Abstract: Disclosed is an automated gun barrel cleaning system (100) and a method thereof for straight hollow cylindrical objects, preferably gun barrels. The system (100) enables scrubbing, mopping, lubrication and wiping of the gun barrel without the need to remove the cleaning device out of the gun barrel during cleaning and to replace the brush or mopping/wiping cloth. The method of cleaning provides a time-stamped cleaning data to monitor the condition of the gun barrel and to estimate quality and effective service life of the gun barrel. The system (100) comprises of a cleaning device 102 connected to a main controller unit 106 wherein the cleaning device 102 includes drive wheel assembly 210, a driven wheel assembly 240, a spray nozzle assembly 260, a vision system 270 and a cleaning assembly 220 providing controlled scrubbing, mopping and wiping functions with controlled supply of pressurized cleaning agent and lubricant oil.

Abstract: Disclosed is an automated storage and retrieval system (100) for returned assets such as mobile phones and tablets that facilitates reduced labor for transporting into and out of inventory, reduced inventory levels, more accurate tracking of storage locations, and space savings. The system (100) comprises a warehouse management unit coupled to a cloud storage medium for receiving, and storing the returned assets and offering them up for bids through a communication network. The warehouse management unit has a receiving unit, a storage unit having sensor based storage racks (17), a retrieval unit and a packaging unit communicatively coupled to a computing device and a storage medium specifically a cloud network. The assets are graded by evaluating the degree of damage, defects, imperfections that might reduce the value, physical/cosmetic state of a device and the like. The system supports manual, automatic, and semi-automatic storage and retrieval operations with maximum reliability.

Abstract: Disclosed is an automated gun barrel cleaning system (100) and a method thereof for straight hollow cylindrical objects, preferably gun barrels. The system (100) enables scrubbing, mopping, lubrication and wiping of the gun barrel without the need to remove the cleaning device out of the gun barrel during cleaning and to replace the brush or mopping/wiping cloth. The method of cleaning provides a time-stamped cleaning data to monitor the condition of the gun barrel and to estimate quality and effective service life of the gun barrel. The system (100) comprises of a cleaning device 102 connected to a main controller unit 106 wherein the cleaning device 102 includes drive wheel assembly 210, a driven wheel assembly 240, a spray nozzle assembly 260, a vision system 270 and a cleaning assembly 220 providing controlled scrubbing, mopping and wiping functions with controlled supply of pressurized cleaning agent and lubricant oil.

Abstract: Disclosed is an automated gun barrel cleaning system (100) and a method thereof for straight hollow cylindrical objects, preferably gun barrels. The system (100) enables scrubbing, mopping, lubrication and wiping of the gun barrel without the need to remove the cleaning device out of the gun barrel during cleaning and to replace the brush or mopping/wiping cloth. The method of cleaning provides a time-stamped cleaning data to monitor the condition of the gun barrel and to estimate quality and effective service life of the gun barrel. The system (100) comprises of a cleaning device 102 connected to a main controller unit 106 wherein the cleaning device 102 includes drive wheel assembly 210, a driven wheel assembly 240, a spray nozzle assembly 260, a vision system 270 and a cleaning assembly 220 providing controlled scrubbing, mopping and wiping functions with controlled supply of pressurized cleaning agent and lubricant oil.

Abstract: A system and method for autocalibration of the camera and lighting components of an inspection and cosmetic grading machine is provided. Camera and lighting assemblies capture images of an object and create a 2D composite image which is processed by an image processing module with a deep learning machine algorithm to detect surface defects in the object and may measure the depth associated with light intensity. Precisely calibrated lights and vision system are required. Over time, environmental factors and component vibration and movement dislocate the components causing both lighting and camera anomalies. Automated systems are proposed that allow automated calibration of these systems without human intervention.

Abstract: Embodiments are directed to systems and methods for the automation of lot composition and pricing for returned assets sent to auction. Embodiments implement deep learning and optimization techniques to set a starting bid price and create lot composition that will provide an organization with the optimal value for its returned assets. A user portal allows organizations to manage auctions, lots, payment, disputes and other functions for buyers and sellers in the auction marketplace, in addition to viewing data analytics and performing “what if” analysis.

Abstract: Embodiments are directed to systems, methods and computer program products for dispositioning returned assets. Embodiments determine, for each asset, the optimal disposition, prepare the asset for its final disposition, label it, and direct it to the appropriate landing bucket. Some embodiments receive an asset, connect it to a hub where it may be activated and received into a warehouse. The asset may require diagnostics to determine operational/functional and cosmetic status and/or other services to prepare it for final disposition. Data may be collected from the asset, the receiving process and a database containing information related to the asset item number and the record for the specific asset. An asset profile is created to which business rules are applied which may determine a preliminary disposition for the asset. Profiles for assets passing preliminarily dispositioning are processed through an optimal value server.

Abstract: A system and method for inspection and cosmetic grading of objects is provided. Camera and lighting assemblies capture images of an object and create a 2D composite image which is processed by an image processing module with a deep learning machine algorithm to detect surface defects in the object. Detected defects are localized and measured for depth of defect by an advanced optical sensor, providing a 3D representation of defects. A cosmetic grading algorithm determines the cosmetic grade of the object and the optimal path of disposition for the item based on the grade.

Abstract: Embodiments are directed to systems, methods and computer program products for dispositioning returned assets. Embodiments determine, for each asset, the optimal disposition, prepare the asset for its final disposition, label it, and direct it to the appropriate landing bucket. Some embodiments receive an asset, connect it to a hub where it may be activated and received into a warehouse. The asset may require diagnostics to determine operational/functional and cosmetic status and/or other services to prepare it for final disposition. Data may be collected from the asset, the receiving process and a database containing information related to the asset item number and the record for the specific asset. An asset profile is created to which business rules are applied which may determine a preliminary disposition for the asset. Profiles for assets passing preliminarily dispositioning are processed through an optimal value server.