Methods and systems for e-commerce fulfillment is disclosed. The method includes sending instruction to product feeder to receive a plurality of products. The method includes sending instruction to product feeder to place each product of plurality of products on a conveyor belt after receiving plurality of products. The method includes receiving product barcode information associated with each product of plurality of products from a barcode scanner. The method includes receiving sales data associated with each product based on product barcode information associated with each product. The method includes computing a return on investment (ROI) associated with each product based on a product value of each product obtained from sales data. The method further includes sending an instruction to conveyor belt to channelize product in a first conveyor path of conveyor belt if ROI of a product of plurality of products is more than or equal to a threshold ROI.

Various embodiments are directed to a system or platform with machine learning capabilities configured to accurately predict in real-time a depreciation factor of a vehicle associated with a customer and further accurately predict a present value of the vehicle based at least in part on card transaction data associated with the customer. Based on one or more factors, such as a determination that the present value of the vehicle falls below a predefined threshold value, one or more auto financing products may be generated and provided to the customer by the system.

A system for selection and configuration of an automated robotic process includes a media input module structured to receive at least one functional media, a media analysis module structured to analyze the at least one functional media and identify an action parameter; and a solution selection module structured to select at least one component of an AI solution for use in an automated robotic process, wherein the selection is based, at least in part, on the action parameter.

In a computer-implemented method and system for capturing the condition of a structure, the structure is scanned with an unmanned aerial vehicle (UAV). Data collected by the UAV corresponding to points on a surface of a structure is received and a 3D point cloud is generated for the structure, where the 3D point cloud is generated based at least in part on the received UAV data. A 3D model of the surface of the structure is reconstructed using the 3D point cloud.

An intelligent property management system comprises an intelligent property management platform. The intelligent property management platform communicates with other system components via respective communication portals. The respective portals comprise a vendor portal, a tenant portal, an investor portal, a security device portal, and a communications service portal. These features enable the intelligent property management system, while utilizing a communication network, to intelligently improve, e.g., maintenance, security, communication, vendors services, and investments relating to managed properties.

A system, method and computer program product for computer based open innovation, includes an asset valuation device receiving asset information regarding tangible or non-tangible assets, and generating a valuation signal, based thereon; a self-executing code device receiving the valuation signal, and generating a self-executing code signal, based thereon; an air router device having both low band radio, and internet router channels for redundant internet communications, and a malicious code removal device for scrubbing malicious code from data received, receiving the valuation signal, and generating a node voting request signal, based thereon; a mesh network having node devices receiving the node voting request signal, and generating vote confirmation signals, based thereon; and computing devices connected to each of the respective node devices, and configured to perform non-fungible token (NFT) generation based on the assets, including tracking respective ownership and valuation of the assets, based on the asset information.

A system for dynamic pricing of secondhand goods comprising: a tag corresponding to an item, a database for hosting information regarding a plurality of items, a software capable of comparing items within the system and identifying a sale price, a location for placing the item for sale; placing an item for sale and dynamically raising or lowering the price of the item for sale as the information in the database is modified.

A processor-implemented method is disclosed. The method includes: receiving user input including a selection of a vehicle and personal data for an entity; obtaining value data for the selected vehicle; accessing database records associated with accounts of the entity at a resource server to obtain pre-qualification information for the entity; determining based on the pre-qualification information for the entity, that the entity is qualified for borrowing resources in connection with the selected vehicle; and in response to determining that the entity is qualified, selectively provide access to the user-inputted personal data for the entity.

Systems and methods are provided for automating property assessment using probable roof loss confidence scores. More particularly, the systems and methods may generate a base-line probable roof loss confidence score based upon buildings, roof, weather, hail, and climate data. The systems and methods may predict a specific event and a set of characteristics of the specific event. The systems and methods may predict a level of roof damage and a cost of roof damage based upon the predicted level of roof damage. The systems and methods may adjust a risk-related factor, and may further adjust policy parameters based upon the risk-related factor. A probable roof loss confidence score may be generated, as well as property insurance claims data or property insurance loss mitigation data based upon probable roof loss confidence score data.

Apparatus and methods for a smart glasses device are provided. The smart glasses device may execute a prediction model on video data captured by the smart glasses device to retrain the prediction model. The prediction model may be retrained in response to a data synchronization between an output of the prediction model and a gesture captured by the smart glasses device.