An artificial intelligence (AI)-based chatbot emergency system

Abstract

An AI-powered, chatbot-based emergency response system consisting of: a user interface module comprising an input module and a chatbot component connected to the input module, the user interface module configured to receive emergency reports from a user via a plurality of input means, including audio input, text input, image input, and widget-based input, the user interface module connected to a user computing device having a display; a storage unit configured to temporarily store the user inputs and permanently store the emergency contacts specified by the user during user registration performed via the user interface module; an input processing module connected to the user interface module and configured to convert multimodal inputs into text representations and preprocess the converted text representation, creating a transcript of the user input image by implementing generative AI models and processing audio inputs using a speech-to-text module to create transcripts; a classification module connected to the input processing module and configured to determine and classify the type of emergency based on the text representation obtained from the user input, wherein the classification module is configured to implement one or more ensemble classifiers to classify the emergency into one of several predefined categories including fire emergency, accident emergency, criminal emergency, gender violence emergency, and medical emergency; an emergency contact notification module connected to the storage unit and configured to retrieve pre-registered emergency contacts of the user and transmit information, including the current location, the nature of the emergency, and user inputs, to the emergency contacts; a workflow determination module connected to the classification module and configured to forward the alarm to the appropriate emergency agencies based on the classified emergency type, the workflow determination module comprising: a severity assessment module connected to the input processing module and the classification module and configured to assess the severity of the emergency based on its type and textual representation, wherein the severity assessment module comprises a scoring mechanism configured to assign a severity index to the emergency based on factors such as the number of people involved, the age of the people, and the affected body areas; and a resource allocation module connected to the severity assessment module and configured to prioritize concurrent emergency requests based on the severity index value and optimize dispatch by selecting the most appropriate emergency service provider based on proximity and availability, allocating emergency services according to the severity index value and the selection of the appropriate emergency agency for the emergency type; a real-time tracking module connected to the resource allocation module and capable of tracking the status of the assigned emergency service in real time, wherein the real-time tracking module is connected to the user interface module and enables the user to track the real-time location of the emergency vehicle and the status of the emergency service; and a first aid guidance module connected to the user interface module and configured to provide the user with interactive first aid guidance and tips while waiting for the arrival of emergency services.

Description

FIELD OF THE INVENTION

[0001] The present disclosure relates to an AI-enabled, chatbot-based emergency response system. More specifically, the present invention relates to an AI-enabled system having various modules that enable the user to connect to the system via a chatbot. The user can provide inputs such as images, videos, audio files, and text messages via the chatbot. Upon receiving the input request, the system activates an emergency response based on the input to enable an effective and rapid response to emergency situations. The system is configured to classify emergencies and deploy appropriate resources such as ambulances, hospital beds, or fire engines using intelligent allocation modules. BACKGROUND OF THE INVENTION

[0002] The current emergency response system has several drawbacks. The existing response system lacks communication and correlation between users and response actions.

[0003] Conventional emergency call systems have significant problems, such as delayed response times, difficulty locating nearby emergency services, and a lack of immediate first aid instructions for those in need. In many emergency situations, the time required to contact first responders and receive important instructions can significantly impact the outcome, particularly in medical emergencies, accidents, and life-threatening incidents. Furthermore, many people are unaware of the immediate first aid measures they can take while waiting for professional assistance, leading to avoidable complications.

[0004] The previous discussion clearly demonstrates the need for an effective emergency response system that effectively addresses the key problems of existing emergency response systems. Therefore, an AI-enabled, chatbot-based emergency response system is proposed that simplifies and accelerates the dispatch of emergency responders. The system minimizes response time, provides real-time first aid guidance, and automatically identifies and notifies support contacts. The system is intended to solve the problems of conventional emergency response systems. Summary of the invention

[0005] This disclosure relates to an AI-enabled, chatbot-based emergency response system called Rakshak. This invention is an AI-enabled, chatbot-based emergency response system that optimizes emergency reporting, assessment, and response through multiple interconnected modules. The system accepts various input formats (text, audio, images), classifies emergencies, prioritizes them by severity, notifies appropriate authorities and emergency contacts, allocates appropriate resources, enables real-time tracking, and provides first aid guidance while waiting for assistance.

[0006] An objective of the present disclosure is to provide an AI-assisted, chatbot-based emergency response system. The system comprises: a user interface module having an input module and a chatbot component connected to the input module, wherein the user interface module is configured to receive emergency messages from a user via multiple input methods, including audio, text, image, and widget-based inputs. The user interface module is connected to a user computer having a display; a storage unit configured to temporarily store the user inputs and permanently store the emergency contacts specified by the user during user registration performed via the user interface module; an input processing module connected to the user interface module and configured tothat it converts multimodal inputs into text representations and pre-processes the converted text representation, wherein a transcript of the user input image is created by implementing generative AI models and audio inputs are processed using a speech-to-text module to create transcripts; a classification module connected to the input processing module and configured to determine and classify the type of emergency based on the text representation obtained from the user input, wherein the classification module is configured to implement one or more ensemble classifiers to classify the emergency into one of several predefined categories including fire emergency, accident emergency, criminal emergency, violence against women emergency, and medical emergency; an emergency contact notification module connected to the storage unit and configured tothat it retrieves the user's pre-registered emergency contacts and transmits information, including the current location, the type of emergency, and the user inputs, to the emergency contacts; a workflow determination module connected to the classification module and configured to transmit the alarm to the appropriate emergency agencies based on the classified emergency type, wherein the workflow determination module comprises: a severity assessment module connected to the input processing module and the classification module and configured to assess the severity of the emergency based on its type and textual representation, wherein the severity assessment module comprises an assessment mechanism configured to assign a severity index to the emergency based on factors such as the number of people involved, the age of the people, and the affected body areas,and a resource allocation module connected to the severity assessment module, configured to prioritize concurrent emergency requests based on the severity index value and optimize dispatch by selecting the most appropriate emergency service provider based on proximity and availability, wherein emergency services are allocated according to the severity index value and the selection of the appropriate emergency agency for the emergency type; a real-time tracking module connected to the resource allocation module, configured to track the status of the assigned emergency service in real time, wherein the real-time tracking module is connected to the user interface module and allows the user to track the real-time location of the dispatched emergency vehicle and the status of the emergency service; and a first aid guidance module connected to the user interface module and configured tothat it provides the user with interactive first aid instructions and tips while waiting for emergency services to arrive.

[0007] Another object of the present disclosure is to provide an AI-enabled chatbot-based emergency response system.

[0008] Another objective of the present disclosure is to minimize response time in emergency situations through efficient multimodal input processing and automatic classification of emergency types.

[0009] Another objective of this disclosure is to optimize resource allocation by assessing the severity of the emergency and intelligently allocating available emergency service providers based on proximity and availability.

[0010] Another objective of this disclosure is to improve user support during critical waiting times by providing interactive first aid instructions tailored to specific emergency situations.

[0011] Another objective of this disclosure is to improve emergency communication by automatically notifying pre-registered emergency contacts with relevant information about the emergency situation.

[0012] To further clarify the advantages and features of the present disclosure, the invention will be explained in more detail with reference to specific embodiments illustrated in the accompanying drawings. These drawings illustrate only typical embodiments of the invention and are therefore not to be considered as limiting its scope. The invention will be described and explained in more detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE CHARACTERS

[0013] These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings, in which like characters represent like parts throughout. Fig. 1 shows a block diagram of an AI-assisted, chatbot-based emergency response system according to an embodiment of the present disclosure; Fig. 2 is a diagram illustrating the architecture of the proposed system according to an embodiment of the present disclosure; Fig. 3 is a diagram illustrating the architecture of the workflow determination module according to an embodiment of the present disclosure; and Fig. 4 illustrates a diagram showing the architecture of the severity assessment module according to an embodiment of the present disclosure.

[0014] Those skilled in the art will also appreciate that the elements in the drawings are shown for convenience and are not necessarily to scale. For example, the flowcharts illustrate the method by key steps to enhance understanding of aspects of the present disclosure. Furthermore, with respect to device construction, one or more components of the device may be represented in the drawings by conventional symbols. The drawings may show only the specific details relevant to understanding embodiments of the present disclosure in order not to clutter the drawings with details that would be readily apparent to those skilled in the art from the present description. DETAILED DESCRIPTION:

[0015] To facilitate understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and will be clearly described. However, the scope of the invention is not limited thereby. Changes and further modifications to the illustrated system, as well as further applications of the principles of the invention, are possible, as would normally occur to one skilled in the art to which the invention pertains.

[0016] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be limiting thereof.

[0017] References in this specification to "one aspect," "another aspect," or similar language mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, the language "in one embodiment," "in another embodiment," and similar language throughout this specification may or may not refer to the same embodiment.

[0018] The terms "comprises," "comprising," or other variations thereof are intended to cover non-exclusive inclusion, such that a process or method comprising a list of steps may include not only those steps, but also additional steps not expressly listed or inherent in that process or method. Likewise, the statement "comprises" for one or more devices, subsystems, elements, structures, or components does not exclude, without further limitation, the existence of other devices, subsystems, elements, structures, components, or additional devices, subsystems, elements, structures, or components.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention pertains. The systems, methods, and examples provided herein are for illustrative purposes only and should not be considered limiting.

[0020] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0021] The functional units described in this specification are referred to as devices. A device may be implemented in programmable hardware devices such as processors, digital signal processors, central processing units, field-programmable gate arrays, programmable array logic systems, programmable logic devices, cloud processing systems, or the like. The devices may also be implemented in software for execution by various types of processors. An identified device may contain executable code and may consist, for example, of one or more physical or logical blocks of computer instructions, which may be organized, for example, as an object, procedure, function, or other construct.However, the executable file of an identified device does not have to be physically stored in the same location, but may consist of different instructions stored in different locations which, logically linked, form the device and fulfill its purpose.

[0022] The executable code of a device or module can consist of one or more instructions and can even be distributed across multiple code segments, different applications, and multiple storage devices. Likewise, operational data can be identified and represented within the device and presented in any form and data structure. The operational data can be captured as a single data set or distributed across different storage devices and can be represented, at least in part, as electronic signals in a system or network.

[0023] References in this specification to "a selected embodiment," "an embodiment," or "an embodiment" mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosed subject matter. Therefore, the phrases "a selected embodiment," "in an embodiment," or "in an embodiment" in various places in this specification do not necessarily refer to the same embodiment.

[0024] Furthermore, the described features, structures, or characteristics may be combined in any manner in one or more embodiments. The following description contains numerous specific details to provide a thorough understanding of embodiments of the disclosed subject matter. However, those skilled in the art will recognize that the disclosed subject matter may be practiced without one or more of the specific details, or with different methods, components, materials, etc. In other instances, well-known structures, materials, or operations are not shown or described in detail in order not to obscure aspects of the disclosed subject matter.

[0025] According to the exemplary embodiments, the disclosed computer programs or modules may be executed in a variety of ways, for example, as an application in the memory of a device or as a hosted application on a server that communicates with the device application or browser using various standard protocols such as TCP / IP, HTTP, XML, SOAP, REST, JSON, and other suitable protocols. The disclosed computer programs may be written in exemplary programming languages ​​that execute from the memory of the device or from a hosted server, such as BASIC, COBOL, C, C++, Java, Pascal, or scripting languages ​​such as JavaScript, Python, Ruby, PHP, Perl, or other suitable programming languages.

[0026] Some of the disclosed embodiments involve or otherwise involve the transmission of data over a network, for example, the delivery of various inputs or files over the network. The network may include, for example, the Internet, wide area networks (WANs), local area networks (LANs), analog or digital wired and wireless telephone networks (e.g., PSTN, Integrated Services Digital Network (ISDN), cellular networks, and Digital Subscriber Line (xDSL)), radio, television, cable, satellite, and / or other transmission or tunneling mechanisms for transmitting data. The network may include multiple networks or subnetworks, each containing, for example, a wired or wireless data path. The network may include a circuit-switched voice network, a packet-switched data network, or another network for transmitting electronic communications.For example, the network may include Internet Protocol (IP) or Asynchronous Transfer Mode (ATM) networks that support voice, such as VoIP, Voice over ATM, or other comparable protocols for voice data communication. In one implementation, the network includes a cellular network configured for the exchange of text or SMS messages.

[0027] Examples of the network include a Personal Area Network (PAN), a Storage Area Network (SAN), a Home Area Network (HAN), a Campus Area Network (CAN), a Local Area Network (LAN), a Wide Area Network (WAN), a Metropolitan Area Network (MAN), a Virtual Private Network (VPN), an Enterprise Private Network (EPN), the Internet, a Global Area Network (GAN), etc.

[0028] Fig. 1 shows a block diagram of an AI-assisted, chatbot-based emergency response system (100) according to an embodiment of the present disclosure.

[0029] According to Fig. 1, the system (100) comprises: a user interface module (102) comprising an input module (104) and a chatbot component (106) connected to the input module (104), wherein the user interface module (102) is configured to receive emergency reports from a user via multiple input means, including audio input, text input, image input, and widget-based input, wherein the user interface module is connected to a user's computing device with a display; a storage unit (108) configured to temporarily store the user inputs and to permanently store emergency contacts specified by the user during user registration performed via the user interface module; an input processing module (110) connected to the user interface module (102) and configured to convert multimodal inputs into text representations and preprocess the converted text representation,wherein a transcript of the user input image is generated by implementing generative AI models, and audio inputs are processed using a speech-to-text module to generate transcripts; a classification module (112) connected to the input processing module (110) and configured to determine and classify the type of emergency based on the text representation obtained from the user input, wherein the classification module (112) is configured to implement one or more ensemble classifiers to classify the emergency into one of several predefined categories including fire emergency, accident emergency, criminal emergency, violence against women emergency, and medical emergency; an emergency contact notification module (114) connected to the storage unit (108) and configured to retrieve pre-registered emergency contacts of the user and to provide information,including the current location, the type of emergency, and user inputs, to the emergency contacts; a workflow determination module (116) connected to the classification module (112) and configured to forward the alarm to the appropriate emergency agencies based on the classified emergency type, the workflow determination module (116) comprising: a severity assessment module (116a) connected to the input processing module (110) and the classification module (112) and configured to assess the severity of the emergency based on its type and textual representation, the severity assessment module (116a) comprising a rating mechanism configured to assign a severity index to the emergency based on factors such as the number of people involved, the age of the people, and the affected body areas, and a resource allocation module (116b),which is connected to the severity assessment module (116a) and configured to prioritize concurrent emergency requests based on the severity index value and optimize dispatch by selecting the most appropriate emergency service provider based on proximity and availability, wherein the emergency services are assigned according to the severity index value and the selection of the appropriate emergency agency for the emergency type; a real-time tracking module (118) connected to the resource allocation module (116b) and configured to track the status of the assigned emergency service in real time, wherein the real-time tracking module (118) is connected to the user interface module (102) so that the user can track the real-time location of the dispatching emergency vehicle and the status of the emergency service; and a first aid guidance module (120) connected to the user interface module (102) and configured toprovide the user with interactive first aid instructions and tips while waiting for emergency services to arrive.

[0030] In one embodiment, the input module (104) is configured to enable the user to report incidents by providing multimodal inputs, wherein the chatbot component (106) is connected to the input module (104) to receive the user inputs, wherein the chatbot component (106) facilitates understanding of interactions with the user in natural language, including English and Hindi, and also guides the user through the emergency reporting process, wherein the chatbot component (106) also facilitates interactive first aid guidance, and wherein all details provided by the user are temporarily stored in the storage unit (108).

[0031] In one embodiment, the input processing module (110) further comprises: a text preprocessing component configured to remove noise from the text representations, normalize text by removing special characters and converting to lowercase, remove stop words, and extract keywords relevant to emergency situations.

[0032] In one embodiment, the classification module (112) further comprises: an ensemble learning component configured to: use multiple machine learning classifiers to analyze emergency inputs; weight the outputs of individual classifiers based on their historical accuracy; and generate a final emergency classification based on the weighted outputs.

[0033] In one embodiment, the workflow determination module (116) is configured to assign police and emergency services for criminal emergencies, to assign women's hotlines, police and emergency services for violent emergencies, to assign police and emergency services for accident emergencies, to assign fire stations and emergency services for fire emergencies, and to assign emergency services for medical emergencies.

[0034] In one embodiment, the severity assessment module (116a) further comprises: a machine learning component trained on historical emergency data to calculate the severity index based on text analysis of the emergency description, image analysis (if available), contextual factors such as time of day and location characteristics, and demographic information of the affected individuals (if available).

[0035] In one embodiment, the resource allocation module (116b) further comprises: a dynamic routing component configured to: continuously update the routes of the emergency service providers based on traffic conditions in real time; and maintain a backup list of alternative service providers in case the primary providers are unavailable.

[0036] In one embodiment, the real-time tracking module (118) further comprises: a geolocation component configured to: triangulate the user's position using multiple data sources, including GPS, cell tower data, and Wi-Fi signals; provide accurate location information even in areas with limited connectivity; and update the user's location in real time as the user moves.

[0037] In one embodiment, the first aid instruction module (120) is configured to: provide interactive visual demonstrations of first aid procedures using images and animated GIFs on the display of the user's computing device via the user interface module (102); customize first aid instructions based on the specific nature and severity of the emergency; provide step-by-step CPR instructions with time cues when appropriate; and customize instructions based on user feedback regarding the victim's condition.

[0038] In one embodiment, the user interface module (102), the input module (104), the chatbot component (106), the storage unit (108), the input processing module (110), the classification module (112), the emergency contact notification module (114), the workflow determination module (116), the real-time tracking module (118), and the first aid guidance module (120) may be implemented in programmable hardware devices such as processors, digital signal processors, central processing units, field-programmable gate arrays, programmable array logic, programmable logic devices, cloud processing systems, or the like.

[0039] The present invention relates to an AI-enabled, chatbot-based emergency response system consisting of multiple interconnected modules that work together to address the limitations of conventional emergency management systems.

[0040] The user interface module serves as the primary interaction point and allows users to report emergencies through multiple input methods, including audio, text, images, and a mobile application with a one-click widget for expedited reporting. This module includes a chatbot component that understands multiple languages, enables natural language interactions, and guides users through the emergency reporting process. The input processing module handles the conversion of multimodal inputs into text representations. It uses generative AI models to process image inputs and generate subtitles, while audio inputs are converted to text using speech-to-text technology. After conversion, the system preprocesses the text by removing noise, normalizing content, eliminating stop words, and extracting emergency-relevant keywords.To protect user data, the system deletes all user-uploaded images immediately after caption generation. The classification module analyzes the processed text representations to determine the type of emergency. Using ensemble learning, which combines multiple machine learning classifiers, the system categorizes emergencies into predefined categories such as fire, accident, crime, violence against women, and medical emergencies. The results of each classifier are weighted according to their historical accuracy to create a final classification, which forms the basis for subsequent response measures. Based on the emergency classification, the workflow determination module creates a customized workflow for the entities to be alerted. For example, criminal emergencies trigger notifications to the police and emergency services, while fire emergencies alert both the fire department and emergency services.The emergency contact notification module simultaneously retrieves the user's pre-registered emergency contacts and transmits important information such as current location, emergency type, and relevant user inputs to these contacts. The severity assessment module assigns each emergency a severity index based on factors such as the number of people involved, age, affected body parts, and other contextual information. This module uses machine learning components trained on historical emergency data to calculate this index, which is crucial for prioritizing concurrent emergency requests. The resource allocation module then identifies and assigns the most appropriate emergency service providers based on proximity, availability, and the determined severity index.It includes a dynamic routing component that continuously updates routes based on real-time traffic conditions and can reallocate resources when higher-severity emergencies are reported. While emergency services are en route, the real-time tracking module provides users with up-to-date location information of emergency vehicles. This module includes geolocation capabilities that triangulate the user's position using multiple data sources to ensure accurate location information even in areas with limited connectivity. Meanwhile, the first aid guidance module provides interactive visual demonstrations and step-by-step instructions tailored to the specific emergency type, helping users provide critical assistance while waiting. These instructions are adjusted based on user feedback on the victim's condition to ensure relevance and effectiveness.

[0041] Fig. 2 is a diagram showing the architecture of the proposed system according to an embodiment of the present disclosure.

[0042] Fig. Figure 2 shows an AI-powered, chatbot-based emergency response system that simplifies and accelerates the dispatch of emergency personnel. The system minimizes response time, provides real-time first aid guidance, and automatically identifies and notifies support contacts. The system is designed to solve the problems of traditional emergency response.

[0043] The proposed system aims to improve response efficiency and avoid delays in vehicle dispatch and immediate action in emergency situations. This addresses the shortcomings of conventional emergency dispatch systems in India. The proposed system uses a structured input-output workflow and integrates multiple modules to optimize emergency reporting, severity assessment, and resource allocation.

[0044] Fig. 3 illustrates a diagram showing the architecture of the workflow determination module according to an embodiment of the present disclosure.

[0045] Fig. 4 illustrates a diagram showing the architecture of the severity assessment module according to an embodiment of the present disclosure.

[0046] According to Fig. 2, the system architecture consists of a series of interconnected modules, beginning with a user interface module containing an input module and a chatbot component. The input module enables users to report incidents via multimodal inputs, including audio, text, image, and widget-based inputs, using a user-equipped computing device equipped with a display. The chatbot component, which understands both English and Hindi, facilitates natural language interaction and guides users through the emergency reporting process. The mobile app and widget interface provide a one-click solution for incident reporting, thus supporting quick and accessible emergency communication. The system ensures user confidentiality by securely storing image inputs in isolation for the duration of the analysis.

[0047] The input processing module connected to the user interface handles the transformation of multimodal inputs into standardized text formats for further analysis. Audio inputs are transcribed using an integrated speech recognition module, while image inputs are captioned using generative AI models. These generated transcripts and captions are then preprocessed by a text preprocessing component that removes noise, normalizes the text, filters stop words, and extracts important emergency-relevant terms. In accordance with data protection principles, all multimedia inputs are processed using temporary storage protocols and permanently deleted after transcript or caption generation is complete, leaving no residual media.

[0048] The classification module, which follows input processing, analyzes the text representations of the user input to determine and categorize the type of emergency. This module uses ensemble learning by integrating the results of multiple classifiers, each weighted based on its historical performance, to generate a robust classification result. Emergencies are classified into one of several predefined categories, including fire emergencies, accident emergencies, crime emergencies, emergencies related to violence against women, and medical emergencies.

[0049] Referring to Fig. 3 After classification, the workflow determination module triggers automatic alerts to the appropriate emergency services based on the detected emergency type. This includes assigning specific services such as police and rescue services for criminal or accident emergencies, women's hotlines for violence against women, fire stations for fire-related emergencies, and ambulances for illnesses. The workflow determination module includes a severity assessment module and a resource allocation module. At the same time, the emergency contact notification module retrieves user-specific emergency contact information previously stored in the storage unit during user registration and sends automatic alerts to the specified contacts with the current location, emergency type, and a summary of the user's multimodal inputs.This ensures that family members or trusted persons are informed immediately and can react accordingly.

[0050] As in Fig.As shown in Figure 4, the severity assessment module calculates a severity index for each emergency by evaluating contextual parameters such as the number of people involved, the age of those affected, and injury characteristics. This module can include machine learning components trained on historical emergency data to improve the accuracy of the severity assessment using text analytics, available image data, temporal factors, and location-specific characteristics. Based on the severity score, the resource allocation module prioritizes and allocates emergency services, taking into account both the severity index and the real-time availability and proximity of emergency responders. It includes a dynamic routing component that adapts to real-time traffic conditions and maintains backup service providers to ensure uninterrupted emergency response.Once emergency responders are dispatched, the real-time tracking module provides continuous updates on the location and status of the assigned emergency vehicle. This module utilizes a geolocation component to determine the user's position via GPS, Wi-Fi signals, and cell tower data, ensuring accurate positioning even in areas with poor internet connectivity. The tracking information is accessible to the user via the user interface module.

[0051] While waiting for emergency responders, the system activates the first aid module. It delivers interactive, emergency-specific visual demonstrations using animated GIFs and image sequences displayed on the user's device. The first aid instructions are contextually tailored to the type and severity of the emergency and can include timed CPR instructions or step-by-step procedures for stopping bleeding. The chatbot component facilitates these interactions and dynamically adjusts the instructions based on real-time user feedback on the victim's condition.

[0052] Designed with privacy-by-design principles, it complies with data protection standards such as the Digital Personal Data Protection (DPDP) Act. All user-provided data, including audio, image, and text content, is processed through a volatile storage mechanism and retained only for the duration of classification and delivery. Upon confirmation of service assignment, all such data is automatically and securely deleted. This ensures that no personal data is retained beyond the period necessary for operational purposes. This volatile data lifecycle underscores the system's commitment to secure, responsible, and compliant data handling.

[0053] The proposed system, called Rakshak, is an AI-powered, chatbot-based emergency response solution with a dual architecture consisting of a user interface and an emergency management engine on the backend side, working in synergy to enable quick and intelligent responses to emergency scenarios.

[0054] The user interface was strategically developed through integration with popular messaging platforms such as WhatsApp and Messenger. This allows users to report emergencies multimodally via text messages, voice notes, and images. This design intentionally avoids reliance on standalone mobile applications, as users may not have the time or inclination to download additional software in crisis situations. The chatbot component supports natural language communication in English and Hindi and guides users through the emergency reporting process while enabling interactive first aid measures. The backend forms a scalable, AI-powered emergency architecture with an intelligent resource allocation module. This module processes emergency data in real time and dynamically determines the optimal deployment of resources such as ambulances, fire engines, and hospital beds.It analyzes the type and severity of the emergency in conjunction with the availability and proximity of emergency personnel. The allocation strategy is supported by a severity assessment module that calculates a severity index based on factors such as the number and age of affected individuals, affected body regions, and current location characteristics. In parallel, the system features an automated emergency contact notification module that promptly informs pre-registered contacts of the user's real-time location, the type of emergency, and relevant incident data. This ensures that close contacts are aware and can respond accordingly in coordination with emergency services. The proposed system also integrates an AI-driven first aid module that provides users with context-specific medical instructions in real time while emergency services are en route. This includes interactive visual demonstrations, such as:Instructions for cardiopulmonary resuscitation or hemostasis are tailored to the identified emergency type and severity and delivered via the user interface to ensure easy understanding and implementation. The proposed system is a comprehensive, robust, and user-centric emergency solution that leverages multimodal AI, dynamic resource optimization, and real-time communication to improve emergency preparedness, response efficiency, and user safety in critical situations.

[0055] The drawings and the foregoing description illustrate examples of embodiments. Those skilled in the art will recognize that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be separated into multiple functional elements. Elements of one embodiment may be added to another embodiment. For example, the order of the processes described herein may be changed and is not limited to the manner described herein. Furthermore, the actions of a flowchart need not be performed in the order shown; nor do all actions need to be performed. Also, actions that are not dependent on other actions may be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples.Numerous variations, whether explicitly stated in the specification or not, such as differences in structure, dimensions, and use of materials, are possible. The scope of the embodiments is at least as broad as indicated in the following claims.

[0056] Advantages, further benefits, and solutions to problems have been described above with reference to specific embodiments. However, the advantages, advantages, solutions to problems, and any components that may result in or enhance an advantage, advantage, or solution are not to be construed as critical, required, or essential features or components of any or all of the claims. REFERENCES 100 An AI-powered chatbot-based emergency response system. 102 User Interface Module 104 Input module 106 Chatbot component 108 storage unit 110 Input processing module 112 Classification module 114 Emergency contact notification module 116 Workflow Determination Module 116a Severity Assessment Module 116b Resource allocation module 118 Real-time tracking module 120 First Aid Guide Module 202 User emergency 202a Entrance 204 Voice recording for emergencies 206 Picture of the emergency scene 208 emergency SMS 210 Emergency request via widget 212 Text conversion and preprocessing 214 Categorization of the emergency 216 SOS to emergency contact 218 Individual workflow depending on the type of emergency 220 Emergency Resource Allocation 222 Service alerted and informed 224 Live location is sent to the user for tracking 226 Instructions for emergency first aid 228 Multilingual text instructions 230 Guided Image Instructions 232 Guided Audio Instructions 302 Tailor-made workflow 304 Criminal 304a Police 304b Ambulance (case) 306 Violence against women 306a Safety Hotlines for Women 306b Police 306c Ambulance (case) 308 accidents 308a Police (normal / traffic) 308b Ambulance 310 fires 310a Fire Station 310b Ambulance 312 Medical 312a Ambulance 402 Number of people 406 Age 408 Type of emergency described 410 Affected body area 412 severity 414 Low 416 Medium 418 High 420 nearby ambulances and hospitals sorted by distance and traffic 422 Number of ambulances > minimum threshold 424 hospital / ambulance assigned 426 Number of ambulances > minimum threshold 428 Minimum threshold for ambulances is half the capacity

Claims

[1] An AI-powered, chatbot-based emergency response system consisting of: a user interface module comprising an input module and a chatbot component connected to the input module, the user interface module configured to receive emergency reports from a user via a plurality of input means, including audio input, text input, image input, and widget-based input, the user interface module connected to a user computing device having a display; a storage unit configured to temporarily store the user inputs and permanently store the emergency contacts specified by the user during user registration performed via the user interface module; an input processing module connected to the user interface module and configured to convert multimodal inputs into text representations and preprocess the converted text representation, creating a transcript of the user input image by implementing generative AI models and processing audio inputs using a speech-to-text module to create transcripts; a classification module connected to the input processing module and configured to determine and classify the type of emergency based on the text representation obtained from the user input, wherein the classification module is configured to implement one or more ensemble classifiers to classify the emergency into one of several predefined categories including fire emergency, accident emergency, criminal emergency, gender violence emergency, and medical emergency; an emergency contact notification module connected to the storage unit and configured to retrieve pre-registered emergency contacts of the user and transmit information, including the current location, the nature of the emergency, and user inputs, to the emergency contacts; a workflow determination module connected to the classification module and configured to forward the alarm to the appropriate emergency agencies based on the classified emergency type, the workflow determination module comprising: a severity assessment module connected to the input processing module and the classification module and configured to assess the severity of the emergency based on its type and textual representation, wherein the severity assessment module comprises a scoring mechanism configured to assign a severity index to the emergency based on factors such as the number of people involved, the age of the people, and the affected body areas; and a resource allocation module connected to the severity assessment module and configured to prioritize concurrent emergency requests based on the severity index value and optimize dispatch by selecting the most appropriate emergency service provider based on proximity and availability, allocating emergency services according to the severity index value and the selection of the appropriate emergency agency for the emergency type; a real-time tracking module connected to the resource allocation module and capable of tracking the status of the assigned emergency service in real time, wherein the real-time tracking module is connected to the user interface module and enables the user to track the real-time location of the emergency vehicle and the status of the emergency service; and a first aid guidance module connected to the user interface module and configured to provide the user with interactive first aid guidance and tips while waiting for the arrival of emergency services. [2] The system according to claim 1, wherein the input module is configured to enable the user to report incidents by providing multimodal inputs, wherein the chatbot component is connected to the input module to receive the user inputs, wherein the chatbot component facilitates understanding of interactions with the user in natural language, including English and Hindi, and also guides the user through the emergency reporting process, wherein the chatbot component also facilitates interactive first aid guidance, and wherein all the details provided by the user are temporarily stored in the storage unit. [3] The system of claim 1, wherein the input processing module further comprises: a text preprocessing component configured to remove noise from the text representations, normalize text by removing special characters and converting to lowercase, remove stop words, and extract keywords relevant to emergency situations. [4] The system of claim 1, wherein the classification module further comprises: an ensemble learning component configured to: use multiple machine learning classifiers to analyze emergency inputs; weight the outputs of individual classifiers based on their historical accuracy; and to generate a final emergency classification based on weighted expenditures. [5] The system of claim 1, wherein the workflow determination module is configured to assign police and emergency services in criminal emergencies, assign women's hotlines, police and emergency services in violence against women, assign police and emergency services in accident emergencies, assign fire stations and emergency services in fire emergencies, and assign emergency services in medical emergencies. [6] The system of claim 1, wherein the severity assessment module further comprises: a machine learning component trained on historical emergency data to calculate the severity index based on: text analysis of the emergency description; image analysis (where available); contextual factors such as time of day and location characteristics; and demographic information of the affected individuals (where available). [7] The system of claim 1, wherein the resource allocation module further comprises: a dynamic routing component configured to continuously update the routes of the emergency service providers based on traffic conditions in real time and to maintain a backup list of alternative service providers in case the primary providers are unavailable. [8] The system of claim 1, wherein the real-time tracking module further comprises: a geolocation component configured to: triangulate the user's position using multiple data sources, including GPS, cell tower data, and Wi-Fi signals; provide accurate location information even in areas with limited connectivity; and update the user's location in real time as the user moves. [9] The system of claim 1, wherein the first aid instruction module is configured to provide, on the display of the user's computing device via a user interface module, interactive visual demonstrations of first aid procedures using images and animated GIFs; customize first aid instructions based on the specific nature and severity of the emergency; provide step-by-step CPR instructions with time cues when appropriate; and customize instructions based on user feedback regarding the victim's condition.

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