EP3963262A1 - Procédé pour le fonctionnement d'un appareil de cuisson et appareil de cuisson - Google Patents

Procédé pour le fonctionnement d'un appareil de cuisson et appareil de cuisson

Info

Publication number
EP3963262A1
EP3963262A1 EP20734521.6A EP20734521A EP3963262A1 EP 3963262 A1 EP3963262 A1 EP 3963262A1 EP 20734521 A EP20734521 A EP 20734521A EP 3963262 A1 EP3963262 A1 EP 3963262A1
Authority
EP
European Patent Office
Prior art keywords
cooking
data
frequency
food
model
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP20734521.6A
Other languages
German (de)
English (en)
Inventor
Helge Nelson
Timo Schmull
Volker Backherms
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Miele und Cie KG
Original Assignee
Miele und Cie KG
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Miele und Cie KG filed Critical Miele und Cie KG
Publication of EP3963262A1 publication Critical patent/EP3963262A1/fr
Pending legal-status Critical Current

Links

Classifications

    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24C—DOMESTIC STOVES OR RANGES ; DETAILS OF DOMESTIC STOVES OR RANGES, OF GENERAL APPLICATION
    • F24C7/00—Stoves or ranges heated by electric energy
    • F24C7/08—Arrangement or mounting of control or safety devices
    • F24C7/082—Arrangement or mounting of control or safety devices on ranges, e.g. control panels, illumination
    • F24C7/085—Arrangement or mounting of control or safety devices on ranges, e.g. control panels, illumination on baking ovens
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/02—Food
    • A—HUMAN NECESSITIES
    • A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
    • A47J—KITCHEN EQUIPMENT; COFFEE MILLS; SPICE MILLS; APPARATUS FOR MAKING BEVERAGES
    • A47J36/00—Parts, details or accessories of cooking-vessels
    • A47J36/32—Time-controlled igniting mechanisms or alarm devices
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N22/00—Investigating or analysing materials by the use of microwaves or radio waves, i.e. electromagnetic waves with a wavelength of one millimetre or more
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
    • G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/68—Food, e.g. fruit or vegetables
    • H—ELECTRICITY
    • H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
    • H05B—ELECTRIC HEATING; ELECTRIC LIGHT SOURCES NOT OTHERWISE PROVIDED FOR; CIRCUIT ARRANGEMENTS FOR ELECTRIC LIGHT SOURCES, IN GENERAL
    • H05B6/00—Heating by electric, magnetic or electromagnetic fields
    • H05B6/64—Heating using microwaves
    • H05B6/6447—Method of operation or details of the microwave heating apparatus related to the use of detectors or sensors
    • H05B6/6467—Method of operation or details of the microwave heating apparatus related to the use of detectors or sensors using detectors with R.F. transmitters
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/26—Pc applications
    • G05B2219/2643—Oven, cooking

Definitions

  • the present invention relates to a method for operating a cooking appliance with at least one cooking space for preparing food and a cooking appliance.
  • High-frequency measuring system high-frequency data are recorded during the cooking process.
  • At least one cooking parameter characterizing the food to be cooked is derived from the high-frequency data by means of at least one processing device.
  • Skewer thermometers are often used for meat products to measure the core temperature. When a certain core temperature is reached, the
  • the method according to the invention is used to operate a cooking appliance with at least one cooking space for the preparation of food.
  • high-frequency data are recorded during the cooking process, ideally
  • At least one of the items to be cooked is converted from the high-frequency data by means of at least one processing device
  • the high-frequency data are processed by means of at least one machine learning model stored in the processing device and in particular self-learning.
  • the processing device independently derives the cooking parameters from the high-frequency data using the model.
  • the method according to the invention offers many advantages.
  • the use of the machine learning model to calculate the cooking parameter from the high-frequency data offers a significant advantage. This allows for a wide variety of cooking processes and previously unknown from the model and not stored in the processing device
  • High-frequency data of the cooking parameters can be reliably predicted or determined.
  • the invention can by its model z. B. also reliably determine the time of completion when recipes are implemented for which the device is not specifically set at the factory due to the variety of possible combinations of foods or which are not stored as such in the device.
  • the cooking process includes inserting the
  • the high-frequency measuring system preferably records at least one reflection spectrum and in particular at least two and preferably several reflection spectra of the cooking space and / or of the food to be cooked. In doing so, the high-frequency data are transmitted using the
  • Processing device calculated from the reflection spectra. Then there is one in particular further processing or offsetting of the reflection spectra is planned. It is also possible that the high-frequency data are reflection spectra. The reflection spectra are then preferably used directly to calculate the cooking parameter.
  • Reflection spectra are, in particular, scattering parameters or S-parameters or include such or are variables derived from the scattering parameters. That offers a special one
  • reflection spectra is understood to mean, in particular, both spectra in the sense of frequency ranges and, in particular, pulses in the sense of time ranges.
  • the high-frequency measuring system is suitable and designed to provide reflection spectra
  • the high-frequency data describe changes in the reflection spectra over time.
  • the reflection spectra are preferably recorded repeatedly during the cooking process.
  • changes over time in the reflection spectra are determined during the cooking process and used to determine the cooking parameter.
  • the high-frequency data preferably reflect changes in the food and / or in the cooking space during the cooking process. It is possible that the high-frequency data are processed further before they are fed to the model. The high-frequency data can, however, also be fed directly or unprocessed to the model.
  • the model is or has been created and / or is being trained by monitored machine learning.
  • at least one amount of data from a representative amount of cooking processes is preferably recorded as a reference.
  • the amount of data preferably includes at least the high-frequency data.
  • Contain cooking parameters as target size also referred to as label.
  • Correlations between the high-frequency data and the cooking parameter are then preferably learned, so that the model is then able, starting from the high-frequency data, to infer the cooking parameter or to calculate the cooking parameter independently. In this way, a particularly reproducible determination of the cooking parameter can be carried out even with very different recipes or cooking processes.
  • model is taken from a group of model types, including at least analytical models and, for example, equations, statistical Models and, for example, regressors, artificial neural networks, convolutional neural networks (CNN or ConvNet), model types of machine learning.
  • CNN convolutional neural networks
  • ConvNet convolutional neural networks
  • model types of machine learning In German, CNN means something like a folding neural network.
  • the model is preferably capable of learning or
  • the model can be suitable and designed that
  • the model comprises at least one piece of software or is designed as such.
  • the model provides an artificial intelligence and / or an assistant.
  • the cooking parameter is preferably taken from a group of parameters, including at least core temperature, browning status, moisture status, freezing status,
  • Crust formation changes in the aforementioned parameters over time.
  • the cooking parameter indicates whether the food is frozen or thawed.
  • the core temperature is given in degrees Celsius or Kelvin, for example.
  • the cooking parameters can be displayed to the user and / or to a control device for controlling a
  • Treatment facility are provided. It is possible and preferred that further cooking parameters characterizing the food or the cooking process are derived from the model.
  • device data are also processed with the model.
  • the model independently derives the cooking parameter from a combination of the high-frequency data with at least the device data.
  • the device data are retrieved and / or provided by a control device of the cooking device in particular.
  • the device data describe in particular at least one variable that is characteristic of the operation of the cooking device.
  • the device data describe, for example, a
  • Control variable and / or control variable for a treatment facility It is possible for the device data to include at least one variable that can be detected by sensors. Such a combination offers a particularly reliable and reproducible determination of the cooking parameter.
  • the device data are in particular taken from a group of data, including at least at least thermal and / or energetic status data of the cooking chamber, operating mode, switched consumers, runtime of the cooking process. It is possible and preferred that the device data include at least one further parameter characterizing the device or its operating state.
  • loading data are also processed with the model.
  • the cooking parameter is preferably derived independently by the model from a combination of the high-frequency data with at least the load data.
  • the model independently derives the cooking parameter from a combination of the high-frequency data with at least the load data and at least the device data.
  • the loading data include in particular at least one characteristic variable for loading the cooking space. Such a combination offers a particularly reliable and reproducible determination of the cooking parameter.
  • the loading data are preferably recorded by the high-frequency measuring system.
  • the loading data are preferably determined from the high-frequency data and / or determined by high-frequency measurements carried out specifically for this purpose by means of the high-frequency measuring system. It is also possible and preferred for the loading data to be recorded by means of at least one sensor device.
  • the sensor device comprises in particular at least one camera device.
  • the loading data are then preferably determined from the image data captured with the camera device.
  • the camera device captures images from the cooking space for this purpose.
  • other sensors can also be provided.
  • the loading data are preferably taken from a group of data, including at least geometric properties of the food and / or of a food container / carrier, position of the food and / or of a food container / carrier, type of food and / or of a food container / carrier.
  • the geometry properties include, for example, a height and / or size and / or width and / or length and / or contour and / or shape and / or a volume of the food and / or the food container / carrier.
  • the load data describe the position of the item to be cooked and / or the item to be cooked / carrier in the cooking space.
  • the load data describe the insertion height of a food rack in the cooking space.
  • the type of food to be cooked is in particular a type of food and, for example, meat, fish, vegetables, pasta.
  • the type of food container is in particular a
  • the loading data include at least one further parameter characterizing the loading.
  • Treatment device for the preparation of food is controlled by means of at least one control device as a function of the cooking parameter.
  • At least one parameter of at least one treatment program also referred to as an automatic function and / or automatic program, is particularly preferably adapted as a function of the cooking parameter.
  • Such Design offers a particularly convenient and at the same time reliable preparation of the most varied of recipes and foods.
  • the cooking parameter describes the finishing time and the control device ends the cooking process when the
  • the treatment device is controlled by at least one control device as a function of at least one treatment program.
  • the control device adapts the treatment program as a function of the cooking parameter.
  • the calculated cooking parameter is communicated to the user,
  • a display device for example via a display device or a mobile terminal or the like.
  • high-frequency measurement radiation with a plurality of distinguishable frequencies and / or phases is introduced into the cooking chamber by means of the high-frequency measurement system
  • the measuring radiation is received again and evaluated by means of the high-frequency measuring system.
  • the reflection spectra are created on the basis of at least one comparison of the received and emitted measurement radiation as a function of frequency and / or phase.
  • the measurement radiation can also be transmitted and / or received and / or evaluated as a pulse and in particular as an ultra-short pulse.
  • the cooking appliance according to the invention is suitable and designed to be operated according to the method described above.
  • the cooking appliance comprises in particular at least one treatment device and at least one cooking space.
  • the cooking appliance comprises at least one high-frequency measuring system for acquiring high-frequency data during the cooking process.
  • a processing device for processing the
  • Radio frequency data is either directly in the device or at an external location, e.g. a cloud or a server.
  • the high-frequency measuring system comprises in particular at least one high-frequency generator and / or at least one antenna (in particular flat or planar) assigned to the cooking chamber and / or at least one transmitting unit and / or at least one
  • Receiving unit and / or at least one evaluation unit receives and/or at least one evaluation unit.
  • Several transmitters and / or receivers can also be provided.
  • the high-frequency measuring system is then constructed, in particular, in the manner of an antenna array or comprises at least one such.
  • the high-frequency measuring system is particularly broadband and can be operated in the range from 100 MHz to 5 GHz, where a good excitation of common food and water is possible.
  • the frequency range is selected in particular so that a
  • the high-frequency measuring system can transmit and / or receive and / or measure in particular over a broadband.
  • the high-frequency measuring system comprises in particular at least one transmitter / receiver unit evaluation transceiver.
  • the high-frequency measuring system, in particular the transceiver, is preferably suitable and designed for at least one of the
  • Frequency sweep pulse radar, FMCW (Frequency Modulated Continuous Wave), other suitable methods for recording
  • the treatment device comprises in particular at least one high-frequency device with at least one high-frequency generator for introducing high-frequency radiation into the cooking chamber.
  • This high frequency radiation is suitable to act as measuring radiation.
  • the high-frequency radiation can also be used to prepare food.
  • the high-frequency measuring system is particularly suitable and designed for determining the degree of absorption that is sent into the cooking space for the preparation of food to be cooked
  • a reduction in the transmission power can be provided.
  • the transmission power provided for preparing the food can be maintained for measuring.
  • the high-frequency measuring system is suitable and designed to use a broadband measurement radiation to determine the degree of absorption, which has a multiple (for example ten or hundred times) lower power than the narrow band sent into the cooking space for the preparation of food to be cooked Has high frequency radiation.
  • the measuring radiation is in particular not suitable or designed for preparing food to be cooked. It is possible that the
  • Measurement radiation is generated by the high-frequency measurement system and that
  • the high-frequency measuring system has its own high-frequency generator for this purpose.
  • Measurement radiation can also be generated by the high-frequency device of the treatment device.
  • FIG. 1 is a purely schematic representation of a cooking device according to the invention in a front view; and FIG. 2 shows a purely schematic and sketchy representation of a relationship between a variable that is characteristic of the food or the state of cooking in relation to the cooking time.
  • FIG. 1 shows a cooking device 1 according to the invention, which is designed here as an oven 100 or combination device.
  • the cooking appliance 1 is operated according to the method according to the invention.
  • the cooking appliance 1 has a heatable cooking space 2, which can be closed by a cooking space door 12.
  • the cooking device 1 is provided here as a built-in device. It can also be designed as a stand-alone device.
  • a treatment device 6 For the preparation of food to be cooked, a treatment device 6 is provided which, in the view shown here, is not visible in the cooking space 2 or inside the device.
  • the treatment device 6 comprises, for. B. a heating device with several heating sources for heating the cooking space 2.
  • a heating source for example, a top heat and / or a bottom heat, a hot air heat source and / or a grill heat source or other types of heat sources can be provided.
  • a steam generator can also be provided.
  • the treatment device 6 can be designed for heating or cooking with high-frequency radiation and for this purpose z. B. comprise a high frequency generator.
  • the cooking appliance 1 here comprises one that is operatively connected to the treatment device 6
  • Control device 16 for controlling or regulating device functions
  • control device 16 preselectable operating modes and preferably also various treatment programs (cooking programs) or
  • control device 16 controls z. B. the treatment device 6 depending on a preselected
  • An operating device 101 is provided for operating the cooking appliance 1. For example, the operating mode, the cooking space temperature and / or an automatic program or a program operating mode or other automatic functions can be selected and set. Further user inputs can also be made via the operating device 101 and, for example, menu control can be performed.
  • the operating device 101 also includes a display device 102 via which user instructions and z. B.
  • Prompts can be displayed.
  • the operating device 101 can display Prompts.
  • Control elements and / or a touch-sensitive display device 102 or a touchscreen are Control elements and / or a touch-sensitive display device 102 or a touchscreen.
  • the treatment device 6 is provided with a high-frequency generator fitted.
  • the high-frequency generator is preferably based on semiconductor technology and is, for example, a solid-state high-frequency generator. But it is also possible that the
  • High-frequency generator is designed as a magnetron or at least includes one.
  • the cooking appliance 1 has a high-frequency measuring system 3, shown here in a highly schematic manner, with a processing device 4. This is recorded during the cooking process
  • High-frequency measuring system 3 reflection spectra and, for example, S-parameters.
  • high-frequency measurement radiation is generated and sent to the cooking chamber 2 and received again.
  • the reflection spectra are obtained by a corresponding comparison of the emitted with the received or reflected measurement radiation.
  • the measurement takes place here, for example, by a frequency sweep and / or FMCW.
  • the HF measurement process can itself also be used to heat the food and be generated, for example, by the high-frequency generator.
  • the reflection spectra are then offset or made available directly as high-frequency data to the processing device 4.
  • a model is stored in the processing device 4 which derives a cooking parameter from the high-frequency data, for example the core temperature and / or the time at which the food to be cooked is finished.
  • the model can be created analytically, statistically or using machine learning processes.
  • the model in combination with the high-frequency data also uses device data and / or load data to calculate the cooking parameter.
  • the loading data can be obtained, for example, by a sensor device 5 that is not visible here in the interior of the cooking appliance 1. This is the
  • Sensor device 5 equipped, for example, with a camera device 15, which records images of the cooking space and its load.
  • the processing device 4 evaluates the images accordingly to obtain the loading data.
  • the camera device 15 can, for. B. capture two-dimensional or three-dimensional or spatial image information or temperature-specific image information or thermal images from the cooking chamber 2.
  • the loading data can, however, also be determined from the reflection spectra.
  • Device data are provided here by the control device 16.
  • the hardware here includes, for example, an antenna in the cooking chamber 2 and a transmitter / receiver unit evaluation transceiver.
  • the antenna is able to enable broadband transmission or reception of high-frequency radiation in the cooking chamber and has z.
  • the transceiver can transmit, receive and measure broadband here.
  • three basic measurement methods are preferred: a frequency sweep, a pulse radar and an FMCW (Freq.-Modulated-Continuous wave).
  • Characteristic spectra (frequency range) or pulses (time range) are preferably generated and recorded for all measurements
  • the high-frequency measuring system 3 can also consist of several transmitters and receivers, similar to an antenna array
  • FIG. 2 shows the relationship between a variable 200 that is characteristic of the food or the cooking state compared to the cooking time 201 and the time of preparation 202 of a food product during preparation in the cooking appliance 1 presented here.
  • Characteristic variables can be the temperature of the food, the volume of the food, the browning of the food or changes in this over time. Also further and derived the cooking state
  • Characteristic quantities are conceivable.
  • the area framed in a rectangle outlines the period of time in which the ready time 202 lies and the food has a particularly favorable degree of cooking or is optimally fully cooked.
  • this period of time can be recorded particularly reliably and at the same time without contact and also robustly with respect to contamination.
  • Treatment program possible so that, for example, the cooking process can be ended in good time to avoid overcooking or drying out.
  • a self-learning system is used in the invention described here, which is based on the methods of monitored machine learning.
  • the monitored machine learning is trained here with representative data.
  • Classical parameters (oven temperature, cooking time, power consumed / energy) are preferably combined with high-frequency data (reflection spectra or S-parameters).
  • Models are created based on the (calculated) high-frequency data and other specific device data predict the core temperature of the food and / or other cooking parameters.
  • This data initially contains an assignment of data to the target variable (so-called label) for learning. Connections (correlation) between the data and the target variable are now learned. The model is then able to independently deduce the target size from the data.
  • the model can be selected differently depending on the type of label. You can choose from, for example, analytical models (equations), statistical models such as B. various regressors, up to neural networks, convolutional neural networks and other artificial learning processes. Different models are created and used for different questions.
  • a label can be, for example: thawed vs. frozen or core temperature in ° C or ready vs. not ready or type of food and many more.
  • Device data describe the thermal and energetic state of the furnace. Therefore, if possible, they include the operating mode, the switched consumers and the duration of the tests and / or other variables.
  • the high-frequency data are preprocessed here in an optimal way and important features are determined by statistical and / or communications technology approaches.
  • the loading should preferably be characterized by these features.
  • the changes can be deducted from the difference calculation and the evaluation.
  • the measurable changes in the S-parameters result from changes in the food during the cooking process, e.g. B. by releasing water, rising or shrinking, crust formation, etc., while the cooking space and its influence on the S-parameters remains constant over time.
  • the temperature difference between the food and the oven becomes smaller and smaller during the cooking process and the food becomes edible when the necessary physical (e.g. rising, evaporation of water) and chemical processes (e.g. change in browning ⁇ L, unfolding of proteins) of cooking have been completed are.
  • a special feature of the invention is also the interaction of the HF data and the device data or furnace data and / or loading data.
  • the model needs on the one hand the condition of the oven and on the other hand a description of the load via another sensor system. Only this combination creates a particularly complete overall picture. This additional
  • the sensor system here is the high-frequency measurement, which indirectly describes the height, size, volume, type and container of the load.
  • the sensor system can e.g. B. also be the sensor device 5 with the camera 15, from whose image data these variables are derived.

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Abstract

L'invention concerne un procédé pour le fonctionnement d'un appareil de cuisson (1) présentant au moins un espace de cuisson (2) pour la préparation d'un produit à cuire, un système de mesure à haute fréquence (3) détectant des données de haute fréquence pendant le processus de cuisson et un paramètre de cuisson caractérisant le produit à cuire étant déduit à partir des données de haute fréquence à l'aide d'un dispositif de traitement (4). Les données de haute fréquence sont traitées à l'aide d'un modèle d'apprentissage machine à auto-apprentissage et enregistré dans le dispositif de traitement (4). Le dispositif de traitement (4) déduit de manière autonome le paramètre de cuisson à partir des données de haute fréquence à l'aide du modèle.
EP20734521.6A 2019-07-15 2020-06-23 Procédé pour le fonctionnement d'un appareil de cuisson et appareil de cuisson Pending EP3963262A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102019119075.4A DE102019119075A1 (de) 2019-07-15 2019-07-15 Verfahren zum Betreiben eines Gargeräts und Gargerät
PCT/EP2020/067485 WO2021008825A1 (fr) 2019-07-15 2020-06-23 Procédé pour le fonctionnement d'un appareil de cuisson et appareil de cuisson

Publications (1)

Publication Number Publication Date
EP3963262A1 true EP3963262A1 (fr) 2022-03-09

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EP20734521.6A Pending EP3963262A1 (fr) 2019-07-15 2020-06-23 Procédé pour le fonctionnement d'un appareil de cuisson et appareil de cuisson

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US (1) US20220283135A1 (fr)
EP (1) EP3963262A1 (fr)
DE (1) DE102019119075A1 (fr)
WO (1) WO2021008825A1 (fr)

Families Citing this family (5)

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Publication number Priority date Publication date Assignee Title
DE102021120310A1 (de) 2021-08-04 2023-02-09 Topinox Sarl Verfahren zur Lasterkennung in einem Garraum eines Gargeräts sowie Gargerät
DE102021133444A1 (de) 2021-12-16 2023-06-22 Rational Aktiengesellschaft Verfahren zum Ermitteln eines Zustands eines Garguts in einem Garraum eines Gargrills, Verfahren zum Trainieren einer künstlichen Intelligenz sowie Gargerät
WO2023187058A1 (fr) * 2022-03-31 2023-10-05 BSH Hausgeräte GmbH Système de cuisson, dispositif de cuisson et procédé de fonctionnement d'un système de cuisson
IT202200010652A1 (it) * 2022-05-23 2023-11-23 Candy Spa Forno di cottura
WO2023057658A2 (fr) * 2023-02-06 2023-04-13 V-Zug Ag Dispositif de cuisson doté d'un dispositif de prise de vues

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Publication number Priority date Publication date Assignee Title
DE102014111019A1 (de) * 2014-08-04 2016-01-21 Miele & Cie. Kg Verfahren und Hausgerät
US20190059133A1 (en) * 2017-08-16 2019-02-21 The Markov Corporation Sensors for Training Data Acquisition in an Intelligent Electronic Oven
DE102018105232A1 (de) * 2018-03-07 2019-09-12 Rational Aktiengesellschaft Verfahren zum Erkennen wenigstens eines Beladungsparameters eines Garraums von einem Gargerät sowie Gargerät

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US20220283135A1 (en) 2022-09-08
WO2021008825A1 (fr) 2021-01-21
DE102019119075A1 (de) 2021-01-14

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