DESC:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:
SYSTEM AND METHOD FOR MENTAL HEALTH MONITORING USING BEHAVIORAL ANALYSIS BASED ON SOCIAL MEDIA CONTENT

APPLICANT:
Tata Consultancy Services Limited
A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th floor,
Nariman point, Mumbai 400021,
Maharashtra, India

The following specification describes the invention and the manner in which it is to be performed.

PRIORITY INFORMATION
[001] This patent application takes priority from 2718/MUM/2013.

TECHNICAL FIELD
[002] The present disclosure described herein, in general, relates to health monitoring system, and more particularly to the health monitoring system for detecting a psychological condition of a user.

BACKGROUND
[003] The world health organization highlights that a plurality of psychiatric diseases such as depression, schizophrenia or alike, rank first among all diseases in terms of causing disability of all disabilities. The most common form of the psychiatric disease is depression, which represents mental health disorders, followed by anxiety disorders. The timely detection of such mental health diseases and their proper diagnosis is required for wellness and healthy life of individuals affecting from these disorders.
[004] In the existing scenario, in order to monitor presence of the psychiatric diseases, wellness domain requires modeling human behavior treatment. Further, the presence of the psychiatric disease is monitored through an on-going evaluation by a trained mental health professional. Few laboratory tests are available for the psychiatric diseases which make overall diagnosis, monitoring, and treatment of the psychiatric diseases difficult and time consuming. Further clinicians have to base their evaluations on limited patient contacts, relying on a patient's self-reported experiences, behavior indicated by relatives and/or friends, and various mental health examinations.
[005] Alternatively, physical wellness and personal health management have also been implemented by the use of sensors. Though the use of sensors allows the monitoring of the psychiatric diseases, however, these sensors substantially increase the cost and complexity associated with the entire monitoring and diagnosis process. The psychiatric diseases monitoring and diagnosis therefore have been found extremely challenging due to the complex nature of data being requirement for analysis as-well-as subjectivity involved in diagnostic techniques, and therefore have not been able to address with due care and efficacy. Efforts have been made to directly interact with the individuals in order to detect and diagnosis the psychiatric diseases via online tests employing rating scales. However, these online tests are disease specific and also require an individual to volunteer for a test. Further, such test may not be meaningful for determining the psychiatric diseases as the person may unaware of her psychiatric condition.

SUMMARY
[006] Before the present systems and methods, are described, it is to be understood that this application is not limited to the particular systems, and methodologies described, as there can be multiple possible embodiments which are not expressly illustrated in the present disclosures. It is also to be understood that the terminology used in the description is for the purpose of describing the particular versions or embodiments only, and is not intended to limit the scope of the present application. This summary is provided to introduce concepts related to systems and methods for detecting a psychological condition of a user and the concepts are further described below in the detailed description. This summary is not intended to identify essential features of the disclosure nor is it intended for use in determining or limiting the scope of the disclosure.
[007] In one implementation, a system for detecting a psychological condition of a user is disclosed. In one aspect, the system may comprise a processor and a memory coupled to the processor for executing a plurality of modules present in the memory. The plurality of modules may comprise an information capturing module, an anomaly identification module, a reasoning module, and a rating scale module. The information capturing module may capture textual content from one or more social networking servers. In one aspect, the textual content captured may be associated to the user. The anomaly identification module may derive a textual pattern over a periodic time interval based upon analysis of the textual content. It may be understood that the textual content may be analyzed by using a text analytics technique. The reasoning module may detect a psychological disorder of a plurality of psychological disorders. It may be understood that the plurality of psychological disorders and a plurality of symptoms associated therewith are stored in a rule database. Further, the psychological disorder may be detected by mapping the textual pattern with one or more symptoms corresponding to the psychological disorder. The rating scale module may display contextual information to the user based upon the psychological disorder detected. In one aspect, the contextual information may be stored in a database. The rating scale module may further receive a response associated with the contextual information from the user. In one aspect, the response signifies presence of the psychological disorder in the user. The rating scale module may further assign a score based upon the response. The rating scale module may further validate the psychological disorder detected based upon the score, thereby detecting the psychological condition of the user.
[008] In another implementation, a method for detecting a psychological condition of a user is disclosed. In one aspect, textual content may be captured from one or more social networking servers. In one aspect, the textual content captured may be associated to the user. Upon capturing the textual content, a textual pattern may be derived over a periodic time interval based upon analysis of the textual content. It may be understood that the textual content may be analyzed by using a text analytics technique. Based upon the analysis of the textual content, a psychological disorder of a plurality of psychological disorders may be detected. In one aspect, the plurality of psychological disorders and a plurality of symptoms associated therewith are stored in a rule database. It may be understood that the psychological disorder may be detected by mapping the textual pattern with one or more symptoms corresponding to the psychological disorder. Subsequent to the detection of the psychological disorder, contextual information may be displayed to the user based upon the psychological disorder detected. In one aspect, the contextual information may be stored in a database. Upon displaying the contextual information, a response associated with the contextual information may be received from the user. The response signifies presence of the psychological disorder in the user. After receiving the response, a score based upon the response may be assigned. Subsequent to the assigning of the score, the psychological disorder detected may be validated based upon the score, thereby detecting the psychological condition of the user. In one aspect, the aforementioned method for detecting the psychological condition of the user is performed by a processor using programmed instructions stored in a memory.
[009] In yet another implementation, non-transitory computer readable medium embodying a program executable in a computing device for detecting a psychological condition of a user. The program may comprise a program code for capturing textual content from one or more social networking servers. In one aspect, the textual content captured may be associated to the user. The program may further comprise a program code for deriving a textual pattern over a periodic time interval based upon analysis of the textual content analyzed by using a text analytics technique. The program may further comprise a program code for detecting a psychological disorder of a plurality of psychological disorders. The plurality of psychological disorders and a plurality of symptoms associated therewith are stored in a rule database. In one aspect, the psychological disorder may be detected by mapping the textual pattern with one or more symptoms corresponding to the psychological disorder. The program may further comprise a program code for displaying contextual information, stored in a database, to the user based upon the psychological disorder detected. The program may further comprise a program code for receiving a response associated with the contextual information from the user. The response signifies presence of the psychological disorder in the user. The program may further comprise a program code for assigning a score based upon the response. The program may further comprise a program code for validating the psychological disorder detected based upon the score, thereby detecting the psychological condition of the user.

BRIEF DESCRIPTION OF THE DRAWINGS
[0010] The foregoing detailed description of embodiments is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the disclosure, there is shown in the present document example constructions of the disclosure; however, the disclosure is not limited to the specific methods and apparatus disclosed in the document and the drawings.
[0011] 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 same numbers are used throughout the drawings to refer like features and components.
[0012] Figure 1 illustrates a network implementation of a health monitoring system for detecting a psychological condition of a user is shown, in accordance with an embodiment of the present disclosure.
[0013] Figure 2 illustrates the system, in accordance with an embodiment of the present disclosure.
[0014] Figure 3 illustrates functional representation of the system, in accordance with an embodiment of the present disclosure.
[0015] Figure 4 illustrates a method for detecting the psychological condition of the user, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION
[0016] Some embodiments of this disclosure, illustrating all its features, will now be discussed in detail. The words "comprising," "having," "containing," and "including," and other forms thereof, are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Although any systems and methods similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present disclosure, the exemplary, systems and methods are now described. The disclosed embodiments are merely exemplary of the disclosure, which may be embodied in various forms.
[0017] Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure is not intended to be limited to the embodiments illustrated, but is to be accorded the widest scope consistent with the principles and features described herein.
[0018] Systems and methods for detecting a psychological condition of a user are described. In one aspect, the psychological condition of the user may be detected based on textual content published on social networking applications such as Facebook®, Twitter®, LinkedIn® or the like associated to the user. In one aspect, the textual content published on the social networking applications may comprise one or more words forming a descriptive sentence. In one aspect, the textual content may be published by the user or may be published by any other user associated to the user or may be published on behalf of the user.
[0019] It may be understood that the present disclosure discloses an effective and efficient mechanism for detecting a psychological disorder of a plurality of psychological disorders. Examples of the plurality of psychological disorders may include, but not limited to, schizophrenia, borderline personality disorders, depression, Alzheimer, delusions. The psychological disorder may be detected based on analysis performed on the textual content. In one aspect, the analysis may be performed by using a text analytics technique that may include, but not limited to, a natural language processing (NLP) technique, a grammatical pattern recognition technique, a sentiment analysis technique, and the like. Subsequent to the detection of the psychological disorder, the psychological disorder may be classified into one of the plurality of psychological disorders.
[0020] After the classification of the psychological disorder, contextual information may be displayed to the user based upon the psychological disorder detected. In one aspect, the contextual information may comprise one or more contents displayed in a form, including, but not limited to, an animated image, a game, an audio file, a video file, a set of questionnaire, and the like. It may be understood that the contextual information may prompt the user to interactively view the contextual information and thereby provide a response corresponding to the one or more contents. In one example, the contextual information may involve a set of game mechanics that may improve the interactive engagement of the user in the contextual information. It may be understood that the set of questionnaire may be represented in a manner such that each question may facilitate to validate the psychological disorder detected. In one aspect, the psychological disorder may be validated based on the response to each question received from the user or analyzing navigating activities of the user associated with the set of game mechanics. In one example, the response may comprise a comment, post, notes, remarks or an indicator suggesting whether the contextual information being displayed is liked by the user such as “like” on Facebook®.
[0021] After receiving the response from the user, a score may be assigned to the response. In one embodiment, the assignment of score may be dependent on level of interaction of the user while navigating the set of game mechanics of the contextual information and/or responses corresponding to each question in the set of questionnaire. In one aspect, the score is compared with a pre-defined score indicating validation of the presence of the psychological disorder in the user. In one aspect, the psychological disorder is validated when the score is greater than the pre-defined score. Further, a report depicting the psychological condition of the user may be generated. The report may facilitate to provide a possible consultation with a clinical psychsician to take necessary measures to cure the user suffering from the psychological disorder.
[0022] While aspects of described system and method for detecting a psychological condition of a user may be implemented in any number of different computing systems, environments, and/or configurations, the embodiments are described in the context of the following exemplary system.
[0023] Referring now to Figure 1, a network implementation 100 of a health monitoring system, hereinafter referred to as a system 102, for detecting a psychological condition of a user is illustrated, in accordance with an embodiment of the present disclosure. In one embodiment, the system 102 may capture textual content from one or more social networking servers. In one aspect, the textual content captured may be associated to the user. The system 102 may further derive a textual pattern over a periodic time interval based upon analysis of the textual content. It may be understood that the textual content may be analyzed by using a text analytics technique. The system 102 may further detect a psychological disorder of a plurality of psychological disorders. It may be understood that the plurality of psychological disorders and a plurality of symptoms associated therewith may be stored in a rule database. Further, the psychological disorder may be detected by mapping the textual pattern with one or more symptoms corresponding to the psychological disorder. The system 102 may further display contextual information to the user based upon the psychological disorder detected. In one aspect, the contextual information may be stored in a database. The system 102 may further receive a response associated with the contextual information from the user. In one aspect, the response signifies presence of the psychological disorder in the user. The system 102 may further assign a score based upon the response. The system 102 may further validate the psychological disorder detected based upon the score, thereby detecting the psychological condition of the user
[0024] Although the present disclosure is explained considering that the system 102 is implemented on a server, it may be understood that the system 102 may also be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a server, a network server, a cloud-based computing environment. It will be understood that the system 102 may be accessed by multiple users through one or more user devices 104-1, 104-2…104-N, collectively referred to as user 104 hereinafter, or applications residing on the user devices 104. In one implementation, the system 102 may comprise the cloud-based computing environment in which a user may operate individual computing systems configured to execute remotely located applications. Examples of the user devices 104 may include, but are not limited to, a portable computer, a personal digital assistant, a handheld device, and a workstation. The user devices 104 are communicatively coupled to the system 102 through a network 106.
[0025] In one implementation, the network 106 may be a wireless network, a wired network or a combination thereof. The network 106 can be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, and the like. The network 106 may either be a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like, to communicate with one another. Further the network 106 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.
[0026] Referring now to Figure 2, the system 102 is illustrated in accordance with an embodiment of the present disclosure. In one embodiment, the system 102 may include at least one processor 202, an input/output (I/O) interface 204, and a memory 206. The at least one processor 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the at least one processor 202 is configured to fetch and execute computer-readable instructions stored in the memory 206.
[0027] The I/O interface 204 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface 204 may allow the system 102 to interact with the user directly or through the client devices 104. Further, the I/O interface 204 may enable the system 102 to communicate with other computing devices, such as web servers and external data servers (not shown). The I/O interface 204 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. The I/O interface 204 may include one or more ports for connecting a number of devices to one another or to another server.
[0028] The memory 206 may include any computer-readable medium and computer program product known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. The memory 206 may include modules 208 and data 210.
[0029] The modules 208 include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement particular abstract data types. In one implementation, the modules 208 may include information capturing module 212, an anomaly identification module 214, a reasoning module 216, a rating-scale module 218, a report generation module 220 and other modules 222. The other modules 222 may include programs or coded instructions that supplement applications and functions of the system 102. The modules 208 described herein may be implemented as software modules that may be executed in the cloud-based computing environment of the system 102.
[0030] The data 210, amongst other things, serves as a repository for storing data processed, received, and generated by one or more of the modules 208. The data 210 may also include a database 224, a rule database 226, and other data 228. The other data 228 may include data generated as a result of the execution of one or more modules in the other modules 222.
[0031] In one implementation, at first, a user may use the client devices 104 to access the system 102 via the I/O interface 204. The user may register themselves using the I/O interface 204 in order to use the system 102. In one aspect, the user may accesses the I/O interface 204 of the system 102 for detecting a psychological condition of a user. In order to detecting the psychological condition of the user, the system 102 may employ the plurality of modules i.e. the information capturing module 212, the anomaly identification module 214, the reasoning module 216, the rating scale module 218, and the report generation module 220. The detailed working of the plurality of modules is described below.
[0032] Referring to figure 3, it may be understood that a plurality of users of one or more social networking websites may perform one or more activities including, but not limited to, comment, post, like, recommend, and endorse. Examples of the one or more social networking websites may include, but not limited to, Orkut®, Facebook®, Twitter®, and LinkedIn®. It may be understood that the one or more activities such as comment may include one or more words forming a descriptive sentence indicating emotions, feelings or sentiments corresponding to a user of the plurality of users. Since the one or more activities indicating the emotions, the feelings or the sentiments, each word of the one or more activities may then be analyzed in order to detect probability of an anomaly hereinafter referred to as a psychological disorder of a plurality of psychological disorders associated to the user. Examples of the plurality of psychological disorders may include, but not limited to, schizophrenia, a bipolar disorder, a borderline personality disorders, a depression, an Alzheimer, and a delusions.
[0033] In order to detect the psychological disorder, at first, the information capturing module 212 may capture textual content from one or more social networking servers corresponding to the one or more social networking websites. It may be understood that the textual content being shared on the one or more social networking websites is mostly informal, opinionated, and unstructured. In one aspect, in order to capture and thereby transforming the textual content, a web scraping technique may be utilized. The web scraping technique facilitates to capture the textual content from the one or more social networking websites by using an oAuth (open standard for authorization) technique. The oAuth in combination with the web scraping technique enables to connect to third party web sites i.e. Orkut®, Facebook®, Twitter®, and LinkedIn® and thereafter extracts the textual content stored on the one or more social networking servers. In one aspect, the social networking websites promote the OAuth as the primary authentication method, over the traditional email confirmation type processes. The social networking websites may then be enabled to capture the textual content associated to the user for data mining purposes. After extracting the textual content, the web scraping technique further enables to transform the textual content from the unstructured format to a structured format using at least one of Load, Extract, and Transform function known in the art.
[0034] In order to understand the working of the information capturing module 212, consider an example where a ‘User A’ performs an activity i.e. a comment on the social networking website i.e. Facebook®. The comment include the textual content which is as follows:
[0035] “I am feeling very depressed from the past few days”.
[0036] In order to capture the textual content, the information capturing module 212 captures one or more words of the textual content (i.e. “I am feeling very depressed from the past few days”). Upon capturing, the textual content is stored in a database 224 for further analysis.
[0037] Subsequent to the capturing of the textual content, the anomaly identification module 214 may derive a textual pattern over a periodic time interval. In one aspect, the textual pattern may be derived based upon analysis of the textual content. It may be understood that the textual content may be analyzed by using a text analytics technique. Examples of the text analytics technique may include, but not limited to, a natural language processing (NLP) technique, a grammatical pattern recognition technique, and a sentiment analysis technique. In one embodiment, the anomaly identification module 214 may work as a text analytics engine enabled to analyze the textual content captured from the social networking websites. After deriving the textual pattern, the reasoning module 216 may detect the psychological disorder that may be present in the user. In one aspect, the plurality of psychological disorders and a plurality of symptoms associated therewith are stored in a rule database 226. Examples of the plurality of symptoms may include, but not limited to, depressed mood, sleep disturbance, reduced social engagement, reduced energy and activity, reduced motivation, impaired consentration and memory, anxiety, anhedonia, affective flattening, worthlessness, suicidal ideation, guilt, and psychotic symptoms. In one aspect, the psychological disorder may be detected by mapping the textual pattern with one or more symptoms, of the plurality of symptoms, corresponding to the psychological disorder.
[0038] In order to understand the working of the anomaly identification module 214 and the reasoning module 216, consider an example where a ‘User A’ posted a plurality of comments on Facebook®. The plurality of comments are as follows:
[0039] Comment 1: “I am feeling very depressed from the past few days”.
[0040] Comment 2: “Vain are the beliefs and teachings that make man miserable”.
[0041] Comment 3: "I am worthless, I hate myself... there is no reason to live".
[0042] Comment 4: “Why all smoke to enjoy it. I smoke to die.”
[0043] Comment 5: “I can't eat and I can't sleep. I'm not doing well in terms of being a functional human”.
[0044] In order to detect the presence of the psychological disorder in the ‘User A’, the information capturing module 212 captures the textual content (i.e. the plurality of comments) from Facebook®. Upon capturing, the anomaly identification module 214 derives the textual pattern from the plurality of comments that are posted over a periodic time interval by the ‘User A’. In one embodiment the textual pattern may be derived by analyzing the plurality using the natural language processing (NLP). The NLP may perform a plurality of tasks in order derive the textual pattern. The plurality of tasks may include, but not limited to, automatic summarization, machine translation, morphological segmentation, part-of-speech tagging, parsing, sentiment analysis, and word segmentation. In one example, the sentiment analysis may facilitate to identify symptoms of the bipolar disorder based on positiveness or negativeness of emotions expressed by the ‘User A’ in the social networking website through the plurality of comments over the periodic time interval. The sentiment analysis further searches for a periodicity pattern in the positiveness and negativeness in the emotions expressed through the plurality of comments in order to detect the presence of the psychological disorder. In one aspect, the presence of the psychological disorder may be detected in the ‘User A’ when a cyclic pattern is found in the plurality of comments.
[0045] In another embodiment, the textual pattern may be derived by implementing the grammatical pattern recognition technique on the plurality of comments. After deriving the textual pattern, the reasoning module 216 may detect the psychological disorder associated to the ‘User A’ based on mapping of the textual pattern with the plurality of symptoms stored in the rule database 226. Since the textual content present in the plurality of comments is more inclined towards the bipolar disorder (i.e. the psychological disorder), the reasoning module 216 may detect the presence of the bipolar disorder in the ‘User A’.
[0046] In one aspect, the plurality of symptoms may be stored in the rule database 226 in form of a plurality of rules. In order to detect the psychological disorder, the reasoning module 216 may execute a query, associated to the textual pattern, within the plurality of rules. It may be understood that upon executing the query, the one or more rules, of the plurality of rules, may be mapped with the textual pattern, wherein the one or more rules indicates the presence of the psychological disorder in the user.
[0047] Subsequent to the detection of the psychological disorder, the rating scale module 218 displays contextual information to the user. It may be understood that the contextual information may be displayed based upon the psychological disorder detected. In one example, when the psychological disorder is detected as the bipolar disorder, the rating scale module 218 may display the contextual information corresponding to the bipolar disorder. In one aspect, the contextual information may be stored in the database 224. The contextual information may comprise one or more contents represented in form of animated images, a game, an audio file, a video file, a set of questionnaire, and the like. In one aspect, a content of the one or more contents may be displayed to the user thereby prompting the user to provide a response corresponding to the content.
[0048] After displaying the content, the rating scale module 218 may receive the response corresponding to the content as provided by the user. In one aspect, the positiveness in the response towards the content signifies the presence of the psychological disorder in the user. Once the response is received, the rating scale module 218 may further assign a score based upon the response. In one embodiment, the score may be assigned based on the response corresponding to the content. In another embodiment, an average score may be assigned by averaging the score assigned corresponding to each response of the one or more contents displayed to the user. Based upon assigning the score or the average score, the rating scale module 218 may further validate the presence of the psychological disorder in the user. It may be understood that the psychological disorder may be validated when the score or the average score is greater than a pre-defined score.
[0049] In one aspect, the report generation module 220 may generate a report depicting the psychological condition of the user. The report may facilitate to provide a possible consultation with a clinical psychsician to take necessary measures to cure the user suffering from the psychological disorder. In this manner, the presence of the psychological disorder in the user may be detected validated and thus indicating the psychological condition of the user in the report for the reference of the clinical psychsician.
[0050] In order to understand the working of the rating scale module 218, consider an example where the psychological disorder detected by the reasoning module 216 is the bipolar disorder.
[0051] In one embodiment, since the presence of the bipolar disorder is detected in ‘User A’, the rating scale module 218 displays the contextual information in the form of a set of questionnaire. It may be understood that the set of questionnaire may be associated to the bipolar disorder, the set of questionnaire may include, but not limited to, one or more questions corresponding to the one or more symptoms associated to the bipolar disorder. The one or more questions may include, but not limited to,
[0052] Symptom: Depressed Mood
[0053] Question 1: How has your mood been over the last few days?
[0054] Question 2: Have you felt depressed, sad or flat?
[0055] Question 3: Do you experience emotions other than depression?
[0056] Question 4: Have you had feelings of helplessness or hopelessness?
[0057] How do you feel about the future?
[0058] Question 5: How intense are these feelings?
[0059] Question 6: How persistent are these feelings?
[0060] Symptom: Suicidal Ideation
[0061] Question 1: Do you feel that life is not worthwhile or meaningless?
[0062] Question 2: Do you have thoughts of death or dying?
[0063] Question 3: Do you feel that you would be better off dead?
[0064] Question 4: Have you thought about ending your own life?
[0065] Question 5: Have you had thoughts about harming yourself?
[0066] Question 6: Have you made any plans?
[0067] Symptom: Guilt
[0068] Question 1: Do you find yourself feeling guilty about things that have happened in the past?
[0069] Question 2: Are you self critical about your role in things that have gone wrong?
[0070] Question 3: How intense are these feelings?
[0071] Question 4: Are they there some of the time or all of the time?
[0072] Question 5: Do you think these feelings are excessive?
[0073] Question 6: Do you feel in some ways that having this illness is a punishment?
[0074] Symptom: Sleep Disturbance
[0075] Question 1: How has your sleep been over the last couple of days?
[0076] Question 2: How many hours would you usually sleep when you are well?
[0077] Question 3: Is your sleep broken?
[0078] Question 4: Do you awake feeling refreshed?
[0079] Question 5: How many hours in total have you been sleeping over the last couple of nights?
[0080] Since the depressed mood, the suicidal ideation, the guilt, and the sleep disturbance are symptoms of the bipolar disorder, the rating scale module 218 displays the aforementioned set of questionnaire to the user. Upon displaying the one or more questions, the rating scale module 218 may further receive the response corresponding to each question of the one or more questions as displayed.
[0081] In another embodiment, the rating scale module 218 displays the contextual information in the form of a video that may comprise a character in a specific avatar. It may be understood that the symptoms of the psychological disorder are inherited into the character by the rating scale module 218. Since the symptoms of the psychological disorder are inherited, the character in the video is supposed to be suffering from the bipolar disorder. As a result, the rating scale module 218 may enable the character to perform one or more activities corresponding to a scenario of one or more scenarios associated to the bipolar disorder. It may be understood that each activity performed by the character indicates a specific analogy associated to a pre-defined score. In one example, consider a symptom (i.e. ‘Suicidal Ideation’) indicating the presence of the bipolar disorder in the character. In this example when the character performs the one or more activities corresponding to ‘scenario 1’, the pre-defined score assigned for the ‘scenario 1’ is “1”. The scenario 1 herein indicates that “life is not worthwhile or is meaningless”. Similarly, when the character performs the one or more activities corresponding to ‘scenario 2’, the pre-defined score for the ‘scenario 2’ is “2”. The scenario 2 herein indicates that “dying or death, but with no active suicide thoughts or plans”. Similarly, when the character performs the one or more activities corresponding to ‘scenario 3’ the pre-defined score assigned for the ‘scenario 3’ is “3”. The scenario 3 herein indicates that “plans of suicide”,
[0082] Upon displaying the video comprising at least one of the aforementioned scenarios, the rating scale module 218 may prompt the ‘User A’ to provide the response corresponding to the specific scenario comprised in the video. In one aspect, when the character performs the one or more activities corresponding to ‘scenario 2’, the response received in form of a ‘like’ from the user signifies the presence of the psychological disorder (i.e. the bipolar disorder) in the ‘User A’. Thus, in this manner, the rating scale module 218 may assign the score ‘2’.
[0083] Upon assigning the score, the rating scale module 218 compares the score with the pre-defined score in order to validate the presence of the psychological disorder. In one aspect, when the score is greater than a pre-defined score, the presence of the psychological disorder is validated and thus the psychological condition of the ‘User A’ is detected. In an alternative embodiment, several videos corresponding to different scenarios may be displayed to the user, and an individual score may be assigned to response received corresponding to each scenario. Further, an average score indicative of average of the scores assigned to different scenarios may be obtained. Further, the average score may be compared with the pre-defined score in order to validate the presence of the psychological disorder.
[0084] Referring now to Figure 4, a method 400 for detecting a psychological condition of a user is shown, in accordance with an embodiment of the present disclosure. The method 400 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types. The method 400 may be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, computer executable instructions may be located in both local and remote computer storage media, including memory storage devices.
[0085] The order in which the method 400 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method 400 or alternate methods. Additionally, individual blocks may be deleted from the method 400 without departing from the spirit and scope of the disclosure described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method 400 may be considered to be implemented in the above described in the system 102.
[0086] At block 402, textual content from one or more social networking servers may be captured. In one aspect, the textual content captured may be associated to the user. In one implementation, the textual content may be captured by the information capturing module 212.
[0087] At block 404, a textual pattern may be derived over a periodic time interval based upon analysis of the textual content. In one aspect, the textual content may be analyzed by using a text analytics technique. In one implementation, the textual pattern may be derived by the anomaly identification module 214.
[0088] At block 406, a psychological disorder of a plurality of psychological disorders may be detected. In one aspect, the plurality of psychological disorders and a plurality of symptoms associated therewith are stored in a rule database. The psychological disorder may be detected by mapping the textual pattern with one or more symptoms corresponding to the psychological disorder. In one implementation, the psychological disorder may be detected by the reasoning module 216.
[0089] At block 408, contextual information may be displayed to the user based upon the psychological disorder detected. In one aspect, the contextual information may be stored in a database. In one implementation, the contextual information may be displayed by the rating scale module 218.
[0090] At block 410, a response associated with the contextual information from the user may be received. In one aspect, the response signifies presence of the psychological disorder in the user. In one implementation, the response may be received by the rating scale module 218.
[0091] At block 412, a score may be assigned based upon the response. In one implementation, the score may be assigned by the rating scale module 218.
[0092] At block 414, the psychological disorder may be validated based upon the score, thereby detecting the psychological condition of the user. In one implementation, the psychological disorder may be validated by the rating scale module 218.
[0093] Although implementations for methods and systems for detecting a psychological condition of a user have been described in language specific to structural features and/or methods, it is to be understood that the appended claims are not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as examples of implementations for detecting the psychological condition of the user.
[0094] Exemplary embodiments discussed above may provide certain advantages. Though not required to practice aspects of the disclosure, these advantages may include those provided by the following features.
[0095] Some embodiments enable a system and a method to detect behavioral anomalies based on models of psychiatric normalcy.
[0096] Some embodiments enable a system and a method to automatically detect behavioral anomaly and classify the behavioral anomaly from a plurality of behavioral anomalies based on the information published on a social media.
[0097] Some embodiments enable a system and a method to provide ubiquitous analysis of human psychiatric condition.
[0098] Some embodiments enable a system and a method to automatic presentation and analysis of the rating-scale tests for psychiatric analysis.
[0099] Some embodiments enable a system and a method to validate the behavioral anomaly detected based on the interactive contextual information presented to the user. ,CLAIMS:We Claim:

1. A method for detecting a psychological condition of a user, the method comprising:
capturing, by a processor, textual content from one or more social networking servers, wherein the textual content captured is associated to the user;
deriving, by the processor, a textual pattern over a periodic time interval based upon analysis of the textual content, wherein the textual content is analyzed using a text analytics technique;
detecting, by the processor, a psychological disorder of a plurality of psychological disorders, wherein the plurality of psychological disorders and a plurality of symptoms associated therewith are stored in a rule database, and wherein the psychological disorder is detected by mapping the textual pattern with one or more symptoms corresponding to the psychological disorder;
displaying, by the processor, contextual information to the user based upon the psychological disorder detected, wherein the contextual information are stored in a database;
receiving, by the processor, a response associated with the contextual information from the user, wherein the response signifies presence of the psychological disorder in the user;
assigning, by the processor, a score based upon the response; and
validating, by the processor, the psychological disorder detected based upon the score, thereby detecting the psychological condition of the user.

2. The method of claim 1, wherein the text analytics technique comprises a natural language processing (NLP) technique, a grammatical pattern recognition technique, a sentiment analysis technique.

3. The method of claim 1, wherein the plurality of psychological disorders comprises schizophrenia, borderline personality disorders, depression, Alzheimer, delusions.

4. The method of claim 1, wherein the contextual information comprises one or more contents displayed in a form of an animated images, a game, an audio file, a video file, a set of questionnaire, and wherein the contextual information is prompting the user to provide the response corresponding to the one or more contents.

5. The method of claim 1, wherein the score is assigned by averaging the score corresponding to each response of the one or more contents.

6. The method of claim 1, wherein the psychological disorder is validated when the score is greater than a pre-defined score.

7. A system for detecting a psychological condition of a user, the system comprising:
a processor; and
a memory coupled to the processor, wherein the processor is capable of executing a plurality of modules stored in the memory, and wherein the plurality of modules comprising:
an information capturing module for capturing textual content from one or more social networking servers, wherein the textual content captured is associated to the user;
an anomaly identification module for deriving a textual pattern over a periodic time interval based upon analysis of the textual content, wherein the textual content is analyzed using a text analytics technique;
a reasoning module for detecting a psychological disorder of a plurality of psychological disorders, wherein the plurality of psychological disorders and a plurality of symptoms associated therewith are stored in a rule database, and wherein the psychological disorder is detected by mapping the textual pattern with one or more symptoms corresponding to the psychological disorder; and
a rating scale module for
displaying contextual information to the user based upon the psychological disorder detected, wherein the contextual information are stored in a database,
receiving a response associated with the contextual information from the user, wherein the response signifies presence of the psychological disorder in the user,
assigning a score based upon the response, and
validating the psychological disorder detected based upon the score, thereby detecting the psychological condition of the user.

8. The system of claim 7, wherein the rating scale module assigns the score by averaging the score corresponding to each response of one or more contents, and wherein the one or more contents is indicative of the contextual information.

9. The system of claim 7, wherein the rating scale module validates the psychological disorder when the score is greater than a pre-defined score.

10. The system of claim 7 further comprising a report generating module for generating a report depicting the one or more symptoms indicating the psychological condition of the user.

11. A non-transitory computer readable medium embodying a program executable in a computing device for detecting a psychological condition of a user, the program comprising:
a program code for capturing textual content from one or more social networking servers, wherein the textual content captured is associated to the user;
a program code for deriving a textual pattern over a periodic time interval based upon analysis of the textual content, wherein the textual content is analyzed using a text analytics technique;
a program code for detecting a psychological disorder of a plurality of psychological disorders, wherein the plurality of psychological disorders and a plurality of symptoms associated therewith are stored in a rule database, and wherein the psychological disorder is detected by mapping the textual pattern with one or more symptoms corresponding to the psychological disorder;
a program code for displaying contextual information to the user based upon the psychological disorder detected, wherein the contextual information are stored in a database;
a program code for receiving a response associated with the contextual information from the user, wherein the response signifies presence of the psychological disorder in the user;
a program code for assigning a score based upon the response; and
a program code for validating the psychological disorder detected based upon the score, thereby detecting the psychological condition of the user.