METHOD AND SYSTEM TO FACILITATE ACCESS TO AND USE OF CONTEXTUAL IDENTITY INFORMATION DURING LAW ENFORCEMENT ENCOUNTERS FOR MINIMIZING CONFRONTATIONAL TENSIONS
A system and a method facilitating access to and use of contextual identity information during law enforcement encounters for minimizing confrontational tensions are disclosed. A user updates information corresponding to a user, his/her associates, and a vehicle on a government server and an insurance server. A law enforcement official uses a law enforcement device to access the government server, the insurance server, and a law enforcement server. The law enforcement official accesses the government server, the law enforcement server, and the insurance server to verify the identification of the user and the vehicle. The system allows the law enforcement official to have required information on the law enforcement device to verify the identification of the user and the vehicle. The law enforcement official verifies the identification at ease thereby reducing potential hostile situations with the user.
1 . A method of enhancing vehicle tag validation during a law enforcement procedure, the method comprising the steps of
receiving, by a processor, information corresponding to a user or an associated member of the user, and a vehicle associated with the user, wherein the information includes biometric data comprising at least one of facial recognition data and thumbprint data;
generating, by the processor or AI, a quick read identification number (QRID) based on the information of the user or the associated member, and the vehicle;
receiving, by the processor, a query during a law enforcement procedure, the query comprising at least one of a scanned vehicle tag number, a scanned QRI chip or barcode positioned on the vehicle, or captured biometric data of the user or the associated member;
parsing, by the processor, the query for searching a pre-stored database and retrieving real-time information from public records;
identifying, by the processor, the QRID in the pre-stored database corresponding to the user or the associated member, or the vehicle;
verifying, by the processor, using facial recognition technology or thumbprint validation whether the QRID matches with the user or the associated member, and the vehicle;
generating, by the processor, a tiered risk assessment of the user or the associated member based on factors including criminal history, outstanding warrants, behavioral conditions, medical conditions, weapon registrations, and stolen vehicle status; and
displaying, by the processor, the tiered risk assessment and a driver summary on a law enforcement device to allow a law enforcement officer to determine appropriate engagement protocols.
2 . The method of claim 1 , further comprising providing, by the processor, an automatic translation and response using artificial intelligence (AI) when a primary language of the user or the associated member differs from a language of the law enforcement officer, wherein the automatic translation and response translation enables real-time translation of spoken and written communications during the law enforcement procedure.
3 . The method of claim 1 , further comprising receiving, by the processor, a stolen vehicle report from the user via a client device, and transmitting a real-time message to local law enforcement via a law enforcement server, wherein the stolen vehicle status is reflected in QRID lookup results during subsequent law enforcement procedures.
4 . The method of claim 1 , further comprising executing, by the processor, a a Non-Uniform Illumination Correction algorithm on a captured image of a vehicle tag to optimize for low-contrast sources including temporary paper tags, damaged tags, or obscured tags, and extracting an alphanumeric character string using a Convolutional Neural Network-based Optical Character Recognition module.
5 . The method of claim 1 , further comprising providing, by the processor, an instacoach feature that delivers real-time, context-aware instructions to the law enforcement officer based on identified medical or mental or special conditions of the user or the associated member disclosed in a profile.
6 . The method of claim 1 , further comprising generating, by the processor, a Vehicle Authenticity Probability score based on temporal, geospatial, and semantic consistency across data sources including a government server, a law enforcement server, and an insurance server.
7 . The method of claim 1 , further comprising utilizing, by the processor, a Deep Learning Model for multi-source data federation and correlation across disparate datasets stored in government server, a law enforcement server, an insurance server, and a database to identify patterns indicative of authenticity, expiration, and ownership.
8 . The method of claim 1 , further comprising generating, by the processor, a Consolidated Status Display incorporating the tiered risk assessment and correlated data summaries, and transmitting the Consolidated Status Display to the law enforcement device for display to the law enforcement officer.
9 . A system for enhancing vehicle tag validation during a law enforcement procedure, the system comprising:
a processor, and
a memory coupled to the processor, wherein the memory stores program instructions executed by the processor;
a biometric sensor configured to capture at least one of facial recognition data and thumbprint data; and
a Trained Vehicular Data Search and Validation Engine (TSVE), wherein the processor executes the program instructions to:
receive information corresponding to a user, an associated member of the user, and a vehicle associated with the user, wherein the information includes biometric data and a primary language selection;
generate a quick read identification number (QRID) based on the information of the user, the associated member and the vehicle;
receive a query during a law enforcement procedure, wherein the query comprises at least one of a scanned vehicle tag number, a scanned QRI chip or barcode, or captured biometric data;
parse the query for searching a pre-stored database and retrieve real-time information from public records;
verify the identity of the user or the associated member using facial recognition technology or fingerprint validation;
generate a tiered risk assessment based on factors including criminal history, outstanding warrants, behavioral conditions, medical conditions, weapon registrations, and stolen vehicle status; and
display the tiered risk assessment and a driver summary on a law enforcement device to allow a law enforcement officer to determine appropriate engagement protocols.
10 . The system of claim 9 , wherein the processor executes the program instructions to provide an automatic AI translation and response option when a primary language of the user or the associated member differs from a language of the law enforcement officer.
11 . The system of claim 9 , wherein the system further comprises a QRI chip or barcode positioned at a bottom of a vehicle windshield on or near a Vehicle Identification Number plate, wherein the QRI chip or barcode contains encoded information corresponding to the QRID associated member with the vehicle and registered users.
12 . The system of claim 9 , wherein the processor executes the program instructions to receive a stolen vehicle report from the user via a client device and transmit a real-time message to local law enforcement via a law enforcement server.
13 . The system of claim 9 , wherein the TSVE executes a Non-Uniform Illumination Correction (NUIC) algorithm on a captured image of a vehicle tag to optimize for low-contrast sources including temporary paper tags, damaged tags, or obscured tags.
14 . The system of claim 9 , wherein the TSVE utilizes a Convolutional Neural Network-based Optical Character Recognition module to extract an alphanumeric character string from a processed image of a vehicle tag.
15 . The system of claim 9 , wherein the TSVE generates a Vehicle Authenticity Probability score based on temporal, geospatial, and semantic consistency across data sources including a government server, a law enforcement server, an insurance server, and a database.
16 . The system of claim 9 , wherein the TSVE utilizes a Deep Learning Model for multi-source data federation and correlation across disparate datasets stored in a government server, law enforcement server, an insurance server, and a database.
17 . The system of claim 9 , wherein the processor executes the program instructions to provide an instacoach feature that delivers real-time, context-aware instructions to the law enforcement officer based on identified medical or mental or special conditions of the user or the associated member.
17 . The system of claim 9 , wherein the TSVE generates a Consolidated Status Display incorporating the Vehicle Authenticity Probability score and correlated data summaries for transmission to the law enforcement device.
18 . The system of claim 9 , wherein the TSVE is operatively connected to a Mobile Data Acquisition Unit via an encrypted, low-latency Application Programming Interface (API) channel.
19 . (canceled)
20 . (canceled)