Coffee Pod Authenticity Testing

Single-serve coffee capsule design that allows barcode scanning without damaging the barcode during production. The capsule has a lid element that seals the coffee inside and a flange. The barcode is placed on the flange facing the bottom of the capsule or on an annular element around the flange. This avoids damaging the barcode when sealing the lid to the flange during production. The lid can still be secured to the flange. The capsule can be rotated during brewing to extract the coffee and the beverage exits through pierced holes in the lid.

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Single-serve coffee capsule for use in coffee machines with an optically detectable identification code on the capsule flange that is smaller than the full width. The code, like barcodes or sequences of numbers/letters, helps the machine read and identify the capsule without covering the entire flange. This allows partial coverage of the flange for better sealing and strength compared to full-width codes.

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Coffee machine that allows customization of coffee flavor by using different coffee beans for each cup without mixing them. The machine has a removable bean container with adjustable size to hold just enough beans for a single cup. A closing device seals the container after a preset time. This ensures only the desired amount of beans is ground. The container can then be swapped for a different bean type. The machine also detects the container ID to set the preset time based on bean characteristics.

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Capsule coffee machine that identifies coffee capsules without additional modules like NFC or color sensors. The machine detects the irregular contour of the capsule surface using sensors, then determines the capsule type based on the shape. This allows identifying capsules without extra modules to reduce machine cost. The machine also customizes brewing parameters for each capsule type based on user settings.

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Automatically recognizing the type of capsule inserted into a beverage machine to adapt the brewing parameters. The machine has a capsule recognition module that senses characteristics like color to determine a sample value. It compares the sample to reference values for known capsule types. If no match exceeds a threshold, it selects multiple references to accommodate indistinguishable capsules. This allows the machine to accurately identify capsules without needing unique characteristics.

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Beverage machine that can automatically identify the type of beverage capsule inserted to optimize brewing parameters. The machine has a capsule recognition module that uses an optical sensor and light source to measure properties of the capsule surface. It compares the measured values to reference values for known capsule types to determine the capsule type.

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Beverage preparation machine using capsules that can automatically recognize the capsule type inserted in order to adapt the beverage preparation. The machines have a module (e.g. a color sensor) that senses a property of the capsule like its color, material or resistivity. It compares the sensed value to reference values for known capsule types to determine the capsule type. If no single match is found, it selects at least two reference values to increase certainty.

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Coffee machine that can identify the type of capsule or coffee in the brewing chamber and adjust the brewing parameters accordingly. The machine has a support with multiple identification spots. Signal generators correspond to each spot and detect the corresponding bit. The detected bits form a combination that is used to identify the capsule type. Based on the identified type, the machine brews using optimized parameters for that capsule.

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A capsule for preparing a beverage that can be identified by a machine to adjust parameters for extraction. The capsule contains a code on its surface that can be read when the capsule is rotated in a beverage extraction device. The code provides information to the machine about the beverage inside. By rotating the capsule during extraction, the machine can identify the code and adjust parameters like flow rate, pressure, and temperature for optimal extraction. This enables customization of the extraction process for different capsule contents.

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A system and method for recognizing coffee capsules in a coffee machine without requiring special features on the capsules that increase cost. The method involves using a vibration sensor in the coffee machine to detect vibrations of components like the pump or capsule holder during brewing. The vibrations are caused by the changing mass of the capsule as it absorbs water. Analysis of the vibration data allows recognition of capsule types without physical features.

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Beverage machine that automatically adjusts brewing parameters like temperature and volume based on capsule characteristics. If the machine can't read the capsule features, it defaults to parameters that prevent issues like hot water in cold beverage capsules. The machine has a reader to identify capsule characteristics. If it can't read a capsule, it adjusts brewing parameters to prevent issues like hot water in cold beverage capsules. The user can't override these automated adjustments when the machine can't read the capsule.

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Coffee capsule machine that can recognize the identification code on a coffee capsule, then automatically extract the coffee according to the code. The machine has a camera positioned to view the capsule's code when it is in the extraction chamber. The camera captures an image of the code, which is decoded by the control circuit. The code identifies the coffee type and the circuit sets the extraction parameters accordingly, like water volume and temperature. The circuit controls the heating and extraction units to deliver the customized extraction for the identified coffee capsule.

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A drawer-type capsule coffee machine that can automatically extract and brew coffee from capsules without any manual operation. The machine has an identification assembly to read patterns on the capsule. When the extraction assembly is exposed, the identification assembly reads the pattern and sends a signal to drive the extraction assembly to slide in. A cutter assembly above aligns with the capsule to puncture it. The extraction assembly then slides back out to brew the coffee.

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Reusable coffee capsule assembly with an identification code that allows users to brew their own custom coffee in machines that read barcodes on disposable capsules. The assembly has a reusable capsule inside a jacket with a matching cavity. The capsule has a unique code on its outer flange that can be read by the machine. This allows users to insert their custom-filled capsule into the jacket and brew it in machines that normally only work with branded capsules. The jacket provides a secure enclosure for the capsule and prevents coffee grounds from escaping.

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Beverage machine with a camera to image the pod lid before brewing. The machine has a movable brew chamber that holds the pod. A pod stabilizer contacts the pod lid during chamber closure to position it for imaging by a camera mounted on the chamber cover. This ensures proper lid imaging for pod identification and authentication before brewing starts.

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Optical product checking system for simple and reliable verification of product properties and authenticity. The system uses multiple light-emitting units to illuminate the product from different wavelengths. The reflected light is detected to infer the product color and authenticity. This allows separate determination of properties like color and authenticity. The product packaging contains luminescent pigments that glow when excited by the system. This provides a reliable indication of authenticity by detecting the pigments.

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Beverage brewing system with customizable brew parameters and cartridge recognition to allow using a variety of brewing materials like coffee, tea, soup, etc. The system has features like temperature control, pressure regulation, pump speed adjustment, and impeller aeration to extract flavors consistently from different brewing materials. It also identifies cartridges based on topology and usage patterns to prevent counterfeiting and spoofing. The system allows users to brew custom beverages like espresso, latte, etc. from their own materials in a single-serve brewer.

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Automatic capsule recognition for beverage machines that can identify the type of capsule inserted to optimize beverage preparation. The machine has a camera to capture an image of the capsule in the recognition position. A neural network processes the image to determine the capsule type. This allows the machine to adapt parameters like extraction time and water volume based on the recognized capsule type.

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Capsules for making beverages like coffee using machines like Nespresso that have improved features for compatibility, identification and extraction. The capsules have projections and recesses arranged in a rectilinear pattern along a plane parallel to the flange. The bottom of the capsule has a cross-section that transitions from circular to at least approximately quadrangular shape. This allows improved compatibility and identification with machines using toothed gears, while still allowing extraction of the beverage.

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A liquid delivery system that can make beverages like espresso and cappuccino using removable cartridges containing coffee or other brewing materials. The system has features to customize brewing parameters like water temperature, pressure, and delivery rate to extract optimal flavors from different types of cartridges. It can also identify cartridges using visual signatures to prevent counterfeiting. The system allows brewing variables to be determined and controlled by the machine rather than just fixed timers. It uses sensors to measure conditions like water temperature, pressure, and cartridge type to optimize brewing. The system can also deliver a second liquid like steamed milk separately from the brewed beverage for making drinks like cappuccino. The second liquid can be pressure-assisted by vapor and delivered through the same receptacle as the brewed beverage. The system allows customization and consistency of brewing using

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