The present invention discloses a system for executing safety inspection and maintenance of an outdoor billboard by using a drone, which includes a drone (110) including an optical camera (111) and an infrared camera (112), and flying over a sky around a building in which the outdoor billboard is installed, and taking images of the outdoor billboard and a connection bracket (20) with the building; a monitoring unit (120) monitoring an image taken by the drone (110), and analyzing the image to identify deformation of an exterior (10) of the outdoor billboard and whether the connection bracket (20) is defective; a flight control unit (130) controlling a flight of the drone (110); and an inspection vehicle (140) driving through an inspection area of the outdoor billboard, and having a docking station (141) for landing and charging the drone (110) on a roof, in which safety inspection of an outdoor billboard is sequentially performed by using a drone while moving in an inspection vehicle, and maintenance is performed with one stop according to a safety inspection result by using a maintenance tool provided in the inspection vehicle.

A large language model (LLM) agent standardizes asset metadata and facilitates access to the resulting standardized data via one or more application programming interfaces (APIs). The LLM agent receives non-standardized data that includes first asset metadata describing a first asset and second asset metadata describing a second asset. The first asset metadata is obtained from a first domain and has a first format, and the second asset metadata is obtained from a second domain and has a second format. The data also includes sensor data. The LLM agent converts the different formats into a standardized format, resulting in generation of first standardized data. The LLM agent generates a data model that includes the standardized data and performance trend data. The LLM agent provides access to the data model via one or more APIs.

A computer implemented method, system, and non-transitory computer-readable device that may be used in a remote deposit environment. Upon receiving a user request, based on interactions with the UI, the method implements an electronic deposit of a financial instrument by activating a camera on the client device to generate a LIDAR managed live video stream of image data of a field of view of at least one camera, wherein the live video stream includes high quality confidence scored imagery of at least a portion of each side of the financial instrument. The method continues by extracting data fields based on the formation of image objects of each side of the financial instrument from the live video stream of image data. The extracted data fields are communicated to a remote deposit server to complete the remote deposit.

System and methods are disclosed for matching extracted text data based on one or more similarity scores. The method may include receiving one or more documents from a plurality of data sources, utilizing an optical character recognition algorithm for extracting text data from the one or more documents, comparing, utilizing a fuzzy matching algorithm, the extracted text data to reference dataset(s) to determine one or more matches between the extracted text data and at least one of the reference dataset(s), wherein the one or more matches are based on at least one similarity score, inputting the determined one or more matches and the at least one similarity score into a trained machine-learning model to refine the one or more matches, and outputting a representation of the refined one or more matches and the at least one similarity score to a graphical user interface of a device.

A topology map-based user location estimation apparatus includes an Optical Character Recognition (OCR) module, an OCR filter, and a location finding module. The OCR module is configured to recognize characters from an image of an environment surrounding a user. The OCR filter is configured to, based on a similarity determination with location information stored in a topology map that is generated based on a guide map image, filter one or more characters from the recognized characters. The location finding module is configured to, based on the filtered one or more characters, detect a current location of the user on the topology map.

Methods and systems are described herein for identifying the location and nature of any blur within one or more images received as a user communication and generating an appropriate correction. The system utilizes a first machine learning model, which is trained to identify blurred components of inputted images and determine whether the blurred components are located in portions of the inputted images comprising textual information. The system may apply a corrective action selected by the first machine learning model, which may comprise stitching blurred images together to a sharp product image and/or some other method appropriate for rectifying images received.

A system for generating textual instructions for manufacturers from hybrid textual and image data includes a manufacturing instruction generator that may generate a language processing module from a first training set including at least a training annotated file describing at least a first product to manufacture, the at least an annotated file containing one or more textual data, and at least an instruction set containing one or more manufacturing instructions to manufacture the at least a first product. Manufacturing instruction generator may use the language processing to generate textual instructions for manufacturers from at least an annotated file and may initiate manufacture using the generated manufacturing instructions.

Systems, methods, and devices for automated self-learning machine data normalization, digitization, and extraction for verification and notification are disclosed herein. In some embodiments, a computer-implemented self-learning method for dynamically transmitting electronically a registration authorization request includes accessing an electronic vehicular database to electronically retrieve source data objects, applying an algorithm to the electronically retrieved source data objects, extracting data fields from the source data objects, normalizing the extracted data fields from the source data objects, dynamically selecting a first remotely connected electronic vehicular authorization database, accessing first remotely connected electronic vehicular authorization database-specific vehicular interchange requirements, and electronically transmitting at least one of the electronically retrieved source data objects to the dynamically selected first remotely connected electronic vehicular authorization database based on the source data objects, satisfying the first remotely connected electronic vehicular authorization database-specific vehicular interchange requirements.

An automated invoice coding system for accounts payable is disclosed, comprising a data extraction module, an artificial intelligence (AI) engine with at least one machine learning model, a data processing module, a user interface module, an integration module, and a continuous learning module. The data extraction module extracts data from invoices, and the AI engine processes this data to generate coding predictions for accounting dimensions. The data processing module validates these predictions, while the user interface module displays them for user review and correction. The integration module transmits the validated coding predictions to the accounts payable system. The continuous learning module updates the AI model with new data, ensuring ongoing accuracy. The system operates autonomously without predefined rules or templates, providing real-time coding predictions.

One embodiment provides a computer-implemented method that includes comparing, by a computing device, an input character signal with one or more prestored character templates for determining an estimated difference measure. The computing device, based on the estimated difference measure, determines one or more mixing weights between a stored character patch buffer and a current input character patch for determining an output mixing patch. The computing device further updates the character patch buffer based on the output mixing patch. The computing device additionally substitutes a designated area using the output mixing patch to produce a final output.