System and method for internet-based emergency reporting via authenticated messaging platforms and web-based interfaces with ai-assisted multi-modal incident validation

Abstract

The present disclosure relates to a system for internet-based emergency reporting, comprising: an internet-based communication channel integration layer configured to receive emergency report data from a reporter through a plurality of authenticated internet-based communication channels; a computer-implemented multi-modal incident validation engine communicatively coupled to the integration layer and configured to perform real-time multi-modal validation and analysis on the report data to generate validation intelligence; an emergency dispatch integration pipeline configured to generate a structured emergency incident report based on the validation intelligence and to transmit the report to a dispatcher or emergency dispatch system; and a bidirectional communication maintenance module configured to maintain persistent bidirectional communication between the reporter and the dispatcher. A computer-implemented method for internet-based emergency reporting is also disclosed.

Description

[0001] System and Method for Internet-Based Emergency Reporting via Authenticated Messaging Platforms and Web-Based Interfaces with AI-Assisted Multi-Modal Incident Validation

[0002] DESCRIPTION TECHNICAL FIELD

[0003] The present invention relates to the field of emergency telecommunications technologies, and more particularly to internet-based communication systems, artificial intelligence (Al) -driven multi-modal validation mechanisms, fraud detection methods, multimedia authentication protocols, location verification systems, and emergency dispatch integration technologies.

[0004] The present invention is specifically adapted for emergency reporting scenarios independent of traditional circuit- switched voice telephony networks, enabling secure, reliable, and efficient transmission of emergency information and coordinated response dispatch through any IP-based connectivity, including cellular data, WiFi, satellite internet, and other IP networks.

[0005] More specifically, the present invention relates to a computer-implemented system and method for receiving, validating, and processing emergency reports via authenticated internet-based communication channels (such as messaging platforms, web interfaces, and native applications) operating over any IP-based connectivity, utilizing artificial intelligence for multi-modal incident validation, fraud detection, multimedia evidence authentication, and automated generation of structured dispatch-ready incident reports.

[0006] BACKGROUND OF THE DISCLOSURE

[0007] Conventional emergency response systems (e.g., 911, 112) are fundamentally reliant on circuit-switched voice telephony networks, a technical architecture that presents critical vulnerabilities in modern communication environments, resulting in significant gaps in emergency response coverage. When voice networks fail due to natural disasters (earthquakes, floods, hurricanes), network congestion during mass emergencies (large-scale events, regional crises), or infrastructure damage, while

[0008] NVS002BWO / MABcellular data, WiFi, or satellite internet remain operational, traditional emergency systems are unable to receive and process emergency reports. Additionally, voice communication is impractical or dangerous in scenarios such as hostage situations (where speaking may alert captors), domestic violence incidents (where victims cannot safely vocalize), medical conditions that impair speech (e.g., strokes, throat injuries, severe asthma), and for individuals with hearing or speech disabilities (deafness, aphasia). Language barriers further limit the effectiveness of voice-based emergency systems, as real-time translation is often unavailable or delayed, and call-takers may lack proficiency in the reporter’s native language.

[0009] Existing text-based emergency solutions, such as SMS-to-911, offer only basic message forwarding functionality with no sender authentication, leaving them vulnerable to false reports and malicious abuse that waste critical emergency resources. These systems lack mechanisms for validating incident legitimacy, detecting fraud, or supporting multimedia evidence (e.g., photos, live videos, audio recordings) that are critical for assessing emergency severity, identifying hazards, and verifying incident details. Location verification is limited to basic cell tower triangulation (accuracy range of 100-1000 meters in urban areas, 1-5 kilometers in rural areas, for example), resulting in low positional accuracy that delays responder deployment to the exact incident location. No intelligent incident analysis or classification is performed, and the systems provide only unstructured raw text to dispatchers, requiring manual data extraction and processing that introduces additional delays. As such, SMS-to-911 serves merely as a backup to voice-based systems and is unsuitable for use as a primary emergency communication channel. Meanwhile, authenticated internet communication platforms, including WhatsApp, Telegram, Signal, WeChat, iMessage, and RCS, have become the primary communication method for billions of users worldwide. These platforms offer built-in identity verification (phone number validation, OAuth / SAML integration, biometric authentication), multimedia transmission capabilities (high-resolution photos, 4K live video, lossless audio), multi-network adaptability, and operate over any IP-based connectivity. Users are already familiar with their interfaces, and the platforms’ end-to-end encryption and authentication features provide a foundation for secure, private communication. However, there exists no intelligent gateway to integrate these ubiquitous internet-based channels with existing emergency dispatch infrastructure

[0010] NVS002BWO / MAB(Computer-Aided Dispatch (CAD) systems, Emergency Operations Centers (EOCs), fire / police / EMS response management systems), resulting in the underutilization of a critical communication resource for emergency response.

[0011] Further, traditional emergency dispatch workflows operate in a sequential manner: a call must be fully completed, all information manually extracted and processed, only after dispatch resource decisions are made. This sequential processing introduces significant dispatch delays, especially for high-severity incidents where every second is critical. Additionally, these systems lack a unified structured incident data flow between incident analysis components and dispatch integration components, leading to disjointed data flow, redundant processing, and inaccurate resource allocation. There is no closed-loop feedback mechanism to track response outcomes and continuously improve incident classification, resource prediction, and call-taking quality, resulting in persistent inefficiencies in emergency response operations.

[0012] A long-felt need therefore exists in the art for an intelligent emergency reporting system that leverages authenticated internet-based communication channels as primary emergency pathways, supports any IP connectivity, validates incident legitimacy through automated multi-modal analysis, proactively detects false or fraudulent reports, authenticates multimedia evidence using digital forensics and computer vision, seamlessly integrates with existing emergency dispatch systems via open APIs and standardized data formats, and maintains bidirectional communication for real-time information updates and intelligence gathering. This system must further support efficient dispatch integration by converting validated internet-based reports into structured dispatch-ready incident data and maintaining bidirectional communication for real-time updates. This system must address the technical limitations of conventional voice-dependent emergency infrastructure, inadequate text-based solutions, and fragmented dispatch-side information handling.

[0013] SUMMARY OF THE DISCLOSURE

[0014] The present invention provides a system and method for internet -based emergency reporting that overcomes the limitations of traditional voice-dependent and basic SMS-based systems and further resolves the inefficiencies of sequential dispatch processing and disjointed incident-dispatch data flow.

[0015] NVS002BWO / MABThe invention solves the technical problem of unreliable emergency communication during voice network failures or dangerous voice scenarios by establishing a primary emergency channel based on authenticated internet protocols. It further solves the problem of unverified and fraudulent emergency reports by implementing a computer-implemented multi-modal validation engine that analyzes linguistic patterns, crossvalidates location data from multiple independent sources, and authenticates multimedia evidence using digital forensics and computer vision. Additionally, the invention addresses the inefficiency of manual dispatch processing by automatically generating structured incident reports with operational recommendations based on Al analysis and, in some embodiments, providing dispatch-side integration for timely operational handling.

[0016] In one aspect, the invention comprises an intelligent emergency gateway that integrates with any internet-based communication channel, including authenticated messaging platforms, web -based interfaces, native mobile applications, API-based integrations, embedded interfaces, and loT device interfaces. All supported channels share common technical characteristics including Internet Protocol (IP) connectivity functioning via WiFi, cellular data, satellite internet, or any IP network without requiring circuit-switched voice infrastructure; digital identity verification capability utilizing platform-based authentication, multi-factor authentication, and biometric verification; multimedia transmission support for text, photos, video, audio, and location data; asynchronous communication capabilities allowing for store-and-forward functionality; and structured data capability for JSON / XML transmission and metadata inclusion.

[0017] In another aspect, the invention includes a computer-implemented multi-modal incident validation engine that performs intelligent real-time analysis. This engine conducts linguistic and semantic analysis to detect emergency language, classify incident types, analyze consistency, identify fraud linguistic patterns, and support multiple languages with automatic detection and translation. Crucially, the engine performs location intelligence and multi-source verification by collecting location data from user-shared pins, device-based GPS, WiFi positioning, cellular triangulation, IP geolocation, multimedia metadata, and visual location analysis of photos and videos to extract street signs, landmarks, and environmental clues. The system generates a

[0018] NVS002BWO / MABlocation confidence score based on the agreement between these independent sources.

[0019] In a further aspect, the invention provides multimedia evidence validation and authenticity verification. Unlike passive systems, the invention actively validates evidence through reverse image search to detect stock photos or recycled content, metadata analysis to verify timestamps and geotags, digital forensics to detect photo editing software signatures and cloning, and content analysis using object detection to identify emergency-relevant objects like fire, weapons, or injuries. For video streams, the system performs liveness detection by requesting specific user actions to verify the stream is live and not pre-recorded, alongside real-time scene understanding and audio analysis.

[0020] In yet another aspect, the invention features an integrated fraud detection algorithm that generates a real-time fraud probability score based on identity factors, linguistic factors, location factors, multimedia factors, and behavioral factors. This scoring system categorizes reports into risk levels ranging from very low risk (fast-track to dispatch) to very high risk (block and flag account), enabling the system to filter out serial false reporters and template-based fraud attempts.

[0021] In still another aspect, the invention includes an intelligent interactive validation module that actively engages the reporter to validate and gather evidence. This system dynamically generates validation questions to fill information gaps, test reporter legitimacy, assess immediate danger, and gather tactical intelligence. It requests incident-specific evidence, such as photos of flames for fire incidents or videos of breathing for medical emergencies, and conducts conversational authenticity testing by analyzing response timing, specificity, and emotional congruence.

[0022] In a further aspect, the invention comprises an emergency dispatch integration pipeline that converts unstructured messaging conversations into standardized emergency incident formats. This pipeline generates structured incident reports containing reporter information, incident classification, location intelligence, incident narratives, evidence packages, validation assessments, and operational response recommendations. The system integrates directly with Computer-Aided Dispatch (CAD) systems, Emergency Operations Centers (EOCs), and response force

[0023] NVS002BWO / MABmanagement systems via APIs, delivering validated incidents with attached multimedia evidence and maintaining a link to the live messaging channel for updates.

[0024] In yet another aspect, the invention provides a bidirectional communication maintenance module that keeps the reporter engaged as a real-time intelligence source. This system sends automated status updates to the reporter, enables dispatchers to send direct messages and request real-time updates, and tasks the reporter with intelligence gathering such as counting victims or identifying access points, effectively transforming a passive reporter into an active tactical asset.

[0025] The technical effects of the present invention include providing a resilient emergency communication channel that functions when voice networks fail; significantly reducing false emergency reports through advanced fraud detection; improving response times by automating incident classification and resource recommendation and supporting faster dispatch handling through automated validation and structured incident packaging; enhancing situational awareness for responders through authenticated multimedia evidence; enabling safe reporting in dangerous situations where voice calls are impossible; integrating seamlessly with existing emergency infrastructure without requiring replacement of legacy systems; supporting dispatch adaptation as incident details evolve; and, in some embodiments, enabling improvement of system performance using historical outcome analysis. Features and advantages of the present disclosure will be disclosed with reference to the enclosed drawings relating to an indicative and a non-limiting implementation example.

[0026] BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a block diagram illustrating the overall system architecture of an internetbased emergency reporting system according to an embodiment of the present invention.

[0027] FIG. 2 is a detailed flowchart depicting the multi-modal incident validation process performed by the system according to an embodiment of the present invention.

[0028] NVS002BWO / MABFIG. 3 is a diagram illustrating a bidirectional communication maintenance module between an emergency reporter and a dispatcher according to an embodiment of the present invention.

[0029] FIG. 4 is a flow chart illustrating a method for internet -based emergency reporting according to an embodiment of the present invention.

[0030] DETAILED DESCRIPTION

[0031] The present invention will be described in detail below with reference to specific embodiments and the accompanying drawings. These embodiments are provided to illustrate the technical principles of the invention and are not intended to limit the scope of the invention, which is defined by the appended claims.

[0032] The present invention addresses the aforementioned deficiencies in the prior art by providing an internet-based emergency reporting system and method, also referred to as the “Intelligent Emergency Gateway”, which enables end-to-end automated processing of emergency reports from reception through validation, fraud detection, dispatch integration, and bidirectional communication maintenance, and may further interface with dispatch systems to support operational handling of validated incident data. All components of the system are computer-implemented, ensuring high reliability, real-time performance, and scalability.

[0033] The system comprises five interoperable components: an internet -based communication channel integration layer, a computer-implemented multi-modal incident validation engine (incident intelligence engine), an intelligent interactive validation module, an emergency dispatch integration pipeline, and a bidirectional communication maintenance module. These components work in concert to transform unstructured internet-based emergency reports into standardized, validated structured incident data that can be seamlessly integrated with existing emergency dispatch infrastructure.

[0034] Internet-Based Communication Channel Integration Layer:

[0035] The internet -based communication channel integration layer adopts a platformagnostic architecture, supporting access to all internet-based communication

[0036] NVS002BWO / MABchannels, including but not limited to: authenticated instant messaging platforms (including, by way of non-limiting example, WhatsApp, Telegram, Signal, and other messaging services); web-based interfaces (browser-based emergency reporting forms (HTML / CSS / JavaScript), Progressive Web Applications (PWAs) with offline capabilities, responsive web portals accessible from any device, HTTPS-secured web forms with real-time validation, single-page applications, and government emergency reporting websites (e.g., emergency.gov / report)); native mobile applications (iOS native apps, Android native apps, cross-platform mobile apps, location-aware emergency apps, dedicated public safety apps, device SOS feature integrations); APIbased integrations (RESTful APIs for third-party emergency reporting systems, GraphQL endpoints for flexible data queries, webhooks for event-driven emergency notifications, WebSocket connections for real-time bidirectional communication, Server-Sent Events (SSE) for live updates); embedded interfaces (chat widgets embedded on websites, emergency buttons on web pages and applications, iFramebased emergency forms, browser extensions, smart home device integrations (e.g., Alexa, Google Home emergency skills)); and loT and smart device interfaces (wearable emergency triggers (smartwatches, medical alert devices), connected vehicle emergency systems, smart building notification systems, industrial loT safety systems) .

[0037] All supported channels share technical characteristics: IP connectivity (operating over the TCP / IP network stack, independent of circuit- switched voice networks); digital identity verification capabilities (supporting OAuth, SAML, email / phone verification, multi-factor authentication (MFA), biometric authentication (fingerprint, face ID), device fingerprinting, and account history and reputation scoring); multimedia transmission support (text messaging and structured data, photo upload (JPEG, PNG, HEIC, etc.), video upload and live streaming, audio messages and recordings, location data (GPS coordinates, address, pin), document attachments (PDF, images)); asynchronous communication (enabling message queuing during temporary network outages, store-and-forward functionality, and offline composition); and structured data capability (supporting JSON / XML data transmission, form-based data collection, metadata inclusion (timestamps, device info, location), and schema validation). This layer ensures resilience to voice network failures, as it operates

[0038] NVS002BWO / MABexclusively over IP-based connectivity, and provides the initial input for the incident validation and report-generation processes.

[0039] The digital identity verification capability of the system is supported by a multi-layer authentication trust scoring system, which comprehensively evaluates the credibility of the reporter’s identity through four layers with specific scoring criteria.

[0040] Layer 1 is a platform verification (30 points). This layer verifies the reporter’s identity based on the authentication mechanism of the communication platform. Key evaluation items include whether the phone number is cryptographically bound and verified by the platform, whether the account is in good standing without abuse flags, whether the registered device information is consistent with the current usage device, and whether the account has passed the basic security review of the platform. A full score of 30 points is awarded if all items are satisfied; deductions are made proportionally for missing or inconsistent items.

[0041] Layer 2 is an account history (30 points). The system assesses the long-term behavior of the account to enhance identity trust. Evaluation indicators include account age (established accounts with more than 4 years get higher scores), regular usage activity (frequent messaging activity indicates a real user), no prior false emergency report records, and a reasonable social graph (e.g., 150+ contacts for consumer messaging platforms). The full score is 30 points, with deductions for newly created accounts, dormant accounts suddenly activated, or accounts with a history of false reports. Layer 3 is enhanced security (20 points). This layer focuses on the additional security measures enabled by the reporter for the account. It includes whether two-factor authentication is enabled, whether biometric locks (fingerprint, face ID) are active, whether the account is linked to a verified email, and whether other security features (such as login notification, abnormal login protection) are activated. The full score is 20 points, with corresponding points awarded based on the completeness of security configuration.

[0042] Layer 4 is a real-time behavior (20 points). The system analyzes the real-time behavior of the reporter during the emergency reporting process to verify identity consistency. Key indicators include whether the login pattern is consistent with historical records (to prevent account compromise), whether the location history is continuous and

[0043] NVS002BWO / MABreasonable, and whether the device fingerprint (hardware information, software environment) matches the historical data of the account. The full score is 20 points, and abnormalities such as sudden changes in login location or mismatched device fingerprints will result in score reductions.

[0044] The total identity trust score ranges from 0 to 100 points, with the following interpretations: 80-100 points (High Trust) - fast-track validation; 60-79 points (Medium Trust) - standard validation; 40-59 points (Low Trust) - enhanced validation; 0-39 points (Very Low Trust) - manual review required. This multi-layer scoring system provides a quantitative basis for identity verification, effectively distinguishing genuine users from potential fraudsters.

[0045] Computer-Implemented Multi-Modal Incident Validation Engine (Incident Intelligence Engine) :

[0046] The computer-implemented multi-modal incident validation engine (incident intelligence engine) is a core component of the system, implementing automated realtime analysis across five dimensions to validate incident legitimacy, classify incident type, verify location accuracy, authenticate multimedia evidence, and detect fraud. This engine serves as the primary source of incident intelligence for validation, structured incident packaging, and dispatch-side operational handling.

[0047] A. Linguistic and Semantic Analysis:

[0048] This sub-component employs NLP models trained on genuine emergency reports to identify linguistic patterns indicative of emergencies, including urgency indicators (e.g., “help”, “emergency”, “dying”, “fire”, etc.), action verbs associated with emergencies (e.g., “attacked”, “collapsed”, “burning”, “bleeding”), and emotional language consistent with genuine distress. It classifies incidents into predefined categories (medical emergency (cardiac, respiratory, trauma, poisoning, psychiatric, etc.), fire incident (structure, vehicle, wildfire, explosion), law enforcement (violence, theft, assault, kidnapping, domestic violence), traffic accident (collision, hit-and-run, vehicle fire, road obstruction), natural disaster (earthquake, flood, landslide, severe weather), environmental hazard (gas leak, chemical spill, structural collapse), missing person, etc.) with confidence scoring (0-100%) that updates in real time as new information is received and is incorporated into the structured incident data record.

[0049] NVS002BWO / MABThe sub -component analyzes temporal, geographic, and logical consistency of the report, detects fraud linguistic patterns (vague descriptions, copy-pasted text, excessive emotional language without factual content, inconsistent details, evasive responses), and supports multi-language detection, real-time translation for dispatchers, and cultural context awareness to account for regional variations in emergency communication. All classification and consistency data is incorporated into the structured incident data record for downstream operational use.

[0050] B. Location Intelligence and Multi-Source Verification:

[0051] This sub-component implements cross-validation of incident location using five independent data sources to ensure accuracy and detect geographic fraud: (1) user-shared location (location pins shared via messaging platform, coordinates from location-sharing feature, address manually typed by user, landmarks or descriptive location info); (2) device-based location (GPS coordinates from device, WiFi-based positioning (if on WiFi network), cellular network triangulation, IP-based geolocation); (3) multimedia metadata (geotagged photos (EXIF GPS data), video geo tags, timestamp information, device information in metadata); (4) visual location analysis (automated extraction of street signs, addresses, landmarks (buildings, monuments, geographic features), business names and signage, license plates (for region identification), environmental clues (vegetation, architecture style, language on signs) from photos / videos, reverse image search to identify known locations); and (5) linguistic location clues (addresses or locations mentioned in text, landmarks described, directional information (“near”, “across from”), local terminology (neighborhood names, local landmarks)). A location confidence score is generated based on the consistency of these sources: high confidence when multiple independent sources agree within a predefined threshold proximity (for example, within about 100 meters in some embodiments); medium confidence when some sources agree with minor discrepancies; low confidence when data is conflicting or derived from a single source; and location anomaly when the reporter’s location differs significantly from the incident location. All location data may be made available for downstream operational processing and dispatch-side handling. Geographic fraud detection identifies red flags such as reporters located in a different city / country than the incident, impossible location timelines, and metadata stripping from photos.

[0052] NVS002BWO / MABC. Multimedia Evidence Validation and Authenticity Verification:

[0053] This sub-component actively validates the authenticity and relevance of multimedia evidence (photos, videos, audio) through computer vision, digital forensics, and content analysis. For photos / images: reverse image search compares content against internet databases, stock photo libraries, and social media to detect stock photos, previously published images, or recycled content; metadata analysis verifies recent timestamps (not old photo being reused), location consistency, and device information, while detecting metadata manipulation or stripping; digital forensics identifies photo editing software signatures, cloned regions (copy-paste within image) , inconsistent compression artifacts, and lighting / shadow anomalies; and content analysis employs object detection to identify emergency-relevant objects (fire, smoke, flames, weapons (guns, knives, etc.), vehicles involved in accidents, injuries or blood, structural damage, flood water, debris from disasters) and scene classification to confirm consistency with the reported incident. For video streams: liveness detection verifies real-time capture through interactive challenges (e.g., “pan camerato the left”, “show current time on watch”), analysis of playback artifacts, and motion pattern detection; real-time video analysis tracks incident progression (e.g., fire spreading, crowd movement) and extracts key frames for evidence. For audio messages: voice stress analysis distinguishes genuine panic from scripted speech; background noise analysis identifies emergency-related sounds (traffic, sirens, screaming, alarms); and acoustic environment analysis confirms consistency with the reported location (indoor vs. outdoor) . All evidence validation results and incident severity assessments derived from multimedia analysis are incorporated into the structured incident data record for downstream operational use.

[0054] In particular, this sub-component implements a multi-stage authentication process for photos / images to ensure their authenticity and relevance to the incident.

[0055] Stage 1 is the authenticity verification, which includes reverse image search, metadata analysis, and digital forensics.

[0056] The system compares the submitted photos against multiple databases, including commercial reverse image search services, stock photo databases, social media archives, and news archives. If matches are found (e.g., the image is identified as a

[0057] NVS002BWO / MABstock photo from 2019 or a published news image), a fraud flag is triggered, and the image is deemed non-original.

[0058] The system extracts key metadata from the image, including device model, camera parameters (aperture, exposure time, ISO), timestamp, GPS coordinates, altitude, shooting direction, and software version. Validation checks include:

[0059] - whether the timestamp is sufficiently recent relative to the reported incident, - whether the GPS coordinates match the reported incident location,

[0060] - whether the device information is consistent with the reporter’s registered device, and

[0061] - whether the metadata is intact (deliberately stripped metadata is considered suspicious) .

[0062] Inconsistent or impossible metadata values (e.g., a timestamp from five years ago) will result in a reduction in authenticity score.

[0063] The system employs advanced digital forensic techniques to identify image editing or manipulation, comprising: the software signature detection for scanning the image for markers or metadata left by photo editing software to determine if the image has been edited; the compression artifact analysis for checking for double JPEG compression or inconsistent compression levels across different regions of the image, which are indicative of splicing or modification; the clone detection using pattern recognition algorithms to identify copy-pasted regions within the image (e.g., cloned backgrounds or objects); the lighting and shadow analysis for verifying the consistency of lighting sources and shadow directions across the image.

[0064] Stage 2 is the content analysis, which includes an object detection, scene classification, and visual location verification.

[0065] The system uses Al computer vision models to identify emergency-relevant objects in the image, such as fire, smoke, flames, weapons, vehicles involved in accidents, injuries, blood, structural damage, flood water, and debris. The presence of these objects is cross-checked against the text report.

[0066] NVS002BWO / MABThe system further classifies the scene as indoor / outdoor, urban / rural, residential / commercial / public space, and records weather conditions. This classification is verified for consistency with the reported incident context.

[0067] The system also extracts visible street signs, building numbers, landmarks, business names, and license plates from the image. These visual clues are cross-referenced with satellite mapping services, street-level imagery databases, and open geographic data sources to confirm the location matches the reported address.

[0068] D. Integrated Fraud Detection Algorithm:

[0069] This sub-component generates a real-time fraud risk score (0-100%) using a weighted multi-factor model, with weights assigned as follows: identity factors (20%: account age, activity history, prior false report history, identity verification level); linguistic factors (25%: description specificity, consistency, emotional appropriateness, response coherence); location factors (20%: multi-source location agreement, reporter-incident location discrepancy, geographic feasibility); multimedia factors (25%: evidence authenticity, metadata consistency, content-report alignment, evidence freshness); and behavioral factors (10%: response time to validation questions, willingness to provide additional evidence, pattern matching against known fraudster behaviors). The fraud risk score is mapped to five risk levels with corresponding actions: very low risk (0-20%: fast-track to dispatch); low risk (21-40%: standard processing); medium risk (41-60%: additional validation required); high risk (61-80%: manual review before dispatch); very high risk (81-100%: block report and flag account) . The algorithm tracks serial false reporters through behavioral pattern recognition and automatically escalates their fraud risk scores for subsequent reports. Fraud risk score and legitimacy validation results are incorporated into the structured incident data record to inform downstream operational handling.

[0070] Intelligent Interactive Validation Module:

[0071] This component implements active evidence collection through dynamic questioning and incident-specific evidence requests, distinguishing it from passive reportreceiving systems. It generates ranked lists of next-best questions to: fill information gaps (e.g., “How many people are injured?”), test reporter legitimacy (e.g., “What landmarks can you see from your location?”), assess immediate danger (e.g., “Are you

[0072] NVS002BWO / MABin a safe location right now?”), and gather tactical intelligence (e.g., “Which direction did they go?”). For each incident type, it automatically requests relevant multimedia evidence: medical emergencies require patient photos / videos or breathing confirmation; fires require footage of flames / smoke or building entrances; crimes in progress require suspect descriptions or safety-captured video; traffic accidents require vehicle photos or license plate captures. Conversational authenticity testing analyzes response timing, specificity of details provided, knowledge of on-scene conditions, emotional congruence, and compliance with evidence requests to further distinguish genuine from fraudulent reports. All additional information collected by this module is incorporated into the incident data record and may be used to update operational handling as the incident unfolds.

[0073] Emergency Dispatch Integration Pipeline:

[0074] This component transforms unstructured messaging conversations into standardized emergency incident reports and integrates with existing emergency dispatch infrastructure, and may interface with dispatch-side recommendation or resourceselection systems. The structured incident report generation sub-component extracts and organizes data into predefined fields, including reporter information (identity verification, contact details, reporting context, etc.), incident classification (primary type, sub-type, confidence score, severity level, priority score, etc.), location intelligence (validated coordinates, address, access information, landmarks, etc.), incident narrative (initial report, timeline, current situation, involved parties, special circumstances, etc.), evidence package (text transcript, photos, videos, audio, metadata analysis, etc.), validation assessment (fraud risk score, linguistic authenticity, location verification, multimedia authenticity, etc.), operational response recommendations (suggested response forces, tactical considerations, urgency assessment, resource requirements, etc.), and communication status (reporter availability, bidirectional channel status, follow-up capability, etc.). The subcomponent leverages a structured incident data record to eliminate manual data extraction and ensure the report is continuously updated with real-time incident details.

[0075] NVS002BWO / MABThe structured emergency incident report generated by the emergency dispatch integration pipeline includes a complete set of 8 modules with detailed sub-fields, ensuring all critical information is captured for dispatch decision-making:

[0076] 1). Reporter Information

[0077] • Identity Verification: Platform name, verified phone number, account trust score (0-100), authentication level (Basic / 2 FA / Biometric), device fingerprint, account age, and activity history.

[0078] • Contact Information: Platform handle / ID, alternative phone number (if any), preferred contact method (text / video / audio), and bidirectional channel status (Active / Inactive) .

[0079] • Reporting Context: Reporter role (at scene / witness / third party), relationship to victim (if applicable), primary language, and cultural background (for translation and context adaptation).

[0080] 2). Incident Classification

[0081] • Primary incident type (Medical / Fire / Law Enforcement / Traffic / Natural Disaster / Environmental Hazard / Missing Person / Other) .

[0082] • Sub -classification (e.g., Medical: cardiac / respiratory / trauma; Fire:

[0083] structure / vehicle / wildfire) .

[0084] • Confidence score (0-100%), severity level (Critical / High / Medium / Low), and priority score (1-5).

[0085] 3). Location Intelligence

[0086] • Validated Location: Exact coordinates (latitude / longitude), full street address, location confidence score (High / Medium / Low with percentage), and verification sources (GPS / WiFi / cellular triangulation / IP geolocation / multimedia metadata / visual analysis).

[0087] NVS002BWO / MAB• Access Information: Building floor / apartment number, access restrictions (gate code, key requirements), nearby landmarks (for responder navigation), parking availability, and hazards affecting access.

[0088] • Location Anomaly: Flag for reporter remote location (reporter’s location significantly different from incident location) , conflicting location data, or proxy reporting indicators.

[0089] 4). Incident Narrative

[0090] • Initial Report: First message received from the reporter.

[0091] • Timeline: Sequence of events.

[0092] • Current Situation: Latest status of the incident.

[0093] • Involved Parties: Victim count, condition (conscious / unconscious / bleeding), descriptions; suspect count, physical descriptions, weapons carried, direction fled; witness count and contact information (if provided) .

[0094] • Special Circumstances: Disabilities of involved parties, language barriers, presence of pets, environmental hazards (gas leak, chemical exposure), and structural risks (collapsible building) .

[0095] 5). Evidence Package

[0096] • Text Transcript: Full verbatim transcript of the conversation between the reporter and the system / dispatcher.

[0097] • Photos: Attached images with timestamps, authenticity scores (0-100%), and analysis summaries.

[0098] • Videos: Attached video files with key frame extracts, liveness verification results, and scene analysis.

[0099] Audio: Audio messages with voice stress analysis results and background noise classification.

[0100] NVS002BWO / MAB• Metadata Analysis: Consolidated metadata from all multimedia files (location, timing, device info) and cross-validation results.

[0101] 6). Validation Assessment

[0102] • Fraud Risk Score: 0-100% with interpretation (Very Low / Low / Medium / High / Very High Risk).

[0103] • Linguistic Authenticity: Score (0-100%) and flags.

[0104] • Location Verification: Score (0-100%) and discrepancies.

[0105] • Multimedia Authenticity: Score (0-100%) and issues detected.

[0106] • Identity Trust: Score (0-100%) based on the multi-layer authentication trust scoring system.

[0107] • Overall Confidence: High / Medium / Low, with recommendation (Fast-track dispatch / Standard processing / Manual review / Reject).

[0108] 7). Operational Response Recommendation

[0109] • Suggested Response Forces: Type (Police / Fire / Medical / Multiple), quantity and specialization, equipment requirements (e.g., defibrillator, extrication tools), and personnel qualifications (e.g., paramedics with burn training, tactical police officers).

[0110] • Tactical Considerations: Scene safety concerns (e.g., structural instability, armed suspect), approach recommendations, special equipment needs (e.g., breathing apparatus, drones), and coordination requirements.

[0111] • Urgency Assessment: Immediate / Urgent / Standard.

[0112] • Estimated Resource Requirements: Number of units, personnel count, equipment list, and expected response duration.

[0113] 8). Communication Status

[0114] NVS002BWO / MABReporter Availability: Still in contact / Lost connection / Evacuated to safe location.

[0115] • Bidirectional Channel: Active / Inactive.

[0116] • Follow-up Capability (Can provide updates / Cannot be reached).

[0117] The multi-system integration and delivery sub-component interfaces with computer-aided dispatch (CAD) systems and supports delivery of validated incident information and operational recommendations to dispatch systems upon report completion or once sufficient confidence is reached. In some embodiments, dispatch-side recommendation logic may use validated incident classification, location, and severity information to support selection of appropriate response resources.

[0118] Bidirectional Communication Maintenance Module:

[0119] This component maintains persistent bidirectional communication between the reporter, dispatchers, and responding units, transforming the reporter into an active tactical asset. It sends automated status updates to the reporter (e.g., “Help is on the way. Estimated arrival: 8 minutes”) and safety instructions (e.g., “If safe to do so, exit the building immediately”) . Dispatchers can send direct messages to request real-time updates, clarify details, or assign intelligence -gathering tasks (e.g., “Count how many people need medical attention”). Reporters provide ongoing situational updates (e.g., fire spreading, suspect movements), collect additional evidence, guide responders to the exact location, and confirm responder arrival. Post-incident, the module facilitates follow-up information collection for investigations and provides the reporter with an incident report number. This module further serves as the interface for bidirectional communication between the reporter and the dispatcher within the dispatcher workstation.

[0120] The present invention also provides a corresponding method, comprising the steps of: receiving, via an internet-based communication channel integration layer, an emergency report from a user device of a reporter via an authenticated internet -based communication channel, the report comprising at least one of text, location data, and multimedia evidence; performing, by a multi-modal incident validation engine, realtime multi-modal validation on the emergency report, including performing linguistic

[0121] NVS002BWO / MABand semantic analysis on the text to classify an incident and detect fraud, performing multi-source cross-verification on the location data to generate a location confidence score, and performing authenticity verification and content analysis on the multimedia evidence; generating a structured emergency incident report based on the validation results; and transmitting the structured emergency incident report to a dispatcher or emergency dispatch system.

[0122] Referring now to FIG. 1, the overall system architecture of the internet-based emergency reporting system according to an embodiment is shown. In operation, a user or a reporter 101 initiates an emergency report via an authenticated internetbased communication channel 103 (e.g., a messaging app, web interface, or native application) on a user device 102. The initial report data is transmitted via an IP network 104 to an internet-based communication channel integration layer 105. This layer 105 receives and standardizes data from diverse channels, extracting user identity, message content, and metadata.

[0123] The standardized report data is processed by a computer-implemented multi-modal incident validation engine (incident intelligence engine) 106 and an emergency dispatch integration pipeline 117 for structured incident report generation and delivery. The incident intelligence engine 106 performs real-time, multi-dimensional analysis, including: linguistic and semantic analysis 109 for incident classification and fraud pattern detection; location intelligence and multi-source verification 110 generating a location confidence score; multimedia evidence validation and authenticity verification 111; and integrated fraud detection 112. All resulting intelligence is incorporated into the structured incident data record 108. An intelligent interactive validation module 113 dynamically generates questions or evidence requests, with responses also incorporated into the incident data record 108.

[0124] The emergency dispatch integration pipeline 117 generates a structured incident report. This report is delivered via APIs 118 to existing Computer-Aided Dispatch (CAD) systems or Emergency Operations Centers (EOCs) 119. A dispatcher 120 views the report and can maintain real-time communication with the user or reporter 101 via a bidirectional communication maintenance module 121.

[0125] NVS002BWO / MABWith reference to FIG. 2, the validation process begins when an emergency report is received 201. The system then initiates three parallel analytical paths. The first path is the linguistic and semantic analysis 202, which includes sub-steps of an urgency detection 203, an incident classification 204, a consistency analysis 205, and a fraud pattern detection 206. The second path is the location intelligence and multi-source verification 207, comprising a GPS / WiFi / IP collection 208, a visual location analysis 209, a cross-validation 210, and a confidence scoring 211. The third path is the multimedia evidence validation and authenticity verification 212, which performs a reverse image search 213, a metadata analysis 214, digital forensics 215, and an object detection 216. The outputs from all three analytical paths converge at the integrated fraud detection algorithm 217, which calculates a comprehensive fraud risk score 218. Based on this score, the system makes a decision to either fast-track dispatch 219 for very low-risk reports, proceed with standard processing 220 for low-risk reports, require additional validation 221 for medium-risk reports, conduct a manual review before dispatch 222 for high-risk reports, or block and flag the account 223 for very high-risk reports.

[0126] FIG. 3 illustrates the bidirectional communication maintenance module connecting an emergency reporter, the system, and a dispatch center. Communication is bidirectional and persistent, as indicated by the arrows.

[0127] Reporter unit 301 initiates a report via a communication app. Upon receipt, the intelligent interactive validation module 302 automatically sends validation questions or evidence requests 303. The reporter unit 301 provides a reply 304.

[0128] Simultaneously, validated data forms a structured incident report 305 presented to a dispatcher 306. The dispatcher 306 can actively communicate with the reporter unit 301 via an integrated bidirectional messaging interface 307, sending status updates & safety instructions 308, or requesting real-time intelligence updates 309.

[0129] Reporter unit 301, receiving these, can provide an ongoing situational update 310. This loop continues throughout the response, transforming the reporter into an active tactical asset.

[0130] NVS002BWO / MABFIG. 4 is a flowchart illustrating a computer-implemented method for internet-based emergency reporting according to an embodiment of the present invention. The method depicts the end-to-end automated process from report initiation to dispatch. The process begins at step 401, where a user or reporter initiates an emergency report via an authenticated internet-based communication channel on a user device. The report comprises at least one of the following items: text, location data, and multimedia evidence.

[0131] In step 402, the report is received by an internet-based communication channel integration layer. This layer standardizes the data from diverse channels, extracting user identity, message content, and metadata.

[0132] An incident validation process 403 comprises steps 403a to 403d. In step 403a, a multi-modal incident validation engine performs linguistic and semantic analysis on the report text to detect emergency language, classify the incident type, and identify fraud linguistic patterns.

[0133] In step 403b, the engine performs multi-source cross-verification on the location data. It aggregates and compares location information from multiple independent sources (e.g., user-shared location, device GPS, multimedia metadata) and generates a location confidence score based on their consistency.

[0134] In step 403c, the engine performs authenticity verification and content analysis on multimedia evidence. This includes executing a reverse image search, analyzing metadata consistency, performing digital forensics, and using computer vision to identify emergency-relevant objects.

[0135] In step 403d, an integrated fraud detection algorithm calculates a real-time fraud risk score for the report based on a weighted multi-factor model (identity, linguistic, location, multimedia, behavioral factors), determining the subsequent processing path.

[0136] Step 405 represents the optional operation of an intelligent interactive validation module. Based on preliminary analysis, it can dynamically generate validation

[0137] NVS002BWO / MABquestions or specific evidence requests, with user responses incorporated into the incident data record.

[0138] In step 406, upon report completion or when sufficient confidence is reached, the system generates a structured emergency incident report based on the validation results and collected evidence.

[0139] Finally, in step 407, the structured report is transmitted to a dispatcher or an external emergency dispatch system (e.g., a Computer-Aided Dispatch (CAD) system or an Emergency Operations Center (EOC)) via an API within an emergency dispatch integration pipeline, thereby completing the seamless integration from the internetbased report to actual response resources.

[0140] Examples

[0141] Five specific application examples are provided as follows.

[0142] First Example: Emergency Reporting During Natural Disasters with Voice Infrastructure Failure

[0143] In a catastrophic event such as an earthquake or hurricane, traditional circuit-switched voice networks often suffer from physical damage or severe congestion. However, IP-based data connectivity, including local WiFi hotspots, cellular data (LTE / 5G), or Low Earth Orbit (LEO) satellite internet (e.g., Starlink), frequently remains functional. In this embodiment, a user in a disaster zone, unable to complete a voice call, initiates an emergency report via an authenticated messaging platform. The Gateway receives the data-only transmission and utilizes the multi-source location cross-validation system to pinpoint the survivor’s coordinates by correlating device GPS, WiFi positioning, and metadata from sent images, ensuring rescue teams are dispatched to the precise location despite the voice network blackout.

[0144] Second Example: Silent Reporting in High-Danger Situations

[0145] There are scenarios where speaking aloud would place the reporter in immediate peril, such as during a home invasion, a hostage situation, or an incident of domestic violence. In such cases, the system enables the victim to communicate silently through an internet-native messaging interface. Beyond passive receipt of text, the

[0146] NVS002BWO / MABGateway’s interactive validation system engages the user with discrete, dynamic questions (e.g., “Is the suspect armed?”, “Are you in a locked room?”). This allows for the collection of critical tactical intelligence without alerting the perpetrator, while the Al-assisted multi-modal engine validates the urgency of the situation based on the linguistic patterns of the chat.

[0147] Third Example: Multimedia- Driven Medical Triage and Resource Allocation Traditional emergency reporting often lacks the visual data necessary for accurate medical triage. In a medical emergency, a bystander can use the system to transmit high-definition video or photos of a patient’s symptoms (e.g., respiratory distress or physical trauma). The multimedia evidence authentication and content analysis module processes the visual data in real-time. For instance, by analyzing the patient’s breathing rate or skin pallor in a video, the Al engine can recommend a specific level of response, such as suggesting an Advanced Life Support (ALS) ambulance instead of a Basic Life Support (BLS) unit. This transforms unstructured visual evidence into actionable operational recommendations for the dispatcher.

[0148] Fourth Example: Mitigation of Fraudulent Reporting and Prank Calls

[0149] Text-based reporting systems are historically vulnerable to “swatting” or fraudulent reports due to a lack of authentication. The system of the present application addresses this by implementing a multi-factor fraud detection process. If a user submits a report of a major fire using a recycled image from the internet, the system’s reverse image search and digital forensics modules will immediately flag the media as non-original or manipulated. Furthermore, the system analyzes the reporter’s identity trust score and location feasibility (e.g., checking if the reported fire location matches the user’s IP geolocation). If the fraud risk score exceeds a specific threshold, the incident is flagged for secondary review, preventing the unnecessary diversion of emergency resources.

[0150] Fifth Example: Overcoming Language Barriers in International Emergencies

[0151] In situations involving foreign tourists or non-native speakers, voice communication often fails due to language barriers. By utilizing an internet-native gateway, the system allows reporters to submit information in their native language via messaging

[0152] NVS002BWO / MABplatforms. The integration layer can utilize Natural Language Processing (NLP) to perform real-time translation of the text for the local emergency dispatcher. Simultaneously, the dispatcher’s instructions can be translated back into the reporter’s language. This bidirectional, text -based communication ensures that life-saving instructions — such as CPR guidance or evacuation routes — are accurately understood and followed regardless of the language spoken by the victim.

[0153] All in all, the invention provides an authenticated internet-based emergency reporting gateway with multi-modal validation, multimedia authenticity verification, fraud detection, interactive evidence gathering, structured incident packaging, and dispatch-side integration.

[0154] NVS002BWO / MAB

Claims

CLAIMS1. A computer-implemented method for internet-based emergency reporting, comprising:receiving, via an internet-based communication channel integration layer, an emergency report from a user device of a reporter via an authenticated internetbased communication channel, the report comprising at least one of text, location data, and multimedia evidence;performing, by a multi-modal incident validation engine, real-time multi-modal validation on the emergency report, including performing linguistic and semantic analysis on the text to classify an incident and detect fraud, performing multisource cross-verification on the location data to generate a location confidence score, and performing authenticity verification and content analysis on the multimedia evidence;generating a structured emergency incident report based on validation results; andtransmitting the structured emergency incident report to a dispatcher or emergency dispatch system.

2. The method of claim 1, wherein performing multi-source cross-verification on the location data comprises aggregating and comparing at least two independent location sources selected from: a user-shared location, a device-based location, geotags from multimedia metadata, and location clues extracted from visual analysis of multimedia content.

3. The method of claim 1, wherein performing authenticity verification on the multimedia evidence comprises performing a reverse image search to detect stock or reused imagery, analyzing metadata to verify timestamp and geolocation consistency, and performing digital forensic analysis to detect image tampering signatures.

4. The method of claim 3, wherein authenticity verification of video evidence comprises liveness detection including issuing interactive challenges to theNVS002BWO / MABreporter and analyzing the video response for playback artifacts and motion consistency.

5. The method of claim 1, further comprising:generating, by an integrated fraud detection algorithm, a real-time fraud risk score for the emergency report based on a weighted combination of identity factors, linguistic factors, location factors, multimedia factors, and behavioral factors; and processing the emergency report according to a risk level corresponding to the fraud risk score, wherein processing actions include fast-tracking to dispatch, standard processing, requiring additional validation, or blocking and flagging the report.

6. The method of claim 1, further comprising:dynamically generating, by an intelligent interactive validation module based on preliminary analysis by the multi-modal incident validation engine, one or more validation questions or evidence requests and sending them to the user device of the reporter; andincorporating responses from the reporter to the validation questions or evidence requests into an incident data record for further analysis and report updating.

7. The method of claim 1, further comprising:maintaining, via a bidirectional communication maintenance system, a persistent communication channel between the reporter and the dispatcher;sending, via the communication channel, automated status updates or safety instructions to the reporter; andenabling, via the communication channel, the dispatcher to request real-time updates from or assign intelligence -gathering tasks to the reporter.

8. The method of claim 1, wherein the authenticated internet-based communication channel operates over at least one of WiFi, cellular data, satelliteNVS002BWO / MABinternet, or another IP-based network, independent of circuit- switched voice telephony infrastructure.

9. The method of claim 1, wherein the multi-modal incident validation engine generates an identity trust score for the reporter based on a multi-layer evaluation comprising platform -level identity verification, account history analysis, enhanced security configuration assessment, and real-time behavioral consistency analysis.

10. A non-transitory computer-readable storage medium storing computerexecutable instructions that, when executed by one or more processors, cause the processors to perform the method of any one of claims 1 to 9.

11. A system for internet -based emergency reporting, comprising:one or more processors; anda memory storing computer-executable instructions that, when executed by said one or more processors, cause the system to perform the method of any one of claims 1 to 9.

12. A system for internet -based emergency reporting, comprising:an internet-based communication channel integration layer configured to receive emergency report data from a reporter through a plurality of authenticated internet-based communication channels;a computer-implemented multi-modal incident validation engine communicatively coupled to the integration layer and configured to perform real-time multi-modal validation and analysis on the report data to generate validation intelligence; an emergency dispatch integration pipeline configured to generate a structured emergency incident report based on the validation intelligence and to transmit the report to a dispatcher or emergency dispatch system; anda bidirectional communication maintenance module configured to maintain persistent bidirectional communication between the reporter and the dispatcher.NVS002BWO / MAB13. The system of claim 12, wherein the multi-modal incident validation engine comprises:a linguistic and semantic analysis sub-module for detecting emergency language, classifying incident type, and analyzing consistency;a location intelligence and verification sub-module for cross-verifying location data from multiple independent sources;a multimedia validation sub-module for performing authenticity verification and content analysis on multimedia evidence; andan integrated fraud detection sub-module for calculating a fraud risk score based on a multi-factor weighted model.

14. The system of claim 12, further comprising an intelligent interactive validation module configured to dynamically generate and send validation questions or evidence requests to the reporter based on preliminary output from the multimodal incident validation engine, and to incorporate response results into an incident data record.

15. The system of claim 13, wherein the location intelligence and verification submodule generates a location anomaly flag when a device location of the reporter diverges from a reported incident location beyond a configurable threshold, indicating possible proxy reporting.NVS002BWO / MAB

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