Abstract: A computer-implemented system for facilitating the sale of products through an online marketplace providing real-time quality data to users, comprising (a) receiving quality data associated with a sample, wherein the quality data was generated, at least in part, by a device that inspected a sample, (b) causing at least a portion of the quality data to be communicated via a graphical user interface, (c) receiving data indicating a desire (bid) to purchase the sample, (d) determining if a sale has been executed, (e) generating a sale status data based at least in part on the determining of (d), and (f) communicating the sales status data, wherein (a) through (f) are performed using one or more processor circuits. The system can use machine learning to provide real-time price predictions to aid buyers. The system can utilize blockchain technology to ensure valid and consistent data is being communicated to all users.

Abstract: A system and method for automatically detecting the type of product being processed in an industrial machine and switching to the corresponding product model without manual operator input. The system includes a detection module configured to determine the product type based on real-time data such as visual classification, lot number association, or processing schedule. Upon identifying the product type, a model selection engine automatically activates the appropriate product model from among multiple models stored on the machine. This automation eliminates the risk of human error associated with manual switching, improves operational reliability, and ensures accurate processing for environments where multiple product types are handled on the same equipment.

Abstract: A process includes receiving a target quality value, receiving a measured quality value, receiving a source quality value, and sending a source control instruction. The source control instruction is based at least in part on the target quality value, the measured quality value, and the source quality value. The target quality value, the measured quality value, the source quality value, and the source control instruction are communicated via the communication port. The measured quality value is generated by an inspection device configured to inspect a sample. The source quality value is associated with a quality level of a first group of samples. The target quality value indicates a desired quality value of an output group of samples. The source control instruction causes a source selecting device to select one of a plurality of groups of samples, each group having identified quality characteristics.

Abstract: An apparatus is configured to facilitate communication with a network of inspection and sorting devices. It transmits operational instructions to the inspection units and sorting units, ensuring synchronized operations. The apparatus also receives and communicates inspection and sorting data, enabling real-time analysis and decision-making. Additionally, it exchanges status data with these devices, providing insights into their operational performance and health. The apparatus stores the received data for further processing or reporting. Based on the collected data, the apparatus can generate and send notification messages, such as alerts for performance issues or maintenance needs. Furthermore, it can send updated instructions to inspection and sorting units to adjust their configurations or operations as necessary, improving efficiency and responsiveness. This system enhances control, monitoring, and management of inspection and sorting tasks in complex environments.

Abstract: An adaptable inspection and sorting unit includes an attachment mechanism, an inspection sensor device that is capable of inspecting a sample, a sorting device that is capable of deflecting the sample, a data port that is capable of receiving information, and a power port that is connectable to a power source. The attachment mechanism, the inspection device, the sorting device, the data port, and the power port are physically connected together. A memory circuit and a processor circuit read information received via the data port, control the inspection sensor device, and control the sorting device. The adaptable inspection and sorting unit is capable of inspecting and deflecting a sample traveling along the processing line. The speed of the sample is measured utilized to determine a delay between inspecting the sample and sorting the sample.

Abstract: A method includes collecting measurement data of a sample, determining a confidence value associated with the measurement data, determining if the confidence value is less than a confidence threshold value, and causing the measurement data to be stored in a memory device if the confidence value is less than the confidence threshold value. The method further includes utilizing the measurement data for training of a machine learning system, thereby updating the operation of the machine learning system. The measurement data is measured by an inspection device located at a first location. In one example, the measurement data is a captured image captured by an optical inspector. The confidence value associated with the captured image represents the confidence of the machine learning system regarding what is displayed in the captured image.

Abstract: A method includes the steps collecting measurement data of a sample utilizing an adaptable inspection unit or while the sample is in-flight, determining a volume or area of the sample based at least in part on the measurement data, and calculating a weight of the sample based at least in part on the volume or area of the sample. The measurement data includes a captured image that includes a plurality of pixels. The determining of the volume of the sample includes determining the number of pixels in the captured image that display a portion of the sample, or determining the maximum number of consecutive pixels that display a portion of the sample in two or three dimensions.

Abstract: A method includes the steps collecting measurement data of a sample utilizing an adaptable inspection unit or while the sample is in-flight, determining a volume or area of the sample based at least in part on the measurement data, and calculating a weight of the sample based at least in part on the volume or area of the sample. The measurement data includes a captured image that includes a plurality of pixels. The determining of the volume of the sample includes determining the number of pixels in the captured image that display a portion of the sample, or determining the maximum number of consecutive pixels that display a portion of the sample in two or three dimensions.

Abstract: A method includes the steps collecting measurement data of a sample utilizing an adaptable inspection unit or while the sample is in-flight, determining a volume or area of the sample based at least in part on the measurement data, and calculating a weight of the sample based at least in part on the volume or area of the sample. The measurement data includes a captured image that includes a plurality of pixels. The determining of the volume of the sample includes determining the number of pixels in the captured image that display a portion of the sample, or determining the maximum number of consecutive pixels that display a portion of the sample in two or three dimensions.