosure may also be implemented as an adaptive sampling system for identifying a user's emotional state, which uses an input method to receive a feed from a user with one or more sensors at a default sample frequency. A data processing unit (DPU) may also be included in certain embodiments to implement a neural network engine for training a first machine learning (ML) model to extract user face characteristics.
[00037] In certain implementations, the user's face characteristics will be used to train a second machine learning model that will determine the user's emotional quotient. One possible implementation is to use the EQ as a basis for determining when to recalculate the feed's sampling frequency. In certain implementations, the trigger is used to determine a new sample frequency, and the feed is collected using this new sampling frequency. In certain implementations, the user's facial characteristics may be diagnosed by writing instructions for the wireless transmitting device to gather the feed at the increased sample frequency. Conductance, body temperature, and/or facial expressions may all be included in various examples of embodiments of the current disclosure.
[00038] The system described may also be included in certain embodiments of the present disclosure. The artificial intelligence module may, in some implementations, include a machine learning algorithm that has been trained on a dataset consisting of user sentiment data.
[00039] The system described may also be included in certain embodiments of the present disclosure. The output module may, in certain implementations, communicate the user's calculated mood swings to them in real time via a mobile device, a wristwatch, or another kind of computer device.
[00040] A method for assessing the psychological condition of a user may also be included in embodiments of the current disclosure. This method may include obtaining data pertaining to the user's mood, behaviour, and physiological state. In certain embodiments, the determination of whether or not mood swings are present involves doing an analysis of the data received using an artificial intelligence system. In other embodiments, it may additionally be possible to display the user's psychological state as it has been determined.
[00041] The data that is received may, in certain implementations, contain information about the user's social interactions, sleep patterns, exercise routine, and/or dietary habits. The artificial intelligence algorithm may, in certain implementations, be a deep learning algorithm that has been trained on a dataset consisting of user sentiment data. The determined mood swings may be delivered to the user in certain implementations in the form of a tailored report or a visual representation on a computer device.
[00042] In certain implementations, the ML model may be chosen from the group consisting of Supervised Machine Learning Model, Decision Tree, Decision Treed), Neural Networks, Support Vector Machine, and combinations thereof. A new text communication message may be sent to the sensor in certain implementations, with the message asking for contextual data or emotional support information.
[00043] At least one of the calculating or deciding may be performed in accordance with the facial eye features, face jaw features, facial acoustic facial characteristics, and combinations thereof in accordance with certain embodiments. The recorded information may be split into a training set and a test set, as the neural network engine is programmed to do in certain implementations.
[00044] Variations of those preferred embodiments may become apparent to those of ordinary skill in the art upon reading the foregoing description. The inventors expect skilled artisans to employ such variations as appropriate, and the inventors intend for the invention to be practiced otherwise than as specifically described herein. Accordingly, this invention includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Moreover, any combination of the above-described elements in all possible variations thereof is encompassed by the invention unless otherwise indicated herein or otherwise clearly contradicted by context.
[00045] In addition, the present disclosure may also provide a memory containing the computer program as mentioned above, which includes machine-readable media and machine-readable transmission media. The machine-readable media may also be called computer-readable media, and may include machine-readable storage media, for example, magnetic disks, magnetic tape, optical disks, phase change memory, or an electronic memory terminal device like a random access memory (RAM), read only memory (ROM), flash memory devices, CD-ROM, DVD, Blue-ray disc and the like. The machine-readable transmission media may also be called a carrier, and may include, for example, electrical, optical, radio, acoustical or other form of propagated signals—such as carrier waves, infrared signals, and the like.
[00046] Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the subject matter described herein, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
[00047] All references to a/an/the element, apparatus, component, means, step, etc. are to be interpreted as referring to at least one instance of the element, apparatus, component, means, step, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated. The discussion above and below in respect of any of the aspects of the present disclosure is also in applicable parts relevant to any other aspect of the present disclosure.
[00048] The wordings such as include, including, comprise and comprising do not exclude elements or steps which are present but not listed in the description and the claims.
[00049] It also shall be noted that as used herein and in the appended claims, the singular forms a, an, and the include plural referents unless the context clearly dictates otherwise. This invention can be achieved by means of hardware including several different elements or by means of a suitably programmed computer. In the unit claims that list several means, several ones among these means can be specifically embodied in the same hardware item. The use of such words as first, second, third does not represent any order, which can be simply explained as names.
[00050] Various techniques may be described herein in the general context of software, hardware elements, or program modules. Generally, such modules include routines, programs, objects, elements, components, data structures, and so forth that perform particular tasks or implement particular abstract data types. The terms module, functionality, and component as used herein generally represent software, firmware, hardware, or a combination thereof. The features of the techniques described herein are platform-independent, meaning that the techniques may be implemented on a variety of commercial computing platforms having a variety of processors.

Claims
I/We Claim:
1. A system for determining psychological status of a user, comprising:
a) an input module for receiving data related to the user's mood swing status, behavior, and physiological state;
b) an artificial intelligence module configured to analyze the received data and determine the presence of psychological status; and
c) an output module for presenting the determined psychological status of the user.
2. The system of claim 1, wherein the input module comprises one or more sensors for detecting the user's physiological parameters, including heart rate, skin conductance, body temperature, and/or facial expressions.
3. The system of claim 1, wherein the artificial intelligence module comprises a machine learning algorithm trained on a dataset of user mood data.
4. The system of claim 1, wherein the output module presents the determined mood swings in real-time to the user via a mobile device, smartwatch, or other computing device.
5. A method for determining psychological status of a user, comprising:
a) receiving data related to the user's mood, behavior, and physiological state;
b) analyzing the received data using an artificial intelligence algorithm to determine the presence of mood swings; and
c) presenting the determined psychological status of the user.
6. The method of claim 5, wherein the received data includes information about the user's social interactions, sleep patterns, exercise routine, and/or dietary habits.
7. The method of claim 5, wherein the artificial intelligence algorithm is a deep learning algorithm trained on a dataset of user mood data.
8. The method of claim 5, wherein the determined mood swings are presented to the user in the form of a personalized report or visual representation on a computing device.

ARTIFICIAL INTELLIGENCE BASED EMOTIONAL STATE/MOOD SWING DETERMINATION
Abstract
The present disclosure provides system and method to determine an emotion state of a person. The method may comprise step of receiving data related to the user's mood, behaviour and physiological state, and analysing the received data using an artificial intelligence algorithm to determine the presence of mood swings. Further method may include step of presenting the determined psychological status of the user.
, Claims:Claims
I/We Claim:
1. A system for determining psychological status of a user, comprising:
a) an input module for receiving data related to the user's mood swing status, behavior, and physiological state;
b) an artificial intelligence module configured to analyze the received data and determine the presence of psychological status; and
c) an output module for presenting the determined psychological status of the user.
2. The system of claim 1, wherein the input module comprises one or more sensors for detecting the user's physiological parameters, including heart rate, skin conductance, body temperature, and/or facial expressions.
3. The system of claim 1, wherein the artificial intelligence module comprises a machine learning algorithm trained on a dataset of user mood data.
4. The system of claim 1, wherein the output module presents the determined mood swings in real-time to the user via a mobile device, smartwatch, or other computing device.
5. A method for determining psychological status of a user, comprising:
a) receiving data related to the user's mood, behavior, and physiological state;
b) analyzing the received data using an artificial intelligence algorithm to determine the presence of mood swings; and
c) presenting the determined psychological status of the user.
6. The method of claim 5, wherein the received data includes information about the user's social interactions, sleep patterns, exercise routine, and/or dietary habits.
7. The method of claim 5, wherein the artificial intelligence algorithm is a deep learning algorithm trained on a dataset of user mood data.
8. The method of claim 5, wherein the determined mood swings are presented to the user in the form of a personalized report or visual representation on a computing device.