A system and a method for alerting and attending to adverse events

Abstract

The present disclosure discloses a system (100) and a method (200) for alerting and attending to adverse events outs. The system (100) comprises a centralized server (102) to communicate with an application platform (104) comprising a set of artificial intelligence- bots (AI-bots) to handle emergency protocol over an Internet of Things (IoT) environment (106); a registration module (110) to register a new member and generate a unique digital code; an emergency detection module (112) to determine and trigger the nature of the emergency or detect and identify said nature of the emergency, trigger and categorize said nature of the emergency; an identification module (116) to identify and authenticate member identity after said emergency and generate at least one alert notification; a notification module (118) to receive said categorized nature of the emergency and said alert notification, and to initialize said set of AI-bots to communicate with at least one emergency service, emergency responders and authorities.

Description

[0001] A SYSTEM AND A METHOD FOR ALERTING AND ATTENDING TO ADVERSE

[0002] EVENTS

[0003] FIELD OF INVENTION

[0004] The present disclosure generally relates to the field of alerting and attending to adverse events. More particularly, the present disclosure relates to a system and a method for alerting and attending to adverse events.

[0005] DEFINITIONS

[0006] As used in the present disclosure, the following terms are generally intended to have the meaning as set forth below, except to the extent that the context in which they are used indicates otherwise.

[0007] Adverse / Emergency events: The term ‘adverse / emergency event’ hereinafter interchangeably used in the disclosure refers to accidents and medical emergencies including fainting, falling and losing consciousness, reducing or eliminating the possibility of asking for help by themselves.

[0008] Member / victim: The term ‘member / victim’ hereinafter interchangeably used in the disclosure refers to a person / driver who meets with an accident or is having a medical emergency without the ability to inform anyone by themselves.

[0009] BACKGROUND

[0010] Around the world, people face dangerous and potentially life-threatening emergencies daily. In India, specific demographics, including women, senior citizens, and young adults, such as college and university students, are particularly vulnerable. For instance, data from the National Crime Report Bureau (NCRB) reveals that a woman is raped every six minutes, and the National Family Health Survey (NFHS) reports that about 50% of women experience domestic abuse. The National Institutes of Health (NIH) states that nearly 2 million elders suffer injurious falls each year, with half of them eventually succumbing to their injuries. Finally, there are approximately 500,000 devastating road accidents per year, 70% of which involve the youth. Oftentimes, these accidents can lead to the declaration of ‘John and Jane Doe’ due to the lack of proper identification documents and scarring injuries, and being treated as unnamed and unidentified individuals in general and government hospitals, irrespective of their personal preferences.

[0011] While these statistics are already worrisome, the prevalence of such incidents is only likely to increase. Currently, India has a population of over 1.4 billion residents. 150 million of these individuals are senior citizens, projected to reach 350 million by 2051. There are also currently over 650 million women in the country. One thing is clear: regardless of the nature of these emergencies or who is affected, there is a critical need for prompt, reliable, and professional assistance to save lives.

[0012] While the West has sufficiently addressed this need well, through the introduction of its 911 system in 1968, the emergency response programs in India are still undergoing improvements. Although there are provisions for citizens to reach out for assistance, many individuals are still unaware of whom to contact. For example, it has been noted repeatedly that people will reach out to local, obsolete numbers such as 100, 101, and 108 for police and ambulance support, compared to 112, the central line. Others wish to continue to coordinate with local providers, such as hospital ambulance lines and jurisdiction police lines but are left disappointed when their numbers are invalid, or their services are undependable. Finally, some individuals consciously restrain interactions with the emergency authorities due to allegations of aggression or unethical practices. These factors combined lead to a general mistrust and pessimistic outlook on the availability of services, resulting in the potential mismanagement of emergencies due to chaos, panic, and confusion arising from independent handling.

[0013] When citizens choose to handle emergencies independently, three significant issues arise. The first is the lack of efficient communication. Take the example of an individual suffering from a stroke. At the most, they may be able to place one phone call to a loved one before being completely disoriented. If they were alone and if that phone call was silenced, by the time help was reached, it may be far too late, resulting in one’s loss to perform all Activities of Daily Living (ADL). Next is the lack of role clarity; even if a few beloveds are informed of the situation, they seldom know what to do. For example, who should call the ambulance and at what number? Who should go to the hospital with the patient? Who should perform first aid, and are they knowledgeable / equipped to do so? Lastly, an inherent lack of vital information, such as one’s blood group, medication allergies, insurance coverage, etc., leads to delays while trying to gather this information.

[0014] Therefore, there is felt a need for a system and a method for alerting and attending to adverse events.

[0015] OBJECTS

[0016] Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows:

[0017] It is an object of the present disclosure to ameliorate one or more problems of the prior art or to at least provide a useful alternative.

[0018] An object of the present disclosure is to provide a system for alerting and attending to adverse / emergency events.

[0019] Another object of the present disclosure is to provide a system for alerting and attending to adverse events that ensure the well-being and safety of the members of such events.

[0020] Yet another object of the present disclosure is to provide a system that accurately identifies anonymous users who have been in severe accidents and medical emergencies through uniquely generated unique digital codes.

[0021] Still another object of the present disclosure is to provide a secure authentication process for identifying individuals involved in serious accidents and medical emergencies.

[0022] Yet another object of the present disclosure is to provide a system that ensures user identification in the absence of manual verification.

[0023] Still another object of the present disclosure is to provide a system for facial reconstruction in cases where the damage is too severe for standard facial recognition.

[0024] Yet another object of the present disclosure is to provide a system that facilitates automated communications to all members of the emergency response team of the informal support network and professional authorities via phone calls and messaging technologies. Still another object of the present disclosure is to provide a role-based sharing of confidential information based on user-defined permissions to ensure that sensitive data does not fall into unauthorized hands.

[0025] Yet another object of the present disclosure is to provide a method for alerting and attending to adverse events.

[0026] Other objects and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.

[0027] SUMMARY

[0028] The present disclosure envisages a system and a method for alerting and attending to adverse events. The system comprises a centralized server, an application platform, and an Internet of Things (loT) environment.

[0029] The centralized server is configured to communicate with an application platform comprising a set of artificial intelligence- hots (Al-bots) to handle at least one emergency protocol over an Internet of Things (loT) environment.

[0030] The centralized server includes a data collection module, a registration module, an emergency detection module, an identification module, and a notification module.

[0031] The data collection module is configured to collect and store vehicle details, member personal details, registration details, pre-uploaded images of members in different projections, live positioning data of the vehicle and member devices, a set of authentication rules, a pre-trained Al model, and a predefined emergency protocol.

[0032] The registration module is configured to register a new member and generate a unique digital code for each registered member.

[0033] The emergency detection module is configured to determine and trigger the nature of the emergency in real-time by means of a computing device of the member or detect and identify the nature of the emergency by a third party member by scanning the unique digital code or capturing a real-time image, and further configured to trigger and categorize the nature of the emergency. The identification module is configured to identify and authenticate the member identity after the occurrence of the nature of the emergency by means of the set of authentication rules or by means of the pre-trained Al model and is further configured to generate at least one alert notification.

[0034] The notification module is configured to cooperate with the emergency detection module and the identification module to receive the categorized nature of the emergency and the alert notification and is further configured to initialize the set of Al-bots to communicate with at least one emergency service, emergency responders and authorities according to at least one emergency protocol.

[0035] In an embodiment, the system comprises a wearable device configured to cooperate with the application platform to receive blood pressure, pulse rates and ECGs, oxygen saturation, and other information gathered by means of a plurality of sensors of the wearable device.

[0036] In an embodiment, the system comprises an encryption module configured to encrypt the vehicle details, member personal details, and registration details to prevent unwanted data leaks.

[0037] In an embodiment, the unique digital code is embedded with an intuitive text mentioning the use of the unique digital code to the third parties, wherein the unique digital code is placed on wallets, vehicle windshields, motorbike helmets, and phone cases, or any visible location of the vehicle.

[0038] In an embodiment, the nature of the emergency includes accidents, medical emergencies, vehicle failure, intersection collisions, fire accidents, crash injuries, floods, and women's safety.

[0039] In an embodiment, the nature of the emergency is triggered by means of a computing device of the member or pressing the emergency button, voice command, or other means of activating the trigger event.

[0040] In an embodiment, the emergency protocol is selected from a group of protocols consisting of medical services, fire services, women's safety services, transport services, fire services, ambulance services, and petroleum services. In an embodiment, the third party members include formal team members, informal team members, family members, friends, passer by vehicle members, or any person.

[0041] In an embodiment, the emergency event is triggered by using mobile applications, wearable devices, phone calls (backend ticket generation), or any computing device capable of detecting the nature of the emergency, or by scanning the unique digital code manually or by using artificial intelligence intervention or any device capable of scanning the unique digital code.

[0042] In an embodiment, the set of authentication rules is a set of instructions used to identify and authenticate the member by three-factor authentication and select a correct facial image from verified pre -uploaded images of members in different projections along with multiple AI- generated false images, and further configured to identify and confirm a unique identification mark and unique physical features on the member body which includes a scar, birthmark, tattoo or any identical mark.

[0043] In an embodiment, the system further comprises a face reconstruction module configured to capture an image of the member after an accident and perform cross-referencing with the preuploaded face images of the members by means of the pre-trained Al model.

[0044] In an embodiment, the pre-trained Al model is configured to perform a facial reconstruction and recognition to confirm the identity of the member after an accident and further determine the degree of confidence so that at least one emergency protocol for emergency response, handling, and communications can be invoked.

[0045] In an embodiment, the facial reconstruction is performed by:

[0046] • receiving captured image;

[0047] • obtaining three-dimensional facial structure for depth information;

[0048] • enhancing the quality of the image by noise reduction, resolution enhancement, and image alignment / orientation; selecting and loading at least one pre-trained model such as VGG-Face, or FaceNet for feature extraction; detecting key facial landmarks such as eyes, nose, mouth, and chin;

[0049] • creating a three-dimensional mesh of the face map of the key features; and

[0050] • reconstructing and visualizing the facial image.

[0051] In an embodiment, the degree of confidence provides the matching identification in percentage, where the highest degree of confidence is 100% (High Match), and the lowest degree of confidence is 10% (Low match).

[0052] In an embodiment, the alert notification includes the live location of the emergency event, recorded audio clips, recorded video feeds, phone events, and distress signals which include crash and fall detection, and geofencing alerts.

[0053] In an embodiment, the set of Al-bots includes a first AI-Bot, a second AI-Bot, and a third AI- Bot, wherein the first AI-Bot, the second AI-Bot, and the third AI-Bot are in communication with each other to provide feedback.

[0054] In an embodiment, the second AI-Bot is configured to:

[0055] • determine the triggered nature of the emergency event and identify the member and third party (people) closest to the member;

[0056] • identify the triggered nature of the emergency and update the first AI-Bot; and

[0057] • dispatch the nearest at least one emergency service, the emergency responders, and the authorities to assess the one emergency service, the emergency responders, and the authorities when the second AI-Bot is unable to identify the triggered emergency event.

[0058] In an embodiment, the third AI-Bot is configured to inform formal and informal team members of the triggered emergency event or urge them to join a video call.

[0059] In an embodiment, the first AI-Bot is configured to: update the first AI-Bot based on the feedback received by the second AI-Bot and the third AI-Bot; • connect and join video calls and familiarize with the real-time situation and alert notifications which include vital emergency information, including medical profiles, KYC details, a user’s live location, audio clips, and other critical data, to emergency authorities in real-time;

[0060] • wait for team members to arrive at the location;

[0061] • communicate with Hospital coordination to direct the first team member to visit a hospital, brief the on-call doctors about the nature of the incident, and share the medical profile of the victim to ensure proper care during diagnosis and treatment,

[0062] • communicate with location coordination to direct the second team member to visit the location and perform first aid,

[0063] • communicate with ambulance coordination to direct the third team member to stay on the line with the ambulance driver until reaches to location,

[0064] • communicate with doctor consultant coordination to direct the fourth team member to communicate with a doctor over a video call and provide verbal consent.

[0065] The present disclosure also envisages a method for alerting and attending to adverse events. The method comprises the following steps:

[0066] • communicating, by a centralized server with an application platform comprising a set of artificial intelligence-bots (Al-bots) to handle at least one emergency protocol over an loT environment;

[0067] • registering, by a registration module, a new member and generating a unique digital code for each registered member;

[0068] • determining and triggering, by an emergency detection module, the nature of the emergency in real-time by means of a computing device of the member or detecting and identifying the nature of the emergency by a third party member by scanning the unique digital code and categorizing the triggered nature of the emergency; identifying and authenticating, by an identification module, the member identity after the occurrence of the nature of the emergency by means of the set of authentication rules or by means of the pre-trained Al model and generating at least one alert notification; and

[0069] • receiving, by a notification module, the categorized the nature of the emergency and the alert notification and initializing the set of Al-bots to communicate with at least one emergency service, emergency responders, and authorities to attend the event according to at least one emergency protocol.

[0070] BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWING

[0071] A system and a method for alerting and attending to adverse events of the present disclosure will now be described with the help of the accompanying drawing, in which:

[0072] Figure 1 illustrates a block diagram of a system for alerting and attending to adverse events;

[0073] Figure 2 illustrates a flow chart depicting steps involved in the method for alerting and attending to adverse events in accordance with an embodiment of the present disclosure;

[0074] Figure 3 illustrates a flow chart depicting steps for the identification of members in accordance with an embodiment of the present disclosure;

[0075] Figure 4 illustrates a flow chart depicting steps for Al-based emergency coordination in accordance with an embodiment of the present disclosure; and

[0076] Figure 5 illustrates an exemplary embodiment depict the various scenarios of the system in accordance with an embodiment of the present disclosure.

[0077] LIST OF REFERENCE NUMERALS

[0078] 100 - System

[0079] 102 - Centralized Server

[0080] 104 - Application Platform

[0081] 106 - Internet Of Things (loT) Environment

[0082] 108- Data Collection Module 110 - Registration Module

[0083] 112 - Emergency Detection Module

[0084] 114 - Computing Device

[0085] 116 - Identification Module

[0086] 118 - Notification Module

[0087] 120 - Wearable Device

[0088] 120a - Plurality of Sensors

[0089] 122 - Encryption Module

[0090] 124 - Face Reconstruction Module

[0091] 502 - Input Unit / Device

[0092] 502a - Image capturing unit

[0093] 504- Control Unit

[0094] 504a - Extractor

[0095] 504b - Comparator

[0096] 504c - Processor

[0097] 506- Repository

[0098] 508 - Analyzer

[0099] 508a - Moderator Unit

[0100] 510 - Transmitter

[0101] 512 - Application Interface

[0102] 514 - Remote Device 516 - Receiver

[0103] DETAILED DESCRIPTION

[0104] Embodiments, of the present disclosure, will now be described with reference to the accompanying drawing.

[0105] Embodiments are provided so as to thoroughly and fully convey the scope of the present disclosure to the person skilled in the art. Numerous details, are set forth, relating to specific components, and methods, to provide a complete understanding of embodiments of the present disclosure. It will be apparent to the person skilled in the art that the details provided in the embodiments should not be construed to limit the scope of the present disclosure. In some embodiments, well-known processes, well-known apparatus structures, and well-known techniques are not described in detail.

[0106] The terminology used, in the present disclosure, is only for the purpose of explaining a particular embodiment and such terminology shall not be considered to limit the scope of the present disclosure. As used in the present disclosure, the forms "a,” "an," and "the" may be intended to include the plural forms as well, unless the context clearly suggests otherwise. The terms “including,” and “having,” are open ended transitional phrases and therefore specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not forbid the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. The particular order of steps disclosed in the method and process of the present disclosure is not to be construed as necessarily requiring their performance as described or illustrated. It is also to be understood that additional or alternative steps may be employed.

[0107] When an element is referred to as being “engaged to,” "connected to," or "coupled to" another element, it may be directly engaged, connected, or coupled to the other element. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed elements.

[0108] In India, vulnerable demographics such as women, senior citizens, and young adults face dangerous and life-threatening emergencies daily. Data reveals alarming statistics: a woman is raped every six minutes, nearly 50% of women experience domestic abuse, 2 million elders suffer injurious falls yearly, and around 500,000 road accidents occur annually, with 70% involving youth. The growing population, including 150 million senior citizens and over 650 million women, underscores the critical need for prompt and reliable emergency assistance.

[0109] India's emergency response system, unlike the West's established 911 system, remains underdeveloped. Many citizens are unaware of the correct emergency contact numbers and mistrust authorities due to allegations of aggression and unethical practices. This leads to inefficient independent handling of emergencies, characterized by poor communication, unclear roles, and lack of vital information, resulting in mismanagement and delays.

[0110] There is a need for an Al-driven solution using technologies like Large Language Models (LLMs), Text-to-Speech (TTS) systems, Generative Adversarial Networks (GANs), and Machine Learning (ML) algorithms. This system would use personalized digital code flows for user identification and provide real-time emergency moderation. It aims to ensure proper notification, coordination, and task delegation within local networks while facilitating effective communication with existing crisis management infrastructure and authorities.

[0111] To address the issues of the existing systems and methods, the present disclosure envisages a system for alerting and attending to adverse events (hereinafter referred to as “system 100”) and a method for alerting and attending to adverse events (hereinafter referred to as “method 200”). The system 100 will now be described with reference to Figure 1 and the method 200 will be described with reference to Figure 2.

[0112] Referring to Figure 1, the system 100 comprises a centralized server 102, an application platform 104, and an Internet of Things (loT) environment.

[0113] The centralized server 102 is configured to communicate with an application platform 104 comprising a set of artificial intelligence-bots (Al-bots) to handle at least one emergency protocol over an Internet of Things (loT) environment 106.

[0114] The centralized server 102 includes a data collection module 108, a registration module 110, an emergency detection module 112, an identification module 116, and a notification module 118.

[0115] The data collection module 108 is configured to collect and store vehicle details, member personal details, registration details, pre-uploaded images of members in different projections, live positioning data of the vehicle and member devices, a set of authentication rules, a pre-trained Al model, and a predefined emergency protocol.

[0116] The registration module 110 is configured to register a new member and generate a unique digital code for each registered member.

[0117] In an embodiment, the unique digital code is embedded with an intuitive text mentioning the use of the unique digital code to the third parties, wherein the unique digital code is placed on wallets, vehicle windshields, motorbike helmets, and phone cases, or any visible location of the vehicle.

[0118] The emergency detection module 112 is configured to determine and trigger the nature of the emergency in real-time by means of a computing device 114 of the member or detect and identify the nature of the emergency by a third party member by scanning the unique digital code or capturing a real-time image, and further configured to trigger and categorize the nature of the emergency.

[0119] In an embodiment, the nature of the emergency includes accidents, medical emergencies, vehicle failure, intersection collisions, fire accidents, crash injuries, floods, and women's safety.

[0120] In an embodiment, the nature of the emergency is triggered by means of a computing device 114 of the member pressing the emergency button, voice command, or other means of activating the trigger event.

[0121] In an embodiment, the third-party members include formal team members, informal team members, family members, friends, passed by vehicle members, or any person.

[0122] In an embodiment, the emergency event is triggered by using mobile applications, wearable devices, phone calls (backend ticket generation), or any computing device capable of detecting the nature of the emergency, or by scanning the unique digital code manually or by using artificial intelligence intervention or any device capable of scanning the unique digital code.

[0123] In an embodiment, the unique digital code is selected from a group of codes consisting of a QR code, barcode, virtual code, graphic code, alpha-numeric code, or any digital code containing code with a message / quote. The identification module 116 is configured to identify and authenticate the member identity after the occurrence of the nature of the emergency by means of the set of authentication rules or by means of the pre-trained Al model and is further configured to generate at least one alert notification.

[0124] In an embodiment, the set of authentication rules is a set of instructions used to identify and authenticate the member by three-factor authentication and select a correct facial image from verified pre -uploaded images of members in different projections along with multiple AI- generated false images, and further configured to identify and confirm a unique identification mark and unique physical features on the member body which includes a scar, birthmark, tattoo or any identical mark.

[0125] In an embodiment, the pre-trained Al model is configured to perform a facial reconstruction and recognition to confirm the identity of the member after an accident and further determine the degree of confidence so that at least one emergency protocol for emergency response, handling, and communications can be invoked.

[0126] In an embodiment, the facial reconstruction is performed by:

[0127] • receiving captured image;

[0128] • obtaining three-dimensional facial structure for depth information;

[0129] • enhancing the quality of the image by noise reduction, resolution enhancement, and image alignment / orientation;

[0130] • selecting and loading at least one pre-trained model such as VGG-Face, or FaceNet for feature extraction;

[0131] • detecting key facial landmarks such as eyes, nose, mouth, and chin; creating a three-dimensional mesh of the face map of the key features; and reconstructing and visualizing the facial image. In an embodiment, the set of Al-bots includes a first AI-Bot, a second AI-Bot, and a third AI- Bot, wherein the first AI-Bot, the second AI-Bot, and the third AI-Bot are in communication with each other to provide feedback.

[0132] In an embodiment, the set of Al-bots is selected from the group of Al-bots consisting of the “N” number of Al-bots configured with the centralized server 102 and ready to communicate with the application platform 104.

[0133] In an embodiment, the degree of confidence provides the matching identification in percentage, where the highest degree of confidence is 100% (High Match), and the lowest degree of confidence is 10% (Low match).

[0134] In an embodiment, the second AI-Bot is configured to:

[0135] • determine the triggered nature of the emergency event and identify the member and third party (people) closest to the member;

[0136] • identify the triggered nature of the emergency and update the first AI-Bot; and

[0137] • dispatch the nearest at least one emergency service, the emergency responders, and the authorities to assess the one emergency service, the emergency responders, and the authorities when the second AI-Bot is unable to identify the triggered emergency event.

[0138] In an embodiment, the third AI-Bot is configured to inform formal and informal team members of the triggered emergency event or urge them to join a video call.

[0139] In an embodiment, the first AI-Bot is configured to perform:

[0140] • update the first AI-Bot based on the feedback received by the second AI-Bot and the third AI-Bot;

[0141] • connect and join video calls and familiarize with the real-time situation and alert notifications which include vital emergency information, including medical profiles, KYC details, a user’s live location, audio clips, and other critical data, to emergency authorities in real-time;

[0142] • wait for team members to arrive at the location; • communicate with Hospital coordination to direct the first team member to visit a hospital and brief the on-call doctors;

[0143] • communicate with location coordination to direct the second team member to visit the location and perform first aid;

[0144] • communicate with ambulance coordination to direct the third team member to stay on the line with the ambulance driver until reaches to location; and

[0145] • communicate with doctor consultant coordination to direct the fourth team member to communicate with a doctor over a video call and provide verbal consent.

[0146] The notification module 118 is configured to cooperate with the emergency detection module 112 and the identification module 116 to receive the categorized nature of the emergency and the alert notification.

[0147] The notification module 118 is further configured to initialize the set of Al-bots to communicate with at least one emergency service, emergency responders, and authorities to attend the event according to at least one emergency protocol.

[0148] In an embodiment, the alert notification includes the live location of the emergency event, recorded audio clips, recorded video feeds, phone events, and distress signals which include crash and fall detection, and geofencing alerts.

[0149] In an embodiment, the emergency protocol is selected from a group of protocols consisting of medical services, fire services, women's safety services, transport services, fire services, ambulance services, and petroleum services.

[0150] In an embodiment, the system 100 comprises a wearable device 120 configured to cooperate with the application platform 104 to receive blood pressure, pulse rates and ECGs, oxygen saturation, and other information gathered by means of a plurality of sensors 120a of the wearable device 120.

[0151] In an embodiment, the system 100 comprises an encryption module 122 is configured to encrypt the vehicle details, member personal details, and registration details to prevent unwanted data leaks. In an embodiment, the system 100 further comprises a face reconstruction module 124 configured to capture an image of the member after an accident and perform crossreferencing with the pre-uploaded face images of the members by means of the pre-trained Al model.

[0152] In an exemplary embodiment, the system 100 generates unique digital codes for each member registered on the platform. These digital codes are designed to be placed in visible locations during accidents or medical emergencies. Examples include placement within wallets, on vehicle windshields, motorbike helmets, or phone cases. In situations where individuals facing emergencies are unable to raise the alert themselves due to severe pain or unconsciousness, the system 100 employs a three-factor authentication process to confirm their identities. This process, facilitated by a third party such as formal team members, informal team members, family members, friends, passed by vehicle members, or any person or help service, ensures accurate identification before the initiation of moderation and emergency handling procedures. The unique digital codes are embedded in the intuitive text (for example, “Scan here in times of emergency!”), ensuring that the third-parties are well- informed of the role that they are expected to play.

[0153] First, the third party must identify the individual from a set of images, with one being real and the others Al-generated. Second, the third party must recognize a visually distinct indicator that confirms the individual's identity, such as a birthmark, scar, or tattoo. These interactions will be done in local languages, thus preventing any tactical hindrances in the process. If they have accurately identified the compromised individual, communications are initiated, as detailed further in this section.

[0154] A simple image / photograph of the real-time situation is captured by means of the computing device to initiate automated processes in such situations where manual intervention is absent (for example, when the stranger indicates that they would not like to assist, stating liability fears). If the member has not been too severely harmed, a facial recognition model will be initiated by cross-referencing with pre -uploaded images in a database. If, however, the member’s face is significantly damaged, a facial reconstruction module 120 will be run to confirm the identity of the member after an accident. From here, the final image will then be used for user identification. As the accuracy of these results will depend highly on situational factors, such as the amount of blood on the face, facial fractures, vehicle debris, etc., the system will determine a degree of confidence (for example, “100% Match” or “10% Match”).

[0155] In another exemplary embodiment, the system 100 formal team members may consist of family members, local guardians who can quickly reach the individual, on-ground emergency responders, the company’s moderation hot health and auto insurance providers (for asset protection), and local emergency and SOS services. The communications will be sent with appropriate tones and language. For example, if it is a near certain match, the communications will read: “<First Name> <Last Name> has been in an emergency at <Location>. Please make your way there and / or join our video call for further instructions.”. Or, if it were only a 50% match, the communications would read: “<First Name> <Last Name> may have been in an accident. Please call them or go to<Location> to confirm!”. These communications use multi-lingual GPT and TTS technologies, ensuring zero need for human command. Last but not least, real-time data about the individual in distress, such as their blood pressure, pulse rates and ECGs, oxygen saturation, and other information gathered via the platform and their smart devices, will be captured and shared with authorized recipients, including health care professionals deployed on the case.

[0156] Another point to be noted here, however, is that the processes are not solely based on digital codes. For example, if a user scans the digital code but does not assist further, the system will use alternate cues. For example, if the user’s computing device has detected a crash, it can be concluded that the nature of the emergency is a vehicular accident. Similarly, if the unique digital code was scanned and the user’s wearable device indicated an extremely high and irregular heart rate, it can be derived that a cardiac arrest may have occurred. The unique digital codes are dynamic and flexible, meaning they do not encompass only a one-to-one relationship. Whether it is the primary user’s child, spouse, or driver, as long as they have provided a benchmark image, they will be identified using the procedures outlined above, and it is assured that the right people are informed. If there is no match after all efforts, then the individual will be informed that an unrecognized individual has had an emergency in possession of their digital code, and government emergency services will be deployed to the incident site.

[0157] The users will be required to initiate a set-up process within the platform for the system 100 and processes to work effectively. Their set-up process would involve providing details of their emergency response community, friends, and family members, detailing their health profiles, and supplying insurance and identification (Know Your Customer) information. As this information is sensitive and confidential, the sharing of this is purely role-based and dependent on access controls that are set by the users themselves, thus maintaining data privacy and security. It is also encrypted within the systems to prevent unwanted data leaks. Emergency protocols are then determined based on each stakeholder's availability of information. In addition, the member is urged to provide facial scans from various angles, upload photographs, and indicate their distinguishing physical features for the effective implementation of the unique digital codes.

[0158] Further, the emergency moderation procedures commence once a critical situation has been confirmed. The affected individual, his / her team members, and any relevant providers (such as the family doctor) will be invited to join a video call interaction. To mitigate any roleclarity issues (i.e., people not knowing how best to assist in such cases), a human-like Al hot will also be present, acting as the facilitator. Once again, this avatar will use LLMs in various languages and TTS technologies, ensuring an extremely natural and comfortable environment. The Al-bots will take crucial elements of information, piece them together, and delegate clear tasks to everyone present.

[0159] Examples of such information and tasks include the live locations of the individual member and other third parties in need and their on-ground emergency responders (such as coworkers or neighbors) to confirm the nature of the emergency and provide first-aid efforts or identify nearby multispecialty hospitals equipped to handle medical incidents of all severities or nearby police stations where first-information reports (FIRs) may be filed. The Al-bot also considers each member's permissions (e.g., someone with access to funds and insurance data can begin admission procedures in a hospital, while someone with access to a medical profile briefs the doctor, or family members provide consent to doctors). Dry runs and SOPs will be utilized for ideal AI-BOT training and execution. This assistance helps maintain calm and level-headed thinking during panic and chaos. These moderation procedures are activated when an emergency is raised through any mechanism, whether unique digital codes, mobile applications, wearable devices, or backend ticket creations.

[0160] Figure 2 illustrates a flow chart depicting steps involved in a method for alerting and attending to adverse events in accordance with an embodiment of the present disclosure. The order in which method 200 is described is not intended to be construed as a limitation, and any number of the described method steps may be combined in any order to implement method 200, or an alternative method. Furthermore, method 200 may be implemented by processing resource or computing device(s) through any suitable hardware, non-transitory machine -readable medium / instructions, or a combination thereof. The method 200 comprises the following steps:

[0161] At step 202, the method 200 includes communicating, by a centralized server 102 with an application platform 104 comprising a set of artificial intelligence-bots (AI- bots) to handle at least one emergency protocol over an loT environment 106.

[0162] At step 204, the method 200 includes registering, by a registration module 110, a new member and generating a unique digital code for each registered member.

[0163] At step 206, the method 200 includes determining and triggering, by an emergency detection module 112, the nature of the emergency in real-time by means of a computing device 114 of the member or detecting and identifying the nature of the emergency by a third party member by scanning the unique digital code and categorizing the triggered nature of the emergency.

[0164] At step 208, the method 200 includes identifying and authenticating, by an identification module 116, the member identity after the occurrence of the nature of the emergency by means of the set of authentication rules or by means of the pretrained Al model and generating at least one alert notification.

[0165] At step 210, the method 200 includes receiving, by a notification module 118, the categorizing of the nature of the emergency and the alert notification and initializing the set of Al-bots to communicate with at least one emergency service, emergency responders, and authorities to attend the event according to at least one emergency protocol.

[0166] Figure 3 illustrates a flow chart depicting steps for the identification of members in accordance with an embodiment of the present disclosure. The system 100 is designed for emergency coordination and identification of individuals using advanced Al and loT technologies. The flowchart provides a step-by-step process for handling emergencies, ensuring accurate identification, and efficient response. Here’s each step in the flowchart: Step 1 : User Registration and Unique Digital Code Generation o User Registration: The process begins with the registration of new users into the system. During this phase, users provide personal details, vehicle information, and pre-uploaded images. o Unique Digital Code Generation: Each registered user is assigned a unique digital code, which acts as a critical identifier during emergency situations. This code is essential for quickly and accurately identifying individuals when emergencies occur.

[0167] • Step 2: Data Collection o Comprehensive Data Gathering: The system collects a wide range of data, including live positioning data of vehicles and member devices, a set of authentication rules, and pre-trained Al models. This data is vital for accurate identification and effective emergency response.

[0168] • Step 3: Emergency Detection o Triggering the Emergency: An emergency can be triggered through various methods such as phone calls, wearables, or mobile applications. o Initial Assessment by First AI-Bot: A human-like Al avatar (First AI-Bot) is activated to interact with the user and nearby individuals to determine the nature of the emergency. This hot collects initial data and identifies the appropriate protocol for the situation.

[0169] • Step 4: Confidence Level Determination o Confirming Unique Identifiers: The system determines the confidence level of the detected emergency by confirming unique identifiers such as scars, birthmarks, or tattoos using advanced Al and machine learning algorithms.

[0170] • Step 5: Facial Recognition and AI / ML Integration o Facial Recognition: The system employs AI / ML technologies for facial recognition and reconstruction. It matches real images from the dataset with Al-generated images to ensure accurate identification of the individual in distress.

[0171] • Step 6: Identification and Three-Factor Authentication o Victim Identification: To ensure accurate identification, the system uses three- factor authentication, combining AI / ML image recognition with predefined authentication rules. This step ensures that the victim is correctly identified before proceeding with the emergency response.

[0172] • Step 7: Alert Notification and Module Activation o Generating Alerts: Upon successful identification and classification of the emergency, the identification module generates alert notifications. o Coordination by AI-Bots: The notification module activates Al-bots to communicate with emergency services and responders. These hots manage communication with doctors, ambulances, and hospitals, ensuring that all necessary parties are prepared and informed.

[0173] • Step 8: Real-Time Monitoring and Alternate Cues o Continuous Monitoring: The system continuously monitors live positioning data and other relevant metrics to provide real-time updates. Smart devices within the loT environment contribute alternate cues that enhance situational awareness and data accuracy.

[0174] • Step 9: Third-Party Involvement o Good Samaritan Assistance: Third parties, such as Good Samaritans, can assist in the emergency response. They can navigate to a browser-based questionnaire, provide location permissions, and offer visual inspection data, further refining the emergency response.

[0175] • Step 10: Feedback and Optimization o Post-Event Analysis: After the emergency is resolved, the system analyzes the response process to identify areas for improvement. o Updating Protocols: Insights from the analysis are used to update protocols and optimize future responses, ensuring continuous improvement in emergency management.

[0176] Figure 4 illustrates a flow chart depicting steps for Al-based emergency coordination in accordance with an embodiment of the present disclosure. The system begins with user registration and unique digital code generation. The detailed process of the system 100, uses a plurality of Al-bots and human team members to manage emergency situations efficiently. Here is a detailed analysis of each step in the flowchart:

[0177] • Emergency Trigger and Initial Response: o Emergency Triggered: The process starts when an emergency is detected through various means such as phone calls, wearables, or a mobile application; and o First AI-Bot Activation: A human-like Al avatar (First AI-Bot) is activated to gather initial information. It interacts with the user and nearby individuals to determine the nature of the emergency.

[0178] • Situation Identification and Communication: o Situation Identification: First AI-Bot attempts to identify the emergency. If the situation is recognized, it updates the system with the relevant protocol. o Team Notification: The second AI-Bot and Third AI-Bot, which function as communication hots, notify both formal and informal team members, urging them to join a video call to coordinate the response.

[0179] • Coordination of Response: o First AI-Bot for Doctor Consultation: This hot directs the team member responsible for doctor consultation to initiate a video call with a doctor who can provide verbal consent and medical advice; o First AI-Bot for Ambulance Coordination: This bot directs the team member to stay on the line with the ambulance driver, providing real-time updates and ensuring the driver reaches the emergency location promptly; o First AI-Bot for On-Location Coordination: This bot directs team members to the emergency location to perform necessary actions such as first aid; and o First AI-Bot for Hospital Coordination: This bot directs the team member responsible for hospital coordination to handle all procedures at the hospital, including briefing on-call doctors about incoming patients and sharing medical profiles with the attending doctor, and ensuring they are prepared. It also directs the team member to complete all hospital admission procedures based on the KYC information shared in the victim’s profile.

[0180] • Emergency Resolution: o A First Team Member goes to the hospital to brief on-call doctors; o A Second Team Member performs first aid at the location; o A Third Team Member stays on the line with the ambulance driver; and o A Fourth Team Member joins the video call with the doctor and provides verbal consent.

[0181] • Dispatch Closest Responder: The system dispatches the nearest emergency responder to assess and manage the situation in case no team members are reachable.

[0182] • Monitoring and Updates o Continuous Monitoring: The plurality of AI-Bots monitors the situation in real-time, providing updates and further instructions as necessary. o Emergency Coordination Planning: The plurality of AI-Bots ensures that all aspects of the emergency response are coordinated effectively, from on-site actions to hospital preparations.

[0183] Feedback and Optimization o Post-Event Analysis: After the emergency is resolved, the system analyzes the response process to identify areas for improvement. o System Updates: The insights from the analysis are used to update protocols and optimize future responses, ensuring continuous improvement in handling emergencies.

[0184] The system leverages Al technologies and real-time data to provide a comprehensive and efficient response to emergencies. By integrating the set of Al-bots with human team members, the system ensures that all necessary actions are taken promptly, from initial detection and communication to on-site intervention and hospital coordination. Continuous monitoring and feedback loops enhance the system's effectiveness, making it a robust solution for emergency management.

[0185] Figure 5 illustrates an exemplary embodiment depict the various scenarios of the system in accordance with an embodiment of the present disclosure. In an exemplary embodiment, the system 100 can be useful in a first scenario where a victim / member can by himself / herself raise an alert, in a second scenario where a passerby can raise the alert for the victim, and in a third scenario where multiple victims are unable to raise an alert.

[0186] • First scenario where a victim can by himself / herself raise an alert:

[0187] The system 100 includes an input unit 502 configured to be pressed by the victim, to generate an input signal to trigger an emergency event.

[0188] The system further includes a control unit 504 which includes a repository 506, an analyzer 508, and a transmitter 510.

[0189] In an embodiment, the repository 506 includes a set of contact details of the emergency response community.

[0190] In another embodiment, the emergency response community includes friends and family members of the victim who may be contacted in case of adverse situations.

[0191] The analyzer 508 is configured to receive the input signal. In an embodiment, the analyzer 508 includes a moderator unit 508a configured to work on the principles of Generative Pre- Trained Transformers (GPT) and Standard Operating Procedures (SOPs). The moderator unit 508a is configured to cooperate with the transmitter 510 to generate an alert signal to establish the contact with the stored contact details and leverage real-time location data and predetermined roles to ensure a coordinated response between the people corresponding to the stored contact details. The people contacted will be able to perform different tasks such as briefing the doctors, conducting the billing procedures, and verifying or contacting local emergency providers such as ambulance providers or police stations based on the role delegated by the moderator unit.

[0192] In an embodiment, the system 100 includes an application interface 512 configured to be installed in a remote device 514 of the people corresponding to the contact details. In another embodiment, the moderator unit 508a has a human-like avatar trained to use multiple languages.

[0193] • Second scenario where a victim is reliant on a Good Samaritan to raise an alert:

[0194] In an embodiment, the system 100 includes a digital code provided in a wallet, in a cell phone, on a bracelet, on apparel such as a jacket, on a windshield of a four-wheeler or a console of a two-wheeler, on a walking stick. The digital code is unique for each victim and describes details of the victim labelled thereon to be intuitive and used by passersby during the adverse event. It is required that the passerby scans the digital code to enable the opening of a web page on a computing device of the passerby which would share an image of the account holder identified uniquely by the digital code.

[0195] In another embodiment, the system 100 includes an image capturing unit 502a configured to capture a plurality of images containing the face of a victim, and an input device 502. In an embodiment, the input device 502 is a button configured to be pressed by the victim in case of an accident or a medical emergency which may include an accidental fall.

[0196] The repository is 506 further comprises a set of images of the face of a victim in various angles defining facial features of the victim, a set of extraction rules for extracting facial features from captured images, a set of images defining identification marks on the body of the victim such as birthmarks and / or scars, a set of preexisting health parameters, friends and family members of the victim, an image of digital code corresponding to the victim, a set of comparison rules for comparing captured facial features with the pre-stored set of images, a first set of identification rules for comparing identified body marks with the pre-stored identification marks, a second set of identification rules for identifying a digital code, and a set of rules for establishing the contact with the pre-stored contact details.

[0197] In another embodiment, the control unit 504 further includes an extractor 504a, a comparator 504b, and a processor 504c.

[0198] The extractor 504a is configured to receive the captured images and is further configured to extract the facial features from each captured image. The comparator 504b is configured to cooperate with the extractor to receive the extracted facial features and is further configured to compare the extracted facial features with the pre-stored facial features to generate a compared signal if the extracted facial features match the pre-stored facial features.

[0199] The processor 504c is configured to receive the identified body marks and is further configured to compare the identified body marks with the pre-stored identification marks to generate a processed signal if the identified body marks match the pre-stored identification marks.

[0200] Thus, the working of the system 100 is dependent on three factors that are:

[0201] • A digital code that identifies the potential victim;

[0202] • Face recognition (manual or through reconstruction algorithms); and

[0203] • Distinct identifying marks on the body to confirm identity.

[0204] In a working embodiment, the passerby would initiate the web page through the digital code and capture images as required by the web page to generate a compared signal if the facial features match with the stored features. Once the compared signal is generated, further interaction will take place through the web page.

[0205] In another embodiment, the web page will be configured to display the language of interaction for the passerby to freely communicate with the system. Further interaction includes requiring inputs related to the identification marks on the victim from the passerby. Once processor 608c generates the processed signal thus, completing the three-factor authentication pass, the moderator unit is configured to cooperate with the transmitter to generate an alert signal to establish contact with the stored contact details and leverage realtime location data and predetermined roles to ensure a coordinated response between the people corresponding to the stored contact details. The people contacted will be able to perform different tasks such as briefing the doctors, conducting the billing procedures, and verifying or contacting local emergency providers such as ambulance providers or police stations based on the role delegated by the moderator unit.

[0206] In the case that an initially captured facial image does not match, then the system is configured to automatically determine the entire community of members, friends, and family that have been set up in the victim’s account to see if a potential match is made and the people in the emergency can be identified. If this succeeds, the member will be informed that one of their team members has been in an accident with appropriate details. If however, this is also unsuccessful, then the moderator unit will establish contact with the passerby to get a live video feed and / or dispatch local SOS authorities to the current location. From the live feed, the control unit is configured to recognize facial features by reconstructing the features and comparing the reconstructed features with the stored features.

[0207] • Multiple victims unable to raise an alert:

[0208] In an embodiment, the system 100 further includes Nearer field communication (NFC) or wireless technology, which is useful in case multiple victims are traveling in the same vehicle as it ensures that all necessary teams are informed so that all victims are catered to. The moderator unit 508a will issue emergency protocols from there onwards as outlined elsewhere in this disclosure.

[0209] In an embodiment, the moderator unit 508a is configured to share relevant information such as insurance, KYC, and medical data of the victim with a medical service provider and an attending doctor to prepare them for the incoming trauma. Further, the moderator unit 508a is configured to establish communication with the nearest multispecialty hospital, to ensure that little to no time is wasted in briefing the doctor and scavenging for documents and data once the patient reaches the hospital.

[0210] To enable the above-mentioned feature, the repository 506 is further configured to store information about the victim, while the control unit includes a GPS unit that can detect the nearest hospitals or clinics. In an embodiment, the system includes a receiver 516 configured to communicate with wearable devices worn by the victim for identifying and sharing with the medics real-time vitals ranging from blood oxygen levels to heart rates and ECGs.

[0211] In one embodiment, the moderator unit is configured to establish a communication (preferably a video call) between ambulance staff, on-ground emergency responders, and attending doctors, so that the medical provider has a clear understanding of the situation in its current state to assemble the emergency room effectively and provide crucial advice and guidance to those with the victim.

[0212] In an embodiment, the repository 506 further includes contact details of car-towing services and insurance providers of the victim. The moderator unit 508a is configured to cooperate with the car-towing services and the insurance providers to help the victim protect their insured assets.

[0213] In an operative configuration, the system 100 architecture for alerting and attending to adverse events revolves around a centralized server 102 configured to communicate with an application platform 104 that comprises a set of artificial intelligence hots (Al-bots) to manage emergency protocols within an Internet of Things (loT) environment 106. The centralized server 102 integrates multiple modules to handle various stages of emergency management, ensuring comprehensive and timely responses to adverse events.

[0214] The data collection module 108 is pivotal and is responsible for aggregating and storing a wide range of data, including vehicle details, personal information of members, registration details, pre-uploaded images of members from different angles, live positioning data of vehicles and member devices, a set of authentication rules, a pre-trained Al model, and predefined emergency protocols. This rich data repository ensures that the system has all the necessary information to accurately identify and respond to emergencies.

[0215] The registration module 110 facilitates the onboarding of new members by generating unique digital codes for each registered individual. This module ensures that each member is uniquely identifiable within the system, allowing for precise tracking and emergency management.

[0216] The emergency detection module 112 is designed to identify and trigger responses to emergencies in real-time. It can determine the nature of the emergency through a member's computing device 114 or by a third party using the unique digital code or real-time image capture. Once an emergency is detected, this module categorizes the nature of the event, ensuring that the response is tailored to the specific type of emergency.

[0217] Upon the occurrence of an emergency, the identification module 116 is activated to verify the identity of the member. This is achieved using the predefined set of authentication rules or the pre-trained Al model. This identification module 116 not only authenticates the member but also generates alert notifications that are crucial for initiating a response.

[0218] The notification module 118 works in tandem with the emergency detection and identification modules. It receives the categorized nature of the emergency and the alert notifications, and subsequently initiates the set of Al-bots. These hots communicate with emergency services, responders, and relevant authorities as per the predefined emergency protocols. The notification module 118 ensures that all stakeholders are informed promptly and that the response is coordinated efficiently to address the adverse event.

[0219] Advantageously, the system 100 provides for alerting and attending to adverse events. The system is designed to identify an individual who has been in a compromised situation, such that the individual cannot operate a mobile phone or any application due to severe disability, pain, or unconscious state. The system 100 accurate identification of an individual using a three-factor authentication. The system 100 live locations, audio clips, video feeds, phone events, and distress signals (such as crash and fall detection, geofencing alerts, etc.) to determine the nature of the emergency and initiate appropriate protocols. The system 100 coordinates with local team members, informing them of the situation, and advising them on the steps to take to bring any situation under control and potentially save a life. The system coordinates with emergency responders and authorities to ensure the timely deployment of resources to the aid of the individual and share real-time health information such as vitals, prescriptions, blood groups, and allergies.

[0220] The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Other examples and implementations are within the scope and spirit of the disclosure and appended claims. For example, due to the nature of software, functions described above can be implemented using software executed by a processor, hardware, firmware, hardwiring, or combinations of any of these. Features implementing functions may also be physically located at various positions, including being distributed such that portions of functions are implemented at different physical locations.

[0221] The foregoing description of the embodiments has been provided for purposes of illustration and is not intended to limit the scope of the present disclosure. Individual components of a particular embodiment are generally not limited to that particular embodiment, but are interchangeable. Such variations are not to be regarded as a departure from the present disclosure, and all such modifications are considered to be within the scope of the present disclosure.

[0222] TECHNICAL ADVANCEMENTS

[0223] The present disclosure described herein above has several technical advantages including, but not limited to, the realization of a system and a method for alerting and attending to adverse events that:

[0224] • ensures the well-being and safety of the members of such events;

[0225] • works to save the lives of the members;

[0226] • real-time emergency services;

[0227] • ensures the well-being and safety of the member;

[0228] • accurately identifies anonymous users who have been in severe accidents and medical emergencies through uniquely generated digital codes;

[0229] • secure authentication process for identifying individuals involved in serious accidents and medical emergencies;

[0230] • ensures user identification in the absence of manual verification; and

[0231] • facilitates automated communications to all members of the emergency response team.

[0232] The embodiments herein and the various features and advantageous details thereof are explained with reference to the non-limiting embodiments in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.

[0233] The foregoing description of the specific embodiments so fully reveals the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and / or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.

[0234] The use of the expression “at least” or “at least one” suggests the use of one or more elements or ingredients or quantities, as the use may be in the embodiment of the disclosure to achieve one or more of the desired objects or results.

[0235] While considerable emphasis has been placed herein on the components and component parts of the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiment as well as other embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.

Claims

CLAIMS:

1. A system (100) for alerting and attending to adverse events, said system (100) comprising:• a centralized server (102) configured to communicate with an application platform (104) comprising a set of artificial intelligence- bots (Al-bots) to handle at least one emergency protocol over an Internet of Things (loT) environment (106), wherein said centralized server (102) comprises: o a data collection module (108) configured to collect and store vehicle details, member personal details, registration details, pre-uploaded images of members in different projections, live positioning data of the vehicle and member devices, a set of authentication rules, a pre-trained Al model, and a predefined emergency protocol, o a registration module (110) configured to register a new member and generate a unique digital code for each registered member; o an emergency detection module (112) configured to determine and trigger the nature of the emergency in real-time by means of a computing device (114) of the member or detect and identify said nature of the emergency by a third party member by scanning said unique digital code or capturing real-time image, and further configured to trigger and categorize said nature of the emergency; o an identification module (116) configured to identify and authenticate the member identity after the occurrence of said nature of the emergency by means of said set of authentication rules or by means of said pre-trained Al model, and further configured to generate at least one alert notification; and o a notification module (118) configured to cooperate with said emergency detection module (112) and said identification module (116) to receive said categorized nature of the emergency and said alertnotification, and further configured to initialize said set of Al-bots to communicate with at least one emergency service, emergency responders and authorities to attend said event according to said at least one emergency protocol.

2. The system (100) claim as claimed in claim 1, wherein said system (100) comprises a wearable device (120) configured to cooperate with said application platform (104) to receive blood pressure, pulse rates and ECGs, oxygen saturation, and other information gathered by means of a plurality of sensors (120a) of the wearable device (120).

3. The system (100) claim as claimed in claim 1, wherein said system (100) comprises an encryption module (122) is configured to encrypt said vehicle details, member personal details, and registration details to prevent unwanted data leaks.

4. The system (100) claim as claimed in claim 1, wherein said unique digital code is embedded with an intuitive text mentioning the use of said unique digital code to the third parties, wherein said unique digital code is placed on wallets, vehicle windshields, motorbike helmets, and phone cases, or any visible location of the vehicle.

5. The system (100) claim as claimed in claim 1, wherein said nature of the emergency includes accidents, medical emergencies, vehicle failure, intersection collisions, fire accidents, crash injuries, flood, and women's safety.

6. The system (100) as claimed in claim 1, wherein said nature of the emergency is triggered by means of a computing device (114) of the member or pressing the emergency button, or voice command or other means of activating trigger event.

7. The system (100) as claimed in claim 1, wherein said emergency protocol is selected from a group of protocols consisting of medical services, fire services, women's safety services, transport services, fire services, ambulance services, and petroleum services.

8. The system (100) as claimed in claim 1, wherein said third party members include formal team members, informal team members, family members, friends, passed by vehicle members, or any person.

9. The system (100) as claimed in claim 1, wherein said emergency event is triggered by using mobile applications, wearable devices, or phone call (backend ticket generation), or any computing device capable of detecting said nature of the emergency, or by scanning said unique digital code manually or by using of artificial intelligence intervention or any device capable of scanning said unique digital code.

10. The system (100) as claimed in claim 1, wherein said set of authentication rules is a set of instructions used to identify and authenticate the member by three-factor authentication and select a correct facial image from verified pre -uploaded images of members in different projections along with multiple Al-generated false images, and further configured to identify and confirm a unique identification mark and unique physical features on the member body which includes a scar, birthmark, tattoo or any identical mark.

11. The system (100) as claimed in claim 1, wherein said system (100) further comprises a face reconstruction module (124) configured to capture an image of the member after an accident and perform cross-referencing with the said pre -uploaded face images of the members by means of said pre-trained Al model.

12. The system (100) as claimed in claim 1, wherein said pre-trained Al model is configured to perform a facial reconstruction and recognition to confirm the identity of the member after an accident and further determine the degree of confidence so that at least one emergency protocol for emergency response, handling, and communications can be invoked.

13. The system (100) as claimed in claim 12, wherein said facial reconstruction is performed by:• receiving captured image;• obtaining three-dimensional facial structure for depth information;• enhancing the quality of said image by noise reduction, resolution enhancement, and image alignment / orientation;selecting and loading at least one pre-trained model such as VGG-Face, or FaceNet for feature extraction;• detecting key facial landmarks such as eyes, nose, mouth, and chin;• creating a three-dimensional mesh of the face map of said key features; and• reconstructing and visualizing said facial image.

14. The system (100) as claimed in claim 12, wherein said degree of confidence provides the matching identification in percentage, where the highest degree of confidence is 100% (High Match), and the lowest degree of confidence is 10% (Low match).

15. The system (100) as claimed in claim 1, wherein said alert notification includes the live location of the emergency event, recorded audio clips, recorded video feeds, phone event and distress signals which include crash and fall detection, and geofencing alerts.

16. The system (100) as claimed in claim 1, wherein said set of Al-bots includes a first AI-Bot, a second AI-Bot, and a third AI-Bot, wherein said first AI-Bot, said second AI-Bot and said third AI-Bot are in communication with each other to provide feedback.

17. The system (100) as claimed in claim 16, wherein said second AI-Bot is configured to:• determine the triggered nature of the emergency event and identify the member and third party (people) closest to the member;• identify the triggered nature of the emergency and update said first AI-Bot; and• dispatch nearest at least one emergency service, said emergency responders and said authorities to assess said one emergency service, said emergency responders and said authorities when said second AI-Bot unable to identify said triggered emergency event.

18. The system (100) as claimed in claim 16, wherein said third AI-Bot is configured to inform formal and informal team members of the triggered emergency event or urge them to join a video call.

19. The system (100) as claimed in claim 16, wherein said first AI-Bot is configured to:• update the first AI-Bot based on the feedback received by said third AI-Bot and said third AI-Bot;• connect and join video calls and familiarize with the real-time situation and alert notifications which include vital emergency information, including medical profiles, KYC details, a user’s live location, audio clips, and other critical data, to emergency authorities in real-time;• wait for team members to arrive at the location;• communicate with Hospital coordination to direct the first team member to visit a hospital and brief the on-call doctors,• communicate with location coordination to direct the second team member to visit the location and perform first aid,• communicate with ambulance coordination to direct the third team member to stay on the line with the ambulance driver until reaches to location,• communicate with doctor consultant coordination to direct the fourth team member to communicate with a doctor over a video call and provide verbal consent.

20. A method (200) for alerting and attending to adverse events, said method (200) comprises the following steps: communicating, by a centralized server (102) with an application platform (104) comprising a set of artificial intelligence-bots (Al-bots) to handle at least one emergency protocol over an loT environment (106);• registering, by a registration module (110), a new member and generating a unique digital code for each registered member;• determining and triggering, by an emergency detection module (112), the nature of the emergency in real-time by means of a computing device (114) of the member or detecting and identifying said nature of the emergency by a third party member by scanning said unique digital code and categorizing said triggered nature of the emergency;• identifying and authenticating, by an identification module (116), the member identity after the occurrence of said nature of the emergency by means of said set of authentication rules or by means of said pre-trained Al model and generating at least one alert notification; and• receiving, by a notification module (118), said categorized the nature of the emergency and said alert notification and initializing said set of Al-bots to communicate with at least one emergency service, emergency responders, and authorities to attend said event according to said at least one emergency protocol.

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