AI-ENHANCED REAL-TIME CRIME DETECTION AND TRACKING SYSTEM FOR CRIMINAL MONITORING

Abstract

The invention is a mobile application that functions as a forensic tool for distinguishing between suicide, murder, or accidental death by transforming a smartphone into a “black box” for capturing crucial pre-incident data. Using built-in sensors including accelerometer, gyroscope, GPS, and microphone—the app continuously records movement patterns, location history, and ambient audio, securely storing this information for potential forensic analysis. With AI-driven algorithms, the app detects anomalous events indicative of distress or physical confrontation and flags these for investigation. The recorded data is visualized through a forensic interface that helps investigators reconstruct the incident timeline, providing critical insights that support forensic teams in determining the circumstances surrounding a suspicious death.

Specification

Description:In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

The word "exemplary" and/or "demonstrative" is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "exemplary" and/or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms "includes," "has," "contains," and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition word without precluding any additional or other elements.

Reference throughout this specification to "one embodiment" or "an embodiment" or "an instance" or "one instance" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

This invention centers around a mobile application that transforms a smartphone into a forensic data collection device, effectively functioning as a "black box" for the user. It records essential data through integrated sensors, allowing investigators to determine whether a suspicious death was due to suicide, murder, or other causes. The app uses a suite of smartphone sensors-such as accelerometers, gyroscopes, GPS, and microphones-to continuously monitor and log the user's movement, location, and ambient sounds. This sensor data, stored securely on the device, provides a timestamped record of events leading up to the incident, offering valuable forensic insights into the victim's final moments.

The app operates seamlessly in the background, minimizing intrusion into the user's daily activities. Using a lightweight design, it gathers data continuously without significantly impacting device performance or battery life. The recorded data includes movement patterns from the accelerometer and gyroscope, location history from GPS, ambient audio from the microphone, and additional metrics from sensors like the barometer (to detect altitude changes) and heart rate monitor (if available). Each data stream is time-stamped and encrypted to ensure that the information remains secure and tamper-resistant until needed for an investigation.

One of the app's primary features is its ability to detect and flag anomalies through AI-based event detection algorithms. These algorithms analyze the recorded sensor data for irregular patterns that could suggest distress, struggle, or unusual movement. For instance, a sudden shift in movement or altitude, followed by a period of inactivity, could be indicative of a fall or physical altercation. Ambient sounds are also analyzed to detect significant audio cues, such as loud voices, cries for help, or other sounds that might suggest conflict. This flagged data allows forensic teams to prioritize specific moments in the timeline, making the analysis more efficient.

Upon authorization, forensic teams can retrieve the recorded data using a dedicated interface that provides clear visualization of the events. The interface enables investigators to analyze sensor data in a timeline format, displaying movement patterns, location history, and audio recordings to reconstruct the sequence of events. This data visualization, combined with synchronized sensor inputs, helps investigators understand the user's activity prior to the incident, uncovering critical insights into whether a death was intentional or a result of foul play. By correlating sensor data with other sources-like witness statements, CCTV footage, or physical evidence-investigators can build a comprehensive account of the incident.

The app's machine learning algorithms are trained on a wide range of typical human activity patterns, enabling it to distinguish between normal, day-to-day movements and high-stress events. These models learn to identify outliers, such as sharp impacts or prolonged inactivity, that might correlate with a violent encounter or medical emergency. As a result, the app provides a probabilistic assessment of the likelihood of various scenarios, supporting investigators with data-driven insights into whether an event aligns more closely with suicide, murder, or accident.

In addition to assisting forensic investigations, this invention has practical applications for personal safety monitoring. Users can activate the app in "safety mode" if they anticipate potential risk, such as when traveling in unfamiliar areas. Family members of elderly or at-risk individuals may also benefit from using this app to monitor for signs of falls or distress, enabling timely intervention. Furthermore, the app could aid in documenting evidence in cases of domestic violence or abuse, where capturing sensor data and audio could substantiate claims of physical harm or verbal threats.

This invention represents a significant step forward in digital forensics, providing law enforcement with a precise tool for distinguishing between suicide and murder. By capturing and preserving vital sensor data, the app empowers investigators with evidence that can lead to a more accurate understanding of suspicious deaths. The technology not only strengthens forensic accuracy but also has the potential to offer peace of mind to users and their families, ultimately contributing to justice and personal safety.

While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the invention and not as limitation. , Claims:1.A mobile application for forensic investigation, enabling the determination of suspicious deaths as suicide or murder by utilizing smartphone sensors, comprising:
?an accelerometer to detect movement patterns and abrupt shifts,
?a gyroscope to capture rotation and orientation data,
?a GPS module to record location history, and
?a microphone to capture ambient audio data,
?wherein said application continuously collects and securely stores sensor data on the device for post-incident analysis.

2.The mobile application of claim 1, further comprising an event detection algorithm that identifies abnormal patterns in sensor data, such as sudden movements or changes in altitude, indicative of potential distress or physical altercations, thereby flagging data segments for forensic review.

3.The mobile application of claim 1, wherein the accelerometer and gyroscope data are used to differentiate between common daily activities and irregular, high-intensity movements, enabling identification of falls, struggles, or deliberate self-harm.

4.The mobile application of claim 1, wherein GPS data is used to trace the user's movements, establish location history, and detect abrupt changes in location, enabling forensic experts to determine the whereabouts of the user in relation to the incident.

5.The mobile application of claim 1, further comprising an audio processing module that analyzes microphone data to classify sounds as verbal altercations, distress sounds, or silence, providing contextual audio evidence to support forensic analysis.

6.The mobile application of claim 1, further including a secure data storage module that encrypts and stores all sensor data on the device, ensuring data integrity and enabling retrieval by authorized personnel for forensic investigation.

7.The mobile application of claim 1, wherein the data collected is organized into a timeline format, with time-stamped sensor readings, allowing investigators to visualize the sequence of events leading up to the incident.

8.The mobile application of claim 1, further comprising an anomaly detection algorithm that flags significant deviations from typical movement and sound patterns, aiding in early detection of potentially dangerous or suspicious situations.

9.The mobile application of claim 1, wherein the forensic interface provides an interactive visualization of the recorded data, including maps for GPS data, graphs for motion data, and waveforms for audio data, facilitating easier interpretation by forensic teams.

The mobile application of claim 1, further comprising a machine learning model trained on typical human movement and sound patterns, wherein the model identifies and flags anomalous behaviour patterns that may correlate with incidents of suicide or foul play.

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